Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Cache Replacement Scheme against Cache Pollution Attack in Content-Centric Networks

In-network cache is a key service of content-centric networks. As an instance of content-centric networks, Named Data Networking (NDN) uses the traditional Least Recently Used (LRU) and Least Frequently Used (LFU) cache replacement strategy, which will cause large storage redundancy. Besides, as the content cache pollution attack may degrade the performance of the cache mechanisms and increase the content retrieval latency, a robust cache mechanism is especially required. To solve these problems, in this paper we propose a new intra-network cache replacement strategy, which integrates evaluating the popularity of caching contents and the betweenness of the nodes in the network topology. This strategy realizes the best performance of cache replacement in the network by weighing the importance of each node and the cache content popularity within the domain. Finally, the simulation results show that the new cache replacement strategy proposed in this paper can achieve a higher cache hit rate and less average hops than the LRU and the LFU cache replacement strategy in the NDN network.
A Comparison Analysis of Fault Detection Algorithms in Wireless Sensor Networks

Sensors are capable of performing different tasks such as receiving various environmental information and have generated the idea of creating and expanding the traditional networks to Wireless Sensor Networks (WSN). A WSN include various sensor nodes which are distributed in the environment to gather data. The sensor node’s location is not necessarily defined and not specified. Such a feature allows us to release them in hazardous or inaccessible places. On the other hand, this means that the WSN protocols and algorithms must have the ability to self-deny. Other unique features of WSNs are the ability of sensor nodes to collaborate and coordinate with each other. A processor is assigned to each sensor node which initially performs a series of acquired simple information processing to transmits the semi-processed data instead of sending all raw data to the center, Base Station (BS) or the sink node. Fault detections in WSN are of particular importance due to unique technical and conceptual challenges. In this paper, due to the specific limitations and operating conditions of wireless sensor networks, different fault detection algorithms were examined. The best way to improve the ability of fault detection is to reduce energy consumption in sensor nodes. In such techniques, using the residue number system in the wireless sensor network structure improves repair and fault detection. Moreover, modules are attached whenever we require distinguishing and correcting the information security in the number system. Although there are various algorithms yet the Residue Number System algorithm has higher performance.
In-Depth Analysis of the TRL Calibration Procedure for De-Embedding in Numerical Simulations

A general method for de-embedding in numeric simulations is presented. The method uses the well-known TRL calibration technique adapted from the high-frequency measurement realm. For this purpose, models of the calibration standards were designed in a numerical simulation program using the example of a coaxial microstrip transition and the results were then evaluated with a numerical calculation program. A short introduction to the principles of the TRL calibration and its adaption to simulations is followed by a presentation of the simulation setup and the results.
Effects of complexity in perception: From construction to reconstruction

Güc ̧lütürk, Y. , and Güc ̧lü, U. (2018). Brain reading and writing with deep learning: future of neurotechnology. In NVIDIA GPU Technology Conference Europe. † Le, L. , Güçlütürk, Y. , van Gerven, M. , and van Wezel, R. (2018). Biological motion for bionic vision. In Dutch Neuroscience Meeting. ‡ Güc ̧lü, U. , Güc ̧lütürk, Y. , Ambrogioni, L. , Maris, E. , van Lier, R. , and van Gerven, M. (2017). Ultrafast HRF and pRF estimation with deep learning. In De Nederlandse Vereniging voor Psychonomie. ‡ Güc ̧lütürk, Y. Perception, complexity and creativity: from old masters to learning machines. (2016). In NIP Leergang Leven is Leren, Nederlands Instituut van Psychologen. † Güc ̧lütürk, Y. , Güc ̧lü, U. , Jacobs, R. , van Gerven, M. , and van Lier, R. (2016). Neural correlates of liking and perceived complexity of music. In International Association of Empirical Aesthetics. ‡ Güc ̧lütürk, Y. , Jacobs, R. , and van Lier, R. (2015). Liking versus complexity: decomposing the inverted U-curve by accounting for individual differences. In De Nederlandse Vereniging voor Psychonomie. ‡ Güc ̧lütürk, Y. , Jacobs, R. , and van Lier, R. (2015). Differential effects of task anticipation on liking of familiar surfaces. In European Conference on Visual Perception. ‡ Jacobs, R. , Güc ̧lütürk, Y. , and van Lier, R. (2015). What you need is what you like: knowing target and distractor categories is sufficient for distractor devaluation. In European Conference on Visual Perception. ‡ Güc ̧lütürk, Y. , Jacobs, R. , and van Lier, R. (2014). Perceptual complexity and liking of fractal-like statistical geometric patterns. In European Conference on Visual Perception. ‡ Jacobs, R. , Güc ̧lütürk, Y. , and van Lier, R. (2014). Devaluation of distractors limited to evaluations after incorrect target localization. In European Conference on Visual
Analysis on Retention Time and Adaptive Refresh in Embedded DRAMs with Aging Benefits

Embedded DRAMs (eDRAMs) are a promising solution to replace SRAMs for on-chip memories in low-power applications. Gain cells-based eDRAMs, because of their compatibility with standard CMOS process, offer a viable solution to high density storage required by modern SoCs. However, they are usually characterized by a short retention time, which increases their power consumption due to the need of frequent refresh. In this paper, we first analyze the beneficial effects of BTI aging for leakage reduction in eDRAMs and consequent retention time increase. By means of SPICE simulations, we show that, after only a month of operation, retention time increases between 7.2% and 57.9%, depending on cell structure. Retention time increase may exceeds 150% in less than 5 years of operation. Finally, we show how to capitalize on this beneficial effect by adopting an adaptive refresh rate, leading to a significant refresh power reduction over time that, for the considered eDRAM cells, ranges between 10% and 51% in 10 years of operation.
An SDNFV-Based DDoS Defense Technology for Smart Cities

A software defined networking (SDN)-enabled smart city is a new paradigm that can effectively improve the cost efficiency and flexibility of data management through data-control separation. However, it faces significant security threats such as distributed denial of service (DDoS) attacks which jeopardize the security and availability of data and services by overloading the system with excessive traffic from distributed sources. To improve the DDoS defense capability and enhance the security of data management in SDN-enabled smart cities, this paper proposes a DDoS attack Defense strategy based on Traffic Classification (DDTC). We use software defined network function virtualization (SDNFV) architecture and traffic classification strategy, to improve the flexibility and reduce the load of SDN against DDoS attacks. Experimental results show that the proposed DDTC can not only launch DDoS attacks detection quickly, but also accurately track the sources of DDoS attacks. More importantly, it can reduce the risk of attack on the controller of SDN and improve the effectiveness of the system.
Effective Defect Screening of Integrated Large HV LDMOS Driver in Critical Automotive Application towards “Zero Defects”

There are many inline defects associated with full processing such as gate oxide defect, crystal defect and etc. In this work, a dedicated structure was developed by using a large driver HV LDMOS device to assess the risk of inline defects. We have provided 3 different case studies on the development of defect screening methods which are gate oxide integrity in HV MOS, front-end defects and Metal 1 to Metal 1 spacing.
A New Hybrid Approach for C&C Channel Detection

A great many of botnet detection researches focus on recognizing and blocking its significant C&C channel. And they typically require a certain number of C&C training instances to build a behavior detection model. However, when lacking the C&C training instances for new or even unknown botnets, these methods may become inefficient or even invalid. To overcome it, we propose a new hybrid approach for network based C&C channel detection. It neither needs us to prepare the C&C training instances, nor requires deploying malicious activities monitors. It utilizes two heuristic rules to filter the non C&C traffic disobeying common C&C characteristics, and then makes the final C&C detection through a behavior based anomaly detecting model, which only requires normal traffic for training. Our approach achieved the average C&C F-measure of above 0.9 for most evaluation datasets. Moreover, the comparison result not only demonstrates our approach has significant performance advantages than the pure heuristic rule based methods, but also shows that our behavior model can profile network traffic in detail, mine more useful behavior differences than the anomaly models using traditional statistical features, and then achieve a better detection result.
Adaptive Consistency Protocol Based on Grouping Multi-Paxos

Wide-area distributed storage system relies on the data replication to achieve the reliability. One of the main challenges brought by data replication is the consistency. As the variety of applications increases, the level of data consistency of the system has evolved from a single level of consistency, such as traditional strong consistency and eventual consistency, to the best level of consistency by adaptively selecting based on the application. However, the existing adaptive consistency schemes are mostly modified based upon the traditional eventual consistency protocol such as Multi-Paxos and rarely consider the diversity of the Data Centers (DCs), which results in high latency and low throughput with limited bandwidth. In this paper, we propose Grouping Multi-Paxos (GM-Paxos), an adaptive consistency protocol based on Multi-Paxos to alleviate the limitation of bandwidth in the system. Taking the characteristics of the DCs and the system environment into consideration, we design a grouping algorithm to find the primary group and only send the request content to the DCs within the primary group at first, in order to save the bandwidth consumption. Experiments show that GM-Paxos can significantly reduce the latency and improve the throughput. Besides, we synchronize the DCs which have high real-time data requirements at first to ensure the reliability of the system.
Improving Accessibility for Mobility Impaired People in Smart City using Crowdsourcing

The concept of smart cities is gaining interest in the field of urban development. Social inclusion and equality for all citizens are major challenges for smart city architecture. By improving access to the community so the quality of life of mobility impaired people can be improved. Several types of technologies have been proposed to gather necessary information about disability access facilities within a city and provide better accessibility solutions. This paper presents a systematic literature review of some of the technological components which are the building blocks of smart accessibility and a comparison of existing technologies and case studies. A smart city-based system architecture is proposed based on the modification of existing approaches to centralize the accessibility of information and improve the ease of mobility for people with movement disabilities. The proposed architecture gathers and stores disability-related geographical point of interests, provides customized routing services using open source solutions for wheelchair access, and makes the data available for open access.
Optimization of Joint Load Balancing and Bandwidth Allocation in Space Information Networks

Space information network (SIN) has been investigated by many researchers and organizations. The resource allocation problem has been discussed for many times to improve SIN performance. However, the bandwidth allocation problem and load balancing problem are always solved respectively, resulting at sub-optimal solutions. In fact, these two problems are deeply related and the sub-optimal results are not optimal for SIN. Thus, we formulate the joint load balancing and bandwidth allocation problem to get global optimal solution and improve efficiency and performance for the whole system. Based on theoretical analysis, we calculate Hessian matrix of the objective function to prove that the optimization problem is convex. Since this problem is nonlinear and the optimal variables (load and bandwidth) are coupled, there is no closed form expression for the solution. Therefore we propose joint load balancing and bandwidth allocation algorithm to get the numerical result. In addition, we prove that the computational complexity of the algorithm is proportional to the number of nodes. Simulations are made, verifying the efficiency of the proposed algorithm.
Bayesian models for early cross-layer reliability analysis and design space exploration

Designing soft-errors resilient systems is a complex engineering task, which nowadays follows a cross-layer approach. It requires a careful planning for different fault-tolerance mechanisms at different system’s layers: starting from the technology up to the software domain. While these design decisions have a positive effect on the reliability of the system, they usually have a detrimental effect on its size, power consumption, performance and cost. Design space exploration for cross-layer reliability is therefore a multi-objective search problem in which reliability must be traded-off with other design dimensions.Assessing the reliability of a complex system and performing design space exploration in the early phases of the design cycle is a complex task and designers are increasing looking at stochastic models able to provide fast results to quickly drive early design decisions. This paper summarizes some of the results achieved by the authors in more than five years of research in this domain.
Optoelectronic Characteristics of Tungsten Disulphide Based Visible Range Photodetector

In this work, tungsten disulphide (WS2) based heterojunction photodetector device is fabricated on top of silicon (Si) substrate by using simple drop casting method. The device is characterised for its optoelectronic properties in the visible range of light illumination at 465 nm. The current-voltage (I-V) measurement is performed to attain I-V curves of various laser powers (282.6 µW, 589.2 µW, 889.9 µW, 1.07 mW and 1.265 mW). The photocurrent is found to be highly dependent on the laser power. Time based responses are calculated at 3.0 V DC bias voltage for various modulation frequencies (1 Hz, 50 Hz, 100 Hz, 500 Hz, 1 kHz, 3 kHz and 5 kHz). The fabricated device has high responsivity of 66.85 mA/W for an incident laser power of 1.265 mW and detectivity of 1.1763 × 109 Jones for an incident laser power of 282.6 µW. Raman shifts are observed at 351.38 cm−1 and 420.54 cm−1, confirming the successful growth of WS2 and verification of a non-stoichiometric WS2 layer by the energy-dispersive X-ray (EDX) spectroscopy. These results are very promising towards further development for optoelectronic applications.
Research and Implementation of a Secure Anonymous Communication System

The peer to peer network based on distributed hash table could locate the nodes effectively, but it cannot solve the node-failure problem. By introducing the concept of trust into anonymous communication system, this paper proposes a secure query method, in which, the nodes’ trust and the mutual distance between the nodes can be used to select high reliable nodes to construct anonymous communication path. The novel method could alleviate the node-failure problem, and further to improve the message forwarding efficiency.
The Primary Study for the Integration of Wax-Based Microfluidics on Textile Product

This study proposed a novel method for the integration of microfluidic devices on the textile product for the potential applications in wearable sensing and monitoring of human health status. The proposed wearable microfluidic devices enables the controlled fluid flow inside the fiber structure with the capillary force in micro-scale. The structure of microfluidic devices were printed on the surface of a textile product using a commercial wax printer, after thermal treatment, the wax meltdown and permeate through the whole thickness of the textile fiber structure. The hydrophilic textile fiber with hydrophobic wax defined barrier formed a wearable microfluidic device integrated on the textile product. With the proposed protocol, the microfluidics devices could be easily integrated with textile product with a board adaptability with various types of fibers. This primary study for the integration protocols of microfluidics with textile product may have wide potential applications in wearable technology for continues health monitoring and treatment applications.
A Data-Driven Iterative Optimization Compensation Method Based on PJIT-PLS for Gold Cyanidation Leaching Process

Gold cyanidation leaching process (GCLP) as the central unit operation in hydrometallurgy, which suffers from the problem of the optimal setting point based-model is difficult to reach the optimal working point in actual GCLP due to model error, which leads to lower economic benefit. Meanwhile, the process data contains noise and uncertainty on account of the fluctuation of raw material properties. Therefore, how to take the most of that data to make the production process run in the optimal state of economic benefit under the premise that the quality index meets the production requirements is an urgent problem to be solved. In this paper, a data-driven iterative optimization compensation strategy is proposed to solve aforementioned problems. Firstly, probabilistic principle component analysis (PPCA) method is used to preprocess process data for eliminating the effect of noise and uncertainty; Secondly, two relevant models are established between the operating variable increment and the economic benefit increment, the quality index increment based on just in time (JIT) and partial least squares (PLS) method; Finally, the optimal operating variable increment that maximizes the economic benefit increment can be optimized under the condition of quality index satisfies the production requirements and iterated at the new working point, which is constantly close to the optimal working point to improve the economic benefit. Simulation studies have verified the validity of proposed method.
A New Power Load Forecasting Model (SIndRNN): Independently Recurrent Neural Network Based on Softmax Kernel Function

In the development of the smart grid, the accuracy of power load forecasting plays a crucial role. The characteristics of the time series data of power load are affected by different regions. This paper presents a new model, independently Recurrent Neural Network based on softmax kernel function (SIndRNN), for power load forecasting. On the basis of independently RNN (IndRNN), Softmax was introduced to improve the model stability. The SIndRNN model can eliminate some useless information with low probability in the longer time step by adjusting the weight based on probability, so as to ensure the model stability. Meanwhile, it has the characteristics of fast convergence rate, longer time step information processing and effective multi-layer superposition. The experimental results obtained from real grid load data sets show that the SIndRNN model can provide more accurate prediction results than the LSTM model. Moreover, it has the ability to capture mutation information and has better stability than IndRNN model.
On Predicting Taxi Movements Modes in Porto City Using Classification and Periodic Pattern Mining

Trajectory data is produced on a large scale by various types of technologies like the internet of things (IoT) and global positioning system (GPS). When collected data is processed, analyzed, and visualized, it can be transformed into recommendations. This study builds a recommender system by applying the density clustering degree of membership (DOM) measurement. This system contains three phases, in the first phase, we clean, preprocess, and divide the data into samples. The second phase uses the hierarchical density-based clustering (HDBSCAN) to calculate DOM measurement and extracts the class attribute (traffic status). The last phase uses the extracted class attribute and applies the Naïve Bayes and Random Forest classifiers to predict the traffic flow (status) of any given trip. We compare the two classifiers in terms of accuracy and F1-score. For most datasets, the Random Forest outperforms Naïve Bayes in terms of accuracy. Moreover, the Random Forest F1-score was higher than Naïve Bayes for all datasets. This recommender system can be embedded with the recent traffic monitoring systems to provide helpful guidelines for predicting traffic status in origin, destination, and trips routes.
A Unified Malicious Documents Detection Model Based on Two Layers of Abstraction

Due to the ever increasing attacks using malicious documents, the detection of such documents has become a serious and urgent research issue. In the past decade, detection of malicious documents has attracted significant research attentions, and many methods have been proposed, including conventional static detection methods, and dynamic detection methods. However, both of the two categories of methods have limitations under either obfuscated or run-time conditions, and can not achieve a satisfactory detection performance for malicious behaviors. In this work, we firstly present a new descriptive structure of our targeted documents, using a two layers abstraction including the structure and the scripting language. We then propose a unified model for malicious documents detection based on two layers of abstraction. A series of experiments under a real world data set with 20,000 samples show that, our proposed model has a better detection performance for all of the four indicators, the accuracy, precision, recall, and AUC of malicious documents simultaneously, when compared to the Hidost model.
Q-enhanced Lithium Niobate SH0 Resonators with Optimized Acoustic Boundaries

In this work, a quality factor (Q) enhancement technique has been investigated for the fundamental shear-horizontal (SH0) mode lithium niobate resonators. Four supporting tether designs are employed in combination with an optimized release distance to enhance the acoustic isolation and consequently attain a higher Q without introducing spurious modes. The fabricated prototype resonator with tapered tether design at 90 MHz exhibits a Q of 2,600 and an electromechanical coupling ($k_{ t}^{ 2}$) of 17%, resulting in a high figure-of-merit (FoM$= k_{ t}^{ 2} \times Q$) of 442. The Q of 2,600 underlines a 2X Q enhancement from a conventional straight supporting design. The perfectly matched layer (PML) method is adopted to optimize the energy loss with different acoustic boundaries. Results from our comprehensive finite-element analyses (FEA) are also reported to rationalize our design choices and confirm a Q estimation error within 15% of the measured values.