Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Temporal Mix-Zone Placement Approach for Location-Based Services Privacy

This paper proposes the Dynamic Temporal Mix-Zone Placement (DTMZP), a novel heuristic approach for the optimal mix-zone placement problem in Location-Based Services privacy. Mix-zone is a privacy model that changes the pseudonym of $n$ mobile entities (e.g., vehicles) that cross a specific geographic region at a given time interval. We present a formal definition of DTMZP and evaluate it through simulations using the Cab-spotting dataset. We evaluate DTMZP considering the mix-zones coverage and pseudonymisation rates. The obtained results reveal a coverage rate of 88.56% and a pseudonymisation rate of 77.86% in the best scenario.
Blind Detection in Coexistence of Human-Type and Machine-Type Communications

In this paper, we study joint device activity identification, channel estimation, and signal detection for the uplink transmission of a human-type communication (HTC) and machine-type communication (MTC) coexisted massive MIMO system. We first establish a probability model to characterize the crucial system features including channel sparsity of massive MIMO, signal sparsity of MTC packets, and sporadic access of MTC. With the probability model, we formulate a blind detection problem and establish a factor graph representation of the problem. Based on that, we develop a turbo message passing (TMP) algorithm involving affine sparse matrix factorization and service type identification. We show that our proposed blind detection algorithm significantly outperform their counterpart algorithms including the training-based algorithm.
Opportunistic Networks Link Prediction Method Based on Bayesian Recurrent Neural Network

The goal of link prediction is to estimate the possibility of future links among nodes using known network information and the attributes of the nodes. According to the time-varying characteristics and the node’s mobility of opportunistic networks, this paper proposes a novel link prediction method based on the Bayesian recurrent neural network (BRNN-LP) framework. The time series data of a dynamic opportunistic networks is sliced into snapshots in which there exist the correlation information and spatial location information. A vector of a snapshot is constructed based on such information, which represents the link information. Then, the vectors of multiple network snapshots constitute a spatiotemporal vector sequence. Benefiting from the BRNN’s ability of extracting the features of time series data, the correlation between spatiotemporal vector sequence and node connection states is learned, and the law of the link evolution is captured to predict future links. The results on the MIT Reality dataset show that compared with methods such as the similarity-based indices, the support vector classifier, linear discriminant analysis and recurrent neural network, the proposed prediction method is more accurate and stable.
An Improved Behavior Model for IGBT Modules Driven by Datasheet and Measurement

This article describes an improved behavior model for IGBT modules. The new steady-state characteristic model of the IGBT with a correction function was built to improve the saturation current for different gate-emitter voltages. Two important nonlinear capacitances were described by two continuous functions to solve the convergence problem in the circuit simulation. The parasitic parameters generated by the package were extracted by the impedance measurement. All the parameters of the model are derived from datasheet and measurement. The improved model was implemented by MAST language in SABER circuit simulation. A double-pulse test was applied to verify the improved model. Compared with the conventional model for transient waveforms, switching losses, and electromagnetic interference (EMI), the improved model shows a better agreement with experiments. The satisfactory results indicate that the improved model can offer a simple, fast, and generic modeling approach.
Design Considerations for Si- and Ge-Stacked Nanosheet pMOSFETs Based on Quantum Transport Simulations

Design considerations of vertically stacked horizontal nanosheet (NSH) gate-all-around pMOSFETs are examined at the sub-5-nm technology node using in-house-developed non-equilibrium Green’s function (NEGF) quantum ballistic transport simulator. In the individual Si and Ge NSHs, ON-state current and subthreshold swing are evaluated and analyzed for different crystal orientations and various sheet widths. Performance benchmarking of the stacked FET arrays at the iso-footprint is accomplished to explore the roles of sheet configurations further with stack number and sheet spacing changed. It is found that the benefit of the larger effective channel width provided by the wider NSH is always compromised by degraded gate control, especially in the Ge channel. [111] and [100] are shown to be the best transport orientations for individual Si and Ge NSHs, respectively. However, [100] channel vertically confined along [011] shows greater potential for the applications of wider Si and Ge NSHs in the stacked FET array. The process-induced NSH width variation is studied statistically, and it is shown to cause significant performance fluctuations in the stacked array consisting of wide Si or narrow Ge NSHs.
Android Application Security Detection Method Based on Metropolis Algorithm

Smartphones are becoming more and more popular due to their convenience, Android system smartphones accounting for most markets. The security of Android applications (Apps) has become especially important. Currently, there are many security detection methods based on Android Apps. But these methods rarely consider the uncertainty of the feature. In our paper, an Android applications security detection method based on Metropolis algorithm is designed. This method analyzes Android’s 24 dangerous permissions using the Metropolis algorithm, removes uncertainty permissions, and extracts certain permission features. Then, the features are learned and classified through the classification technology of machine learning. Our approach uses 1501 Android Apps samples (including 870 benign samples and 631 malicious samples). From the experiment, our method reduces the detection features, and the accuracy of malicious application detection can reach 93.5%.
Protecting Code Injection Attacks in Intelligent Transportation System

TMC (traffic management system) is the vital unit of Intelligent Transport System (ITS) as it provides complex services by interacting with other smart devices, objects and infrastructure wirelessly. However, there is growing concern about the threat of cyber attacks against connected cars, autonomous vehicles, and Smart Roads, as attacks on these will be more highly visible, have a more immediate effect, and potentially have a disastrous impact on public safety. Code injection attacks pose a serious security threat among the Internet community. In this paper, we provide a security model on ITS against network attacks. We undertake a comprehensive requirement analysis of access control mechanisms for ITS in particular malicious code injections. We propose an innovative solution to filter the SQL and XSS injection attacks by using Snort IDS. The proposed detection technique uses Snort tool by augmenting a number of additional Snort rules. Experimental results from simulations based on Snort rules show that our techniques outperform similar existing techniques.
Clustering Users for the Deployment of UAV as Base Station to Improve the Quality of the Data

Unmanned aerial vehicle (UAV) is a promising technology for mitigating Terrestrial Base Stations failures or even becoming an ideal temporary solution to offloading. UAV as Base Stations (UABS) can be deployed to complement existing cellular systems by providing additional capacity to hotspot areas as well as to provide network coverage in emergency and public safety situations. In terrestrial cellular networks, mobile devices at the cell edge often pose a performance bottleneck due to their long distances from the serving terrestrial base station (TBS), especially in the hotspot period when the TBS is heavily loaded. Simulation results show the gains of the proposed offloading scheme (OS) by improving the throughput, and quality of service (QoS) compared with a conventional LTE overloaded network.
Mr Image Processing to Predict Complete Responders by Evaluating the Tumor Regression Grade: A Sensitivity Analysis

This work aims to realize a computer-aided method in order to correctly predict and classify complete responders (CRs) patients, with rectal cancer diseases diagnosed and treated with neoadjuvant radiochemotherapy (RCT), employing the tumor regression grade (MR-TRG) estimated by magnetic resonance imaging. The study involved a total of 65 patients and a 3.0 Tesla scanner was employed to perform the magnetic resonance (MR) examinations in order to calculate TRG. The automatic method, by processing and testing patients' data, allows to determine the optimum threshold dividing CRs patients from patients that are considered non responders. To automatically determine the best testing rule, a cross-validation statistical analysis was carried out to evaluate the prediction accuracy of the classifier. The algorithm collected the outcomes of the performed cross-validation analysis and the obtained results show the percentages of correct instances and misclassified patients. A sensitivity analysis has also been carried out to study the effect of non-optimum thresholds in the classification procedure. The computer-aided classification of CRs appears to be feasible and it may represent a helpful method to recognize CRs patients, supporting clinicians performing disease prognoses and patient survival expectations in order to provide treatments' customization.
Dynamic Model of Macroregion Subjects' Interests Coordination

A dynamic model for reconciling common and private interests (SOCHI model) of federal entities in the development of the macroregion was built. The model takes into account threshold restrictions on the volume of production, emissions and discharges of pollutants. The presented model takes into account the interaction of federal entities in the development of neighboring entities and the macro-region in symbiosis by redirecting part of the internal investment flows of each region to common development goals and projects, which contributes to a synergistic effect. The results of the model study are tested on the data of the subjects of the Southern Federal District
Energy Efficient Resource Allocation for Wireless Power Transfer-Supported D2D Communication With Battery

Recently, researchers pay much attention to Wireless Power Transfer-supported Device-to-Device (D2D) Communication underlaying Cellular Network (WPT-DCCN), which has reliable and controllable energy resource, high energy and spectral efficiency. However, the Energy Efficiency (EE) of the whole WPT-DCCN, which combines the time allocation and the power control of all users (including base station, cellular and D2D user), is rarely studied. Consequently, this study establishes an EE optimization model of the whole WPT-DCCN. Facing the complexity of the modeled mathematical model, the EE problem is approximately converted to a convex one by a proposed Convex Approximation Scheme (CAS). Due to the fractional expression of the variables needed to be solved, it is hard to directly obtain an optimal solution for the converted convex problem through current convex optimization toolbox such as CVX. Thus, we skillfully utilize the Generalized Benders Decomposition (GBD) to obtain an optimal solution for the converted convex problem by analyzing the characteristics of the converted problem. Simulation results show that the proposed CAS can give a tight lower-bound solution of the original EE problem. Furthermore, CAS can help us to investigate some meaningful system features, for example the suitable energy conversion efficiency setting under different WPT-DCCN scenarios, which can give us a clear reference about the deployment of future WPT-DCCN.
Sentilyzer: Aspect-Oriented Sentiment Analysis of Product Reviews

Aspect-oriented sentiment analysis is used in particular on textual product reviews to identify positive or negative product characteristics. This information is beneficial not only for customers related to purchasing decisions, but also used by manufacturers to improve their products. In this short paper, we introduce Sentilyzer, a system which performs state-of-the-art aspect-oriented sentiment analysis based on automatically generated category-specific sentiment and aspect dictionaries from Amazon product reviews.
Adoption of Business Intelligence - Technological, Individual and Supply Chain Efficiency

Making strategic decisions in a vigorous business setting is a challenge encountered by many organizations nowadays. Today, information is gathered all over the place and is rapidly expanding. Organization required powerful application and systematic system that could run in real time, provide insightful tracking for supply chains, logistics and operations that closely related to applications for sales tracking, hourly, daily to monthly production, financial, and many other sources of business data for purposes that consist of business performance management. Business Intelligence has a critical role in terms of organizational development as Business Intelligence (BI) be able to provide a competitive advantage in the context of achieving positive information asymmetry, that is, unifying and making useful heterogeneous data. However, the impact of BI and the relative importance of its insight on business performance have not yet been investigated. For this study, data were collected from a survey questionnaire of IT managers in 162 multinational companies in Malaysia and analyzed using the partial least squares (PLS) with the SmartPLS software. This research recommends that although BI and its insight contribute to management practices, the information requirements are diverse according on the level of uncertainty versus ambiguity characteristic of the organizations practice.
Research on Fault Detection Technology of Information System Based on Trusted Computing

In this paper, the framework of distributed fault detection for trusted computing is designed, and the operation process of fault detection system is drawn. Aiming at the requirement of distributed fault detection, a distributed fault monitoring mode based on self-organizing network is designed, which solves the key problems of entity monitoring coverage, real-time message dissemination and survivability of monitoring function. In view of the coverage of message dissemination in self-organizing monitoring domain, theoretical analysis and experimental verification are carried out.
Environment Aware Privacy-Preserving Authentication with Predictability for Medical Edge Computing

With the development of IoT, smart health has significantly improved the quality of people's life. A large amount of smart health monitoring system has been proposed, which provides an opportunity for timely and efficient diagnosis. Nevertheless, most of them ignored the impact of environment on patients' health. Due to the openness of the communication channel, data security and privacy preservation are crucial problems to be solved. In this work, an environment aware privacy-preserving authentication protocol based on the fuzzy extractor and elliptic curve cryptography (ecc) is designed for health monitoring system with mutual authentication and anonymity. Edge computing unit can authenticate all environmental sensors at one time. Fuzzy synthetic evaluation model is utilized to evaluate the environment equality with the patients' temporal health index (THI) as an assessment factor, which can help to predict the appropriate environment. The session key is established for secure communication based on the predicted result. Through security analysis, the proposed protocol can prevent common attacks. Moreover, performance analysis shows that the proposed protocol is applicable for resource-limited smart devices in edge computing health monitoring system.
Development of Wireless System for ECG Healthcare Monitoring

Mobile and health-monitoring technology has been consistently developed. This technology has many benefits for people to check their conditions in real time and report their status to medical staff via mobile communication. However, the existing devices are not popularly used because they can cause discomfort. This paper proposes a wireless 1-channel electrocardiogram (ECG) monitoring system that includes watch-type-2 modules and monitoring software. Each module uses not a wired connection but wireless communication such as Bluetooth, RF module to create a potential difference. The use of this system provides better comfort for users than existing ECG monitoring systems.
A dynamic pricing model for Turkey’s electricity distribution companies in the future Smart Grid infrastructure

Smart grids are a system in which the end user (customer, consumer or prosumer) takes an active role in the management of energy and that the energy flow is monitored / controlled in real time by bidirectional data communication, which incorporates advanced measurement infrastructures. The conclusive output of this system is the dynamic pricing of energy for demand response. This paper aims to present a sample dynamic pricing model using artificial neural network for energy distribution companies, utilities, aggregators and distribution system operators in the near future in Turkey.
Detecting Hot Topics From Academic Big Data

Detecting hot topics from massive academic data is a very challenging task. Because various types of academic information are overgrowing, e.g., papers, news, and blogs, which has gone far beyond the limits that researchers can accept. Therefore, how to efficiently and accurately detect hot topics from big academic data is the main problem that researchers are facing. In view of this, we design a general framework for Academic Hot Topic Detection (AHTD). Specifically, in this framework, a DeepWalk-based keyword extraction algorithm for a single paper (S-DWKE) is proposed to detect popular topics in diverse academic fields dynamically. Moreover, we propose a keyword extraction algorithm to extract keywords from multiple articles (M-GCKE), which enables us to detect new topics in emerging academic areas. Then, hot topics can be generated from keywords extracted by the S-DWKE and M-GCKE. A large number of experiments demonstrates the proposed framework effectively improves the performance of hot topic detection in the academic field and performs better than the comparison algorithms. We have applied the above work to the “Academic Headline” application to provide the hot topics for researchers.
Half-Select-Free Low-Power Dynamic Loop-Cutting Write Assist SRAM Cell for Space Applications

Smaller, lighter, and cost-effective satellite design is a major field of research today. Since such satellites are equipped with limited resources, there is a huge demand for low-power cache memory capable of performing reliably even when subjected to harsh cosmic radiations. To address the same, we have proposed a reliable, power-efficient, half-select-free dynamic loop-cutting write assist 12T (DWA12T) cell. The DWA12T has been compared with other state-of-the-art designs, such as the fully differential 8T (FD8T), single-ended disturb free 9T (SEDF9T), bit-interleaving architecture-implementing 11T (BI11T), differential 12T (D12T), and self-refreshing logic-based 12T (WWL12T) cells to estimate their relative performance in terms of major design metrics under severe process, voltage, and temperature (PVT) variations. The proposed cell exhibits <inline-formula> <tex-math notation="LaTeX">$1.65\times /3.03\times /1.08\times /1.38\times $ </tex-math></inline-formula> shorter write delay (<inline-formula> <tex-math notation="LaTeX">${T} _{\text{WA}}$ </tex-math></inline-formula>) and <inline-formula> <tex-math notation="LaTeX">$3.81\times /1.20\times /1.90\times /2.13\times $ </tex-math></inline-formula> higher write ability [write margin (WM)] than that of SEDF9T/BI11T/D12T/WWL12T and <inline-formula> <tex-math notation="LaTeX">$1.36\times /1.54\times /1.22\times $ </tex-math></inline-formula> shorter read delay (<inline-formula> <tex-math notation="LaTeX">${T} _{\text{RA}}$ </tex-math></inline-formula>) and <inline-formula> <tex-math notation="LaTeX">$6.63\times $ </tex-math></inline-formula> higher read stability [read static noise margin (RSNM)] than that of SEDF9T/BI11T/D12T and FD8T, respectively. Moreover, it consumes <inline-formula> <tex-math notation="LaTeX">$1.05\times /1.21\times /1.10\times /1.81\times $ </tex-math></inline-formula> lower static power (<inline-formula> <tex-math notation="LaTeX">${H} _{\text{PWR}}$ </tex-math></inline-formula>) than that of FD8T/SEDF9T/D12T/WWL12T while showing <inline-formula> <tex-math notation="LaTeX">$3.44\times $ </tex-math></inline-formula> higher hold stability [hold static noise margin (HSNM)] when compared to BI11T. In addition, the DWA12T cell consumes <inline-formula> <tex-math notation="LaTeX">$1.01\times /1.02\times /1.04\times $ </tex-math></inline-formula> smaller area than that of BI11T/D12T/WWL12T. Furthermore, DWA12T shows lower susceptibility to soft error when compared to FD8T. These improvements are achieved at the cost of <inline-formula> <tex-math notation="LaTeX">$1.37\times /1.13\times $ </tex-math></inline-formula> penalty in <inline-formula> <tex-math notation="LaTeX">${T} _{\text{WA}}/{T} _{\text{RA}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$6.23\times $ </tex-math></inline-formula> penalty in <inline-formula> <tex-math notation="LaTeX">${H} _{\text{PWR}}$ </tex-math></inline-formula> when compared to FD8T and BI11T, respectively, at <inline-formula> <tex-math notation="LaTeX">${V} _{\text{DD}} = \text{0.7}$ </tex-math></inline-formula> V.
Jamming Suppression in Downlink NOMA Using Independent Component Analysis

In this paper, we propose to use Independent Component Analysis (ICA) as an advanced pre-processing tool for blind suppression of interfering jammer signals in the downlink of non-orthogonal multiple access (NOMA) systems utilizing antenna arrays. The function of ICA is to provide a jammer-mitigated signal to the conventional detection. The proposed method takes the independence between downlink NOMA signal and jamming signal into account. This method greatly improves the capability of interference cancellation and spectrum efficiency of the NOMA downlink. Simulations are given to illustrate the achieved performance gains.
GreenCoin: Empowering smart cities using Blockchain 2.0

The work presented in this paper aims to propose a novel idea to use blockchain technology and IoT devices or solar devices that generate solar power for smart power distribution within the ecosystem for optimal and secured transactions for the demands of many smart cities. The blockchain 2.0 provides a consensus-based mechanism for programmable transactions. The paper presents a blockchain based power distribution ecosystem for smart cities by incorporating a wallet-based currency, called “Green Coin”, for power transactions of buying, selling and lending. It integrates negotiation model for monopoly avoidance and a transparent and fair system to every Power Service Provider (PSP) in the system.