Miracle-dz/UniFAR-FTUs huggingface dataset:
A Privacy-Preserving System for Targeted Coupon Service

It is increasingly popular to use behavioral targeting in online coupon service for targeted coupon delivery, wherein customized coupons can be delivered to only eligible users whose behavioral profiles match targeting profiles specified by the vendor. While allowing the vendor to personalize the service and build loyalty, such practice also raises the security challenges of protecting users’ behavioral profiles and the vendor’ targeting profiles. Our research caters for the trend of targeted coupons yet aims to offer privacy assurance. Unlike prior works which are largely constrained by the mere support for secure behavioral targeting, we ambitiously target a new system design that uniquely provides comprehensive functionalities to satisfy practical needs. Specifically, we explore and present the first full-fledged system design for targeted coupon service with three practical functionalities: (i) Private range search on coupon discounts: it enables a user to securely search for encrypted targeted coupons with certain discount range; (ii) Private behavioral targeting: it ensures that only eligible users can decrypt the ciphertexts of targeted coupons obtained from search; (iii) Private blockchain-empowered coupon redemption: it enables coupon redemption to be securely and transparently settled on the blockchain. We conduct extensive experiments and the results show that our secure system design is practically affordable.
Privacy Protection in Transformer-based Neural Network

With the great success of neural networks, it is important to improve the information security of application systems based on them. This paper investigates a scenario where an attacker eavesdrops the intermediate representation computed by the encoder layers and tries to recover the private information of the input text. We propose a new metric to evaluate the encoder’s ability to protect privacy and evaluate the Transformer-based encoder, which is the first privacy research conducted on Transformer-based neural networks. We also propose an adversarial training method to enhance the privacy of Transformer-based neural networks.
Detection of Void Density in the Through Silicon Via Using Artificial Neural Network

3-D integration of integrated circuits based on through silicon via (TSV) could achieve high-performance, multifunctional and heterogeneous microsystem. However, various defects inside TSV may degrade system performance, it is essential to detect these defects for system design and process improvement. In this paper, we propose a defect detection method using artificial neural network (ANN) to detect the volume density of voids in the TSV. Different ANNs are designed and trained, their performance is compared. The results demonstrate the detection method could predict void density accurately.
Service Station Positioning for Mobile Charger in Wireless Rechargeable Sensor Networks

Near-field wireless power transfer has been widely used for powering sensor nodes in WSN, where mobile charger (MC) is employed to traverse the network. However, due to the limited energy of MC, MC needs to return to service station (SS) for replacing energy source for multiple times. Therefore, the location of SS directly determines the travelling distance of MC and the charging completion time as well. Motivated by this, we consider the SS positioning problem with jointly optimizing the charging tour of MC. We decompose it into two sub-problems and propose efficient algorithms to solve them based on a novel discretization method.
A Systematic analysis of Data-intensive MOOCs and their key Challenges

Big Data blends modern technologies with numerous data management techniques to handle a wide variety of concerns that occur when operating with data of huge volume, variety and velocity. Big data deals with complex semi-structured and unstructured data from several sources and formats which include Social Media content in free form, data from E-commerce sites, Weather forecasting statistics, Clinical Diagnosis, Share Market Transactions and Smart Computing Environments. In the same way, big data offers substantial prospects in the discipline of Education, E- Learning and Learning Analytics. Application of big data analytics in E-Learning helps to assess the quality of Teaching, Development of Curriculum, predict learning outcomes, Career Development and Readiness, Attrition Risks and Feedback Analysis. The Massive Open Online Courses (MOOCs) have produced a major influence on E-Learning with the availability of Live and pre-recorded Lectures, Easy-to-learn Tutorials, Novel Assessment Methodologies, Quick feedback and results. In this paper, we present the various Technologies that formulate the MOOCs and address the learning paradigms and key challenges.
Efficient Sidelink Identity Detection and Frequency Ambiguity Resolution for LTE-D2D Communications

Device-to-device (D2D) communication is a key enabler to facilitate the realization of the Internet of Things (IoT) in which devices directly communicate with each other. One of the major challenges in the D2D network is robust and low-cost synchronization for ultra-reliable low latency communication. To address this issue, this paper presents an efficient frequency ambiguity resolution and sidelink synchronization identity detection scheme for D2D communications in the long term evolution system. To perform low-cost joint detection, the proposed method is based on the grouping of the primary sidelink synchronization sequence (PSSS) subcarriers. The PSSS subcarriers are grouped into a number of subsets wherein the phase difference between the PSSS subcarriers is approximately a multiple of $\pi /2$ . Numerical analysis is performed to present the relationship between detection probability and design parameter. Simulations show that the proposed method has the same performance as the existing detection method, with significantly reduced computational complexity.
Artifact Reduction in Spare-view Image Reconstruction in C-arm CT

C-arm CT images suffer often from data truncation artifacts as a result of the limited detector size and also of interventional devices that are outside of the imaging field-of-view (FOV) of the C-arm CBCT. The artifacts appear prominently in the form of streaks especially when the scanning angular sampling is sparse. In this work, we investigate optimization-based image reconstruction from sparse-view, angularly-varying truncated data with reduced artifacts. The reconstruction problem is formulated as a constrained optimization program in which a combination fidelity term of data and data-derivative was used for effective suppression of the truncation artifacts, and the Chambolle-Pock (CP) algorithm was used to solve the optimization program. The results of the study suggest that while the streak artifacts caused by the angularly-varying truncation in FDK reconstruction become severe as the number of view decreases, the image quality yielded by the optimization-based reconstruction remains largely unchanged.
Virtual Simulation and Modeling of an Inductor-Piece System for Induction Cooker

The present work considers virtual simulations and modeling of an inductor-piece system for induction cooker. As a result of the study an approach for calculating the equivalent parameters of an induction-piece system in an induction cooker, based on the finite element method. The computer investigation and analysis are expanded by modelling processes in COMSOL Multiphysics 5.4. An electromagnetic and temperature model of the equivalent inductor-piece system in an induction cooker is proposed. Electromagnetic and temperature field modelling at different operating frequencies of the power supply has been performed. The results obtained are used in virtual prototyping and industrial electronics training. The proposed methodology, coupled with modern computing, provides new opportunities for optimizing and improving the energy performance of industrial and home devices and systems.
About Measurement of PET Spatial Resolution

Spatial resolution is an important parameter for assessing the performance of positron emission tomography (PET) systems but this apparently simple measurement is complicated by various issues including the reconstruction algorithm, parameter settings and acquisition methodology. Here we describe a series of experiments that aim to untangle some of these issues. Measurements of PET spatial resolution were made using 3 different experimental arrangements: (1) a point source in air; (2) a point source in a radioactive background; and (3) a uniform cylinder positioned at a slightly oblique angle, allowing spatial resolution to be measured from the phantom edge response function. All experiments involved 18F and were performed on a clinical scanner, Biograph mCT. Ordered-subsets expectation maximization including time-of-flight (OSEM+TOF, no point spread function modelling) rapidly converged to a stable full-width at half-maximum (FWHM) for all 3 geometries. OSEM+TOF provided better spatial resolution than Fourier rebinning plus filtered back-projection (FORE+FBP). FWHM degraded with increasing radial distance but in a complex way, apparently dependent on the reconstruction algorithm. FWHM derived from point sources and OSEM+TOF were similar with or without a radioactive background (radial FWHM at 10 cm, 4.77 ± 0.11 mm vs. 4.78 ± 0.06 mm). A uniform cylinder positioned at an oblique angle allowed measurements of radial FWHM (4.74 ± 0.17 mm) that were very consistent with point source measurements. These results support use of the uniform cylinder phantom as a tool for resolution assessment that may be particularly useful for multi-center evaluations due to its simple set-up.
On Parameter Selection for Joint Spectral Reconstruction in Y90 SPECT

Model-based image reconstruction for Y90 SPECT is challenging due to energy-dependent forward models including collimator-detector response (CDR) and attenuation correction factor (ACF). Since it is common to model CDR and ACF for a single energy for one energy window, there is a trade-off between the width of an energy window (number of primary counts) and the accuracy of modeling energy-dependent CDR and ACF. We previously proposed a joint spectral reconstruction (JSR) for Y90 SPECT to utilize wide energy window while accurately modeling energy-dependent forward projections for multiple windows and showed promising quantitative performance. However, the scale parameter model in JSR to relate images of different energy windows was not properly considered. In this work, we argue that scale parameters should reflect two probabilities: 1) different emission probabilities for the different windows and 2) detection probabilities in SPECT crystal that are different for the two windows. These arguments lead us to utilize Monte Carlo simulation of a point source instead of primary phantom counts for estimating scale parameters to avoid the effect of energy-dependent attenuations. We performed a SIMIND MC simulation study with a digital phantom similar to the NEMA PET phantom, but with larger hot spheres and a cold sphere in the center. MC simulation was done with 6 energy windows starting from 105keV to 285keV with the width of 30keV for each window using a new emission spectrum with both internal and external bremsstrahlung generated from theory and Penelope simulations. Our proposed JSR with new scale parameters yielded favorable quantitative results for both hot and cold spheres over other methods such as JSR with previous scale parameters, single spectral reconstructions with narrow and wide energy windows. Our proposed method yielded better recovery coefficients than other methods by 0.5-16.2% at the 30 iteration for all hot spheres. These differences were particularly substantial for the smallest hot sphere, 2.3-16.2%. For the cold sphere, JSR proposed yielded 3.0-15.8% lower residual count error than other methods.
Sliding Mode Control with High-Order Disturbance Observer Design for Disturbance Estimation in SPMSM

This study proposes high -order disturbance observer (HODO) SMC method along with HODO load torque observer to control speed of SPMSM system under speed and load torque variations has been proposed. Firstly, dynamic system of the SPMSM has been formulated. Secondly, switching feedback control law with use of continuous approximation of sliding surface to avoid chattering effect has been used. Thirdly, the proof of reachability condition has been demonstrated. Finally, for estimation of load torque variation HODO observer has been integrated to the control system. The simulation results have been shown significant improvement of performance for both, under nominal parameters and model uncertainties than with conventional PI-PI method.
Identifying Money Laundering Accounts

Money laundering is often associated with criminal activities. Anti-money laundering is thus regarded as an important task in many countries. However, as it is common that money launderers divide the dirty money into multiple parts and make sequences of banking transfers or commercial transactions, manually detecting activities of money laundering is challenging. To ease the task, this work establishes a two-phase intelligent method based on machine learning and data analysis techniques for identifying suspicious money laundering accounts from the transaction data. The first phase emphasizes on identifying every suspicious money laundering account while the second phase further retrieves highly suspicious ones so that both the recall and precision for the identification of money laundering accounts can be somewhat taken care of. Evaluated on the data given by Bank SinoPac, the established intelligent method achieves a recall rate of 26.3%, which is three times the recall rate (8.6%) of the Money Laundering Control Act in Taiwan, in the first phase, and later the precision rate can be increased up to 87.04% in the second phase.
A Review On Performance Of Voice Feature Extraction Techniques

In the digital era, the computing applications are to be secured from anonymous attacks by strengthening the authentication credentials. Numerous methodologies and algorithms have been proposed implementing human biometric as unique identity and one such identity is human voice print. The human voice print is a unique characteristic of the individual and has a wide variety of techniques in representing and extracting the features from the digital speech signals. The voice recognition techniques were executed on different platforms and exploit different mathematical tools in voice feature extraction, leading to dissimilarity in performance and results. In this paper, we investigate, analyze and present a review on performance of numerous voice recognition techniques.
A Compact Measurement Setup for In-Situ Material Characterization in the Lower THz Range

Frequency Modulated Continuous Wave radar systems offer a very large dynamic measurement range, while at the same time they can be fabricated very compact and cost efficient. Due to this, FMCW-sensors are a feasible alternative to costly measurement equipment like vector network analyzers. Applying calibration techniques and algorithms from the VNA domain, they can also be used to characterize materials with very high precision. At the same time, the short measurement time allows their use in mobile high precision measurement setups. This paper presents such a mobile setup that is easily calibrated an can be used for in-situ material parameter estimation and data collection for e.g. channel models. For this purpose, a SiGe-radar system in the lower THz range from 200 GHz to 250 GHz is used.
Optimising Faceted Secure Multi-Execution

Language-Based Information Flow Control (IFC) provides strong security guarantees for untrusted code, but often suffers from a non-negligible rate of false alarms. Multi-execution based techniques promise to provide security guarantees without raising any false alarms. However, all known multi-execution approaches introduce extraneous performance overheads which are rarely studied. In this work, we lay down the foundations for optimisation techniques aimed at reducing these overheads to a managable level, thus helping to make multi-execution more practical. We characterise our optimisations as data-and control-oriented. Data-oriented optimisations reduce storage overheads— which also helps to remove unnecessary repeated computations. In contrast, computation-oriented optimisations rely on program annotations in order to reduce needless computation. These annotations motivate the need for a new, stronger, theoretical notion of transparency— i.e., a stronger notion for characterising the lack of false alarms. To show the efficacy of our optimisation techniques, we apply them to two case-studies: a secure (faceted) database and a chat server written in a multi-execution based IFC framework. Our case-studies clearly show that our optimisations significantly reduce the storage and computational overhead, sometimes from exponential to polynomial order. All of our formal results are accompanied by mechanised proofs in Agda.
ARG: Automatic ROP Chains Generation

Return Oriented Programming (ROP) chains attack has been widely used to bypass Data Execution Prevention (DEP) and Address Space Layout Randomization (ASLR) protection. However, the generation technology for ROP chains is still in a state of manual coding. While, current techniques for automatically generating ROP chains are still insufficiently researched and have few successful applications. On the other hand, the existing methods are based on using Intermediate Language (IL) which is in order to translate the semantics of original instructions for symbolic execution, and then fill in a predefined gadget arrangement to automatically construct a gadget list. This kind of methods may bring following problems: (1) when converting semantics of original to IL, there is a large amount of overhead time, critical instructions may be discarded; (2) the process of populating a predetermined gadget arrangement is inflexible and may fail to construct ROP chains due to address mismatching. In this paper, we propose the Automatic ROP chains Generation (ARG) which is the first fully automatic ROP chains generation tool without using IL. Tested with data from 6 open-source international Capture The Flag (CTF) competitions and 3 Common Vulnerabilities & Exposures (CVE)s, this technology successfully generated ROP chains for all of them. According to the obtained results, our technique can automatically create ROP payloads and reduce up to 80% of ROP exploit payloads. It takes only 3–5 seconds to exploit successfully, compared to manual analysis for at least 60 minutes, as well as it can effectively bypass both Write XOR Execute ( $\text{W}\oplus \text{X}$ ) and ASLR.
Real Processing-in-Memory with Memristive Memory Processing Unit (mMPU)

Memristive technologies are attractive candidates to replace conventional memory technologies and can also be used to perform logic and arithmetic operations. In this paper, we show how memristors are used to combine data storage and computation in the memory, thus enabling a novel non-von Neumann architecture called the 'memristive memory processing unit' (mMPU). The mMPU relies on a memristive logic technique called 'memristor aided logic' (MAGIC) that requires no modification to the memory array structure. By greatly reducing the data transfer between the CPU and the memory, the mMPU alleviates the primary restriction on performance and energy efficiency in modern computing systems. This paper describes basic principles and design considerations of MAGIC and the mMPU and presents a case study of digital image processing to demonstrate the benefits.
Analysis of I[123]-Ioflupane SPECT intensity iso-surfaces to assist the diagnosis of Parkinsonism

Parkinson’s Disease (PD) is a progressive neurological disorder that affects the motor system. It is related to the destruction of pigmented neurons in the substantia nigra by triggers that are still unknown [1] . I[123]-Ioflupane images 1 are usually used for its diagnosis [2] . As PD is characterized by a marked reduction of dopaminergic neurons in the striatal region, these images give us a quantitative measure of the spatial distribution of the transporters in the striatum.
Wind Turbine Planetary Gearbox Fault Diagnosis Based on Self-Powered Wireless Sensor and Deep Learning Approach

In this paper, a novel and more effective fault diagnosis approach for wind turbine planetary gearbox (PGB) is proposed. In order to better detect the faults in the early stage of the faults of the wind turbine PGB, the corresponding maintenance measures can be carried out to prevent the faults from becoming more serious, so as to seriously affect the normal operation of the fan gearbox. The gear with lighter fault degree is used to simulate the early fault signal. Compared with the fault which has seriously affected the normal working condition, the fault characteristics of the early fault signal are more difficult to detect. So in this design, deep belief network (DBN) optimized by quantum particle swarm optimization (QPSO) algorithm is used to extract deeper and more identifiable features of slight fault signal. After optimization by QPSO algorithm, DBN can get a most suitable structure according to the actual working signal of fan gearbox. Then these extracted features are input into the least squares support vector machine (LSSVM) optimized by QPSO for fault diagnosis test. At the same time, the wireless sensor nodes using self-energy in vibration state are optimized. By using microcontroller unit (MCU) MSP430F149 and nRF24L01 radio frequency (RF) chip with lower energy consumption, the normal dormant state can be maintained, the power requirement of transmission mode can be met, the stability of the whole node can be improved, and the phenomenon of energy shortage caused by short-term fluctuation can be prevented. The comparative experiments in this paper show that this method has good effect on the fault diagnosis of wind turbine PGB.
Insider Threat Detection Based on Adaptive Optimization DBN by Grid Search

Aiming at the problem that one-dimensional parameter optimization in insider threat detection using deep learning will lead to unsatisfactory overall performance of the model, an insider threat detection method based on adaptive optimization DBN by grid search is designed. This method adaptively optimizes the learning rate and the network structure which form the two-dimensional grid, and adaptively selects a set of optimization parameters for threat detection, which optimizes the overall performance of the deep learning model. The experimental results show that the method has good adaptability. The learning rate of the deep belief net is optimized to 0.6, the network structure is optimized to 6 layers, and the threat detection rate is increased to 98.794%. The training efficiency and the threat detection rate of the deep belief net are improved.