Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient zero-knowledge proofs and their applications

A zero-knowledge proof is a fundamental cryptographic primitive that enables the verification of statements without revealing unnecessary information. Zero-knowledge proofs are a key component of many cryptographic protocols and, often, one of their main efficiency bottlenecks. In recent years there have been great advances in improving the efficiency of zero-knowledge proofs, bring them closer to wide deployability. In this thesis we make another step towards the construction of computationallyefficient zero-knowledge proofs. Specifically, we construct efficient zero-knowledge proofs for the satisfiability of arithmetic circuits for which the computational cost of the prover is only a constant factor more expensive than direct evaluation of the circuit. We also construct efficient zero-knowledge proofs to check the correct execution of (Tiny)RAM programs. In this case the computational cost for the prover is a superconstant factor larger than executing the program directly. Our proofs also support efficient verification and small proof sizes. For security, they rely on symmetric primitives and could potentially withstand attacks from quantum computers. On a different research direction, we look at group signatures, a fundamental primitive which relies on zero-knowledge proofs. A group signature enables users to sign anonymously on behalf of a group of users. In case of dispute a Manager can identify the author of a signature and potentially banish the user from the group. In this thesis we address the fundamental question of defining the security of fully dynamic group signatures, for which the users can join and leave at any time. Differently from other restricted settings, this case has been largely overlooked in the past. Our security model is general, does not implicitly assume existing design paradigms and captures the security of existing models for more restricted settings.
Evaluation of Healthcare Information System Using Delone and McLean Quality Model, Case study KSA

Health information system (HIS) is crucial systems and widely spread and used. Recently, HIS is gaining popularity importance and a boundless potential for developing countries. HIS has adopted by scaled healthcare provider to deliver a qualified patient healthcare services, patient safety, minimum medical faults, efficient and effectiveness patient care and satisfaction by accessing truthful information at anytime and anywhere. In kingdom of Saudi Arabia (KSA), adopted using HIS in many hospitals. This study used Delone & McLean (D&M) quality model to evaluate the quality of these systems to safeguard the quality of patient care. Delone & McLean quality model is the most validated and popular model used to measure the factor of success of HIS system. This research focuses on six interconnected measurements of Information system factor of success criteria that have been provided by the model. These measurements are: information quality, system quality, services quality, user satisfaction, use and net benefits. These factors interrelated and has positively effect to each other. A questionnaire is distributed to all stakeholder of HIS in four hospitals in Riyadh. The questionnaire contains questions measure the six dimensions. The result is collected from the questionnaire and an analysis is conducted to validate the success of HIS and improve the HIS Quality in KSA Hospital.
A 76.6-dB-SNDR 50-MHz-BW 29.2-mW Multi-Bit CT Sturdy MASH With DAC Non-Linearity Tolerance

This article presents a dual-loop noise-coupling (NC)-assisted continuous-time (CT) sturdy multistage noise-shaping (SMASH) $\Delta \Sigma $ modulator (DSM), employing 1.5-bit/4-bit quantizers. The proposed SMASH can equivalently work as an overall fourth-order DSM with 4-bit internal quantization. The NC applied in this CT SMASH DSM whitens the 1.5-bit quantization noise (QN) and further reduces its in-band tone power, while a finite-impulse response (FIR) filter integrated into the outermost feedback path suppresses the out-of-band (OOB) noise power of the multibit digital-to-analog converter (DAC) input. Together, they avoid any linearization technique for the multibit DAC. Sampled at 1.2 GHz, the 28-nm CMOS experimental prototype measures a signal-to-noise-and-distortion ratio (SNDR) of 76.6 dB and a spurious-free dynamic range (SFDR) of 87.9 dB over a 50-MHz bandwidth (BW), consuming 29.2 mW from 1.2-V/1.5-V supplies and occupying an active area of 0.085 mm2. It exhibits a Schreier figure-of-merit (FoM) (SNDR) of 168.9 dB.
Nerve regeneration through autologous grafts: exploring the 'Bottleneck Theory' further

Background: Two-stage free functional muscle transfer (FFMT) is the gold-standard treatment modality for unilateral congenital facial paralysis. At the first stage a cross facial nerve graft (CFNG) is harvested and coapted to an isolated buccal branch of the functioning facial nerve. The second stage involves harvesting a suitable muscle with its neurovascular pedicle and transplanting this to the face after a delay of six to nine months to permit nerve regeneration; the nerve supplying the muscle is coapted to the CFNG. Aims: The purpose of this thesis was to investigate the impact that an autologous nerve grafts has on nerve regeneration. The hypothesis being tested was that nerve grafts with a higher axonal count would yield better outcomes than those with lower axonal counts. Methods: To test this two parallel experiments were designed. Firstly, a new animal outcome measure was developed selecting the posterior auricular nerve as the subject for intervention; this is an isolated branch of the facial nerve with a distinct measureable outcome through the posterior movement of the ears and recovery of the nerve could be measured accordingly. Secondly, automated axon counts were calculated with a modified technique of serial section electron microscopy. Results: The results demonstrated that a graft with a higher axon count significantly yielded better outcomes than a graft with a lower axon count: larger grafts produced better clinical outcomes of ear movement and a higher number of axons regenerated across the graft. Discussion: It was proposed that larger grafts contained higher nerve growth factors, neurotrophins and provided better structural properties than small grafts, however larger grafts are at risk of increased central scarring through ischaemia. In conclusion, this study supports the test hypothesis that a graft with a higher axonal count will out-perform a graft with a lower axon count.
A sensor tasking algorithm for EO/IR sensors carried by UAVs

We address the problem of controlling EO/IR sensors carried by UAVs, in a context where multiple UAVs are employed to perform a surveillance mission. Swarm-based ISTAR can deliver commanders a more complete situational picture, but controlling a swarm becomes increasingly complex with each additional vehicle. A practical system requires a degree of autonomy to avoid overwhelming operators with detail and/or to avoid requiring too many operators. We present a sensor tasking algorithm which chooses a sequence of tasks for an EO/IR sensor located on an air vehicle. The tasking problem is formulated as a Markov Decision Process (MDP), in which each task is associated with an expected reward stream that would be accrued if the task were put into effect. The size of the MDP state space increases exponentially with the number of candidate tasks, so generic MDP solutions are inapplicable. Instead we propose a solution using Gittins indices. Our problem includes time sensitive tasks, where the probability of receiving a reward decays with system time. The problem also includes tasks that are not time sensitive. The combination of time-sensitive and non-time-sensitive tasks is challenging for an index policy. Our contribution is to create a combination of index algorithms that approximately solves the MDP for a combination of time-sensitive and non-time-sensitive tasks. EO/IR stands for Electro-Optic / Infra-Red. UAV stands for Unmanned Aerial Vehicle. ISTAR stands for Intelligence, Surveillance, Target Acquisition and Reconnaissance.
Die Gruppendiskussion als Evaluationsmethode ? Entwicklungsgeschichte, Potenziale und Formen

This article aims to summarize the progress in the current discussion on theory and practice of group discussions in evaluation and thereby to professionalize the application of this technique in evaluation. After having clarified the definitions of related terms such as group discussion, focus group and group interview, an overview of the development and reception of group discussions in social science and evaluation is provided. It is shown what differentiates evaluation as a discipline when compared to social science in general. From these differentiating characteristics of evaluation result particular potentials for data collection and the design of the evaluation process applying group discussions. Finally, two ideal-typical forms of group discussions are contrasted with each other and vital elements for planning, implementation and analysis are discussed.
Analysis of selected methods for building an ontology for a system of automatic communication at sea

Automatic communication can help reduce errors in communication between navigators, and, consequently, increase the level of navigation safety. This article reviews some methods for the development of an ontology and looks into processes for communication at sea. Three basic elements of ontology can be distinguished: navigational information, communication and interface. The possibility of applying these methods for the construction of ontology was analyzed for a system of automatic communication at sea.
A New Approach for Short-term Time Series Forecasting

Short-term series forecasting is one of the essential issues in a variety of tasks, such as traffic flow prediction, stock market tendency analysis, etc. Most current methods based on stable or abundant historical data. In this paper, we proposed a novel model called LA-NN. It takes advantages of both long short-term memory (LSTM) network and autoregressive integrated moving average (ARIMA) by a relation integration of them. So as to deal with the situation of insufficient historical data and sudden abnormal changes in data. A comparison with other representative forecast models validates that the proposed LA-NN network can achieve a better performance.
High-Fidelity Hexarot Simulation-Based Motion Platform Using Fuzzy Incremental Controller and Model Predictive Control-Based Motion Cueing Algorithm

The real vehicle motion signals cannot be applied for the simulation-based motion platforms (SBMPs) due to the restricted workspace of the SBMPs. The motion cueing algorithms (MCAs) have been used to regenerate the same motion feeling of the real vehicle with consideration of the SBMP's limited workspace. The idea of the model predictive control (MPC) is used recently to find the optimal value of the input signal with consideration of the SBMPs’ limitations. The hexarot manipulator is developed as a mid-sized SBMP with high capability of generating the angular displacement compared to hexapod SBMP. It is the first time that hexarot manipulator is employed as an SBMP with controller unit due to its high workspace limitation, especially in roll angle. In addition, the main contribution of this research is to design and develop a fuzzy incremental controller to configurate the hexarot SBMP using MPC-based MCA to improve the motion feeling of the high-frequency signals compared with the traditional PID controller. The proposed method is verified using MATLAB software. Finally, the results demonstrate the right follow of the motion signals using MPC-based MCA, which is not possible using the common hexapod manipulators.
Research on Model Design about Learning Result Prediction and Intervention Based on Dynamic Data Mining

Predicting the academic performance of online learners and promptly intervening and guiding them is an effective way to improve the effectiveness of online learning. How to predict the academic performance and behavior of online learners, implement academic early warning based on prediction results, and provide evidence for teaching decision-making is one of the problems that network education needs to solve, and also an important research issue in educational big data research. This study uses the decision tree method in data mining technology to predict the learning behavior and performance of online learners, and constructs adaptive learning outcome prediction and intervention models. The application scenarios of the model are analyzed from the perspective of curriculum developers, teachers and students.
Generating implementations from formal specifications : a translator from Promela to Java

HiSPIN stands for Highly interactive SPIN. SPIN itself is an abbreviation for Simple Promela INterpreter. The HiSPIN project aims at supporting the handling of protocols and algorithms. It tries to combine the advantages of the existing tools Promela, SPIN and HiSAP. The main parts of this work comprise a Java implementation of the SPIN simulator and the development of a communication model which enables the simulation process to control visual elements of the HiSAP visualization toolkit.
A forensics acquisition method of WhatsApp data on Android smartphone

Technology evolution in mobile devices and smartphones are kept progressing in demand. The environment of mobile devices is defined as personal usage; isolating the security priority at very beginning. Prior to the reason, it becomes a daring new target for the cybercrimes. WhatsApp application known as most widely used for communication platform, as well as a bold aim to perform mischiefs. Thus in this thesis, we propose the forensically sound method of data acquisition and analysis of WhatsApp application on Android smartphone. We will form guidelines on how forensic investigators extract the artifacts from WhatsApp. By having the artifacts, forensic investigators enable to describe on how the artifacts can be generated, describe relationship of the artifacts and the analysis can be done. The results gained by the method proposed in this thesis are at par with popular mobile forensic tools such as Cellebrite.
Uni- and Multi-modal Uncertainty Visualization in 2D Scalar Field Ensembles

The aim of uncertainty-aware scalar ﬁeld visualization is to convey the most likely case, but also the uncertainty associated with it. In scientiﬁc simulations, uncertainty can be modeled using an ensemble approach. Statistics are then drawn from the ensemble outcome to compute the most likely case and its uncertainty. However, the statistical distributions do not necessarily need to be uni-modal. We present an approach to visualize uncertain 2D scalar ﬁelds that extends existing uni-modal distributions based on colored heightﬁelds and 2D glyphs to multi-modal ones. We compare the approaches by conducting user experiments for both the uni-and multi-modal case
Remaining Useful Life Prediction With Fusing Failure Time Data and Field Degradation Data With Random Effects

Accurate remaining useful life (RUL) prediction has a great significance to improve the reliability and safety for key equipment. However, it often occur imperfect or even no prior degradation information in practical application for the existing RUL prediction methods, which could produce prediction error. To solve this issue, this paper proposes a two-step RUL prediction method based on Wiener processes with reasonably fusing the failure time data and field degradation data. First, we obtain some interesting natures of parameters estimation based on the basic linear Wiener process. These natures explain the relationship between the parameters estimation results and the feature of degradation data, i.e. item sample numbers, detection time and detect frequency, and give the basis regarding how to reasonably fuse the failure time data and field degradation data. Second, under the Bayesian framework, we further propose a two-step method by fusing the failure time data and field degradation data with considering the random effects based on the proposed natures of parameters estimation. In this method, we propose an EM algorithm to estimate the mean and variance drift parameter of Wiener processes by the failure time data. Next, we generalize this two-step RUL prediction method to the nonlinear Wiener process. Last, we use two case studies to demonstrate the usefulness and superiority of the proposed method.
Twitter Sentiment Based Mining for Decision Making using Text Classifiers with Learning by Induction

The amount of data residing in social media currently untapped is certainly limitless as millions of people are constantly posting a message or the other to public forums on the internet. Twitter being one of the largest social media networks with over 336 million monthly active users has proven to be a fertile ground for harvesting opinion from multiple people. This work explores how opinion can be extracted from tweets to discover people’s view concerning a certain subject matter. It focuses mainly on overcoming the limitation of the current approach to social media sentiment based mining for decision making which is that opinions derived from multiple sources are limited to available connections on the social media platforms and lack of improved accuracy of mined opinions. In order to achieve this, the proposed framework provides a platform to mine opinions from more than the available friends and connections on the social media platform and in addition, improve the quality of the opinion mined by implementing supervised learning algorithms with learning by induction in Twitter data analysis. In this research, three different supervised machine learning algorithms were applied to a dataset curated by graduate students at Stanford in order to accurately classify tweets into either positive or negative sentiment based on its content. It was discovered that Maximum Entropy had the highest accuracy of 83.5% among the three algorithms. The research has provided a web application which would enable users such as CEOs, Market Analysts, and random users make quality decision based on others’ opinions.
A Review of Resource Scheduling in Fog based Cloud Environment

: The paper has considered resource scheduling concept in fog environment. Here in this research paper the discussion has been made on cloud computing with Fog computing system. Requirement of Fog computing has been discussed here. Moreover several researches in the field of cloud and fog are explained. The research paper focuses on the scheduling algorithm for resource management. The concept of node duplication has been discussed in critical path algorithm. Such algorithms are suppose to minimize the make span time. These algorithms are performing efficiently to manage cloud resources.
Realistic endoscopic image generation method using virtual-to-real image-domain translation

A realistic image generation method for visualisation in endoscopic simulation systems is proposed in this study. Endoscopic diagnosis and treatment are performed in many hospitals. To reduce complications related to endoscope insertions, endoscopic simulation systems are used for training or rehearsal of endoscope insertions. However, current simulation systems generate non-realistic virtual endoscopic images. To improve the value of the simulation systems, improvement of the reality of their generated images is necessary. The authors propose a realistic image generation method for endoscopic simulation systems. Virtual endoscopic images are generated by using a volume rendering method from a CT volume of a patient. They improve the reality of the virtual endoscopic images using a virtual-to-real image-domain translation technique. The image-domain translator is implemented as a fully convolutional network (FCN). They train the FCN by minimising a cycle consistency loss function. The FCN is trained using unpaired virtual and real endoscopic images. To obtain high-quality image-domain translation results, they perform an image cleansing to the real endoscopic image set. They tested to use the shallow U-Net, U-Net, deep U-Net, and U-Net having residual units as the image-domain translator. The deep U-Net and U-Net having residual units generated quite realistic images.
Load Forecasting Model for Energy Management System using Elman Neural Network

Electric load forecasting is used for forecasting of future electric loads. Since the economy and reliability of operations of a power system are greatly affected by electric load, cost savings mainly depend on load forecasting accuracy. An accurate system load forecasting which is used to calculate short-term electric load forecasts, is an essential component of any Energy Management System (EMS). This can be improved by making use of Artificial Neural Networks (ANN). Existing Boosted Neural Networks (BooNN) technique helps in reduction of forecasting errors and variation in forecasting accuracy. However it is not flexible to rapid load changes.In the proposed work, Elman Neural Network technique is considered. This technique improves the load forecasting accuracy. The proposed method is implemented in IEEE 14 bus system. Simulation results showed that this method has increased the Voltage profile and also the active power losses have been reduced. Overall power transfer capability has been improved. Also the computational time has been minimized when compared to the existing techniques.
Measure liquid viscosity by tracking falling ball Automatically depending on image processing algorithm

Abstrac. A fast method presented within this paper to find transparent liquid viscosity. An introduced tracking algorithm in image processing used to find a terminal velocity automatically of a falling ball in a tube that filled with a tested liquid. Terminal velocity considered an important parameter to find a liquid viscosity. Therefore, four iron balls, with different diameters and a smartphone camera are used to measure the falling ball terminal velocity. The estimated velocity from the introduced algorithm is compared with the calculated velocity and shows a good matching. The second algorithm used to estimate the viscosity of the tested liquid. The limitation of the ball diameter around 5mm to measure the terminal velocity. The result shows error matching between the fitting data and the estimated data ranging (0.00-0.1%). This method is a faster and easier experiment to use to find any transparent liquid viscosity comparing with a viscometer.