Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevention of Rumor Spreading Based on Blockchain

Social media networks (SMNs) have acquired rapid development in recent years as they can provide better channel for fast information spreading. However, some false information and rumor released or propagated in SMNs have become a series social problem. Therefore, a novel rumor prevention model based on blockchain is proposed in this paper. In the model, rumor detection system and voting rule are used to judge the kind of the message released in SMNs, social media runner and some specific nodes with important influence can undertake the work of validation of information and writing to the blockchain. Once the message is judged to be rumor, it is forbidden relaying by other users. The proposed private blockchain and its consensus mechanism can guarantee the rapid processing of transactions; the relayed information from news the resources can be verified through IP address visiting. The person often releases false information can be exposed between different SMNs, this can effectively prevent the spreading of rumor. Some analyses and discussions on the strength of the proposed model are given to show the better feasibility and scalability of the proposed model.
Secure Hybrid Analog and Digital Precoding for Massive MIMO Systems

Hybrid analog and digital precoding is one of the key techniques for reducing the number of radio frequency (RF) chains in massive multiple-input multiple-output (MIMO) systems. However, the security of transmission, as well as the interference among multiple users should be both considered for such a system. In this paper, we introduce the idea of spare beams to design the analog precoder for eliminating the multi-user interference. Meanwhile, for enhancing the security, the digital precoder is designed with the aid of artificial noise (AN). Simulation results demonstrate that the proposed scheme has the advantage of achieving better sum rate in comparison to the traditional scheme, while improving the secrecy rate with the identical number of sparse RF chains.
Lightweight Verification and Fine-grained Access Control in Named Data Networking Based on Schnorr Signature and Hash Functions

Named Data Networking (NDN) is a new kind of architecture for future Internet, which is exactly satisfied with the rapidly increasing mobile requirement and information-depended applications that dominate today's Internet. However, the current verification-data accessed system is not safe enough to prevent data leakage because no strongly method to resist any device or user to access it. We bring up a lightweight verification based on hash functions and a fine-grained access control based on Schnorr Signature to address the issue seamlessly. The proposed scheme is scalable and protect data confidentiality in a NDN network.
Monte Carlo Tree Search With Reinforcement Learning for Motion Planning

Motion planning for an autonomous vehicle is most challenging for scenarios such as large, multi-lane, and unsignalized intersections in the presence of dense traffic. In such situations, the motion planner has to deal with multiple crossing-points to reach an objective in a safe, comfortable, and efficient way. In addition, motion planning challenges include real-time computation and scalability to complex scenes with many objects and different road geometries. In this work, we propose a motion planning system addressing these challenges. We enable real-time applicability of a Monte Carlo Tree Search algorithm with a deep-learning heuristic. We learn a fast evaluation function from accurate, but non real-time models. While using Deep Reinforcement Learning techniques we maintain a clear separation between making predictions and making decisions. We reduce the complexity of the search model and benchmark the proposed agent against multiple methods: rules-based, MCTS, $A^{*}$ search, deep learning, and Model Predictive Control. We show that our agent outperforms these other agents in a variety of challenging scenarios, where we benchmark safety, comfort and efficiency metrics.
Congestion Mitigation using Non-Cooperative Game Theory in LTE Base Stations

Transmission Control Protocol (TCP) is used for delivering internet data to users. TCP is prone to network congestion and its ability to combat network congestion has certain limitations, especially in the case of wireless networks. Currently, Long Term Evolution (LTE) is one of the main chosen standards for mobile and fixed wireless data communications. LTE uses various error correction techniques for reliable data delivery over the air-interface. These cause other issues such as excessive latency due to queuing in the Base Station (BS). Therefore, adequate congestion mitigation mechanisms are required. Existing mechanisms such as Discard Timer (DT) and Random Early Detection (RED) require parameter configuration and are therefore limited in their ability to mitigate congestion. In this paper, a new mechanism has been proposed of implementing congestion management using radio resource allocation techniques with Game Theory as an underlying mathematical framework.
The New Approach to IT Testing : Real Transaction-Based Automated Validation Solution

Traditional IT projects have rolled out newly developed software or systems after iterating manual tests based on the scenarios and cases that are considered sufficient. However, due to the time and budget limitation of IT projects, these traditional tests almost always fail to include all the possible scenarios and cases of the real world. Thus, we cannot eliminate all potential defects before go-live and unexpected failures might occur as a result, which can lead to severe damage to both customers and IT project contractors. This paper demonstrates a real transaction-based automated testing approach named ‘PerfecTwin’ with several real-world examples. PerfecTwin overcomes the above limitations of the traditional testing by running the new and old systems side-by-side, automatically validating the new system against the old system's actual transactions, in real time, which can eliminate almost all potential defects before go-live.
A Gm-C Notch Filter Implemented With gm Over ID Technique For Biosignal Acquisition Systems

In this document, we introduce a second order analog Notch Filter to attenuate the frequency of 60 Hz to be implemented in a CMOS integrated circuit, suitable for the acquisition of biosignals, such as electrocorticography. The filter is implemented using four single-stage transconductance operational amplifiers (OTAs). The design of OTAs is done using the gm over id technique. The proposed OTA operates in the saturation region and uses a ±0.5 V supply voltage and dissipates 21 µW of power with 32 dB open loop gain and 4 MHz GBW. The OTA has been used to implement a Gm-C bi-quad filter, which was simulated in the Cadence Virtuoso environment for TSMC 65 nm technology. The simulation results verify the attenuation value at -11 dB for the 60 Hz resonance frequency, as well as an 130 dB CMRR.
Range Association and Fusion in a Network of Single-Channel Monostatic OFDM Radars

This study introduces a framework for positioning with a network of single-channel monostatic radars with decentralized detection. In this context, a multitarget-multisensor data association procedure for estimating the number of point targets and assigned reflections from the radar stations to each supposed target is described. Next, a data fusion stage for each of these supposed targets as well as remarks on the achievable accuracy are discussed. Based on achieved results for a network of orthogonal frequency-division multiplexing (OFDM) radars, it is concluded that the introduced framework yields appropriate estimates on both number of targets and their positions.
SLA-Aware Flow Provisioning in Next-Generation Software-Defined Networks

The next-generation software-defined networks (SDNs) are designed to be extremely flexible and cost-effective. However, complying with service-level agreements (SLAs) is often challenging with the softwarization of networks. To this end, we propose different methods and techniques in this paper to solve some crucial problems related to flow provisioning with SLA guarantees. The paper is divided into two parts: The first part focuses on the SLA-driven design of SDNs. First, we propose Bitstream, a programmable SDN protocol that can provide carrier-class features. We demonstrate Bitstream on the test-bed of custom-built FPGA-based Terabit white-boxes. We show the effectiveness of Bitstream in providing low latency and 50ms restoration in case of faults. Next, we propose techniques to make Bitstream more application-centric by including delay requirements of service requests. In the second part, we shift our focus to Network Function Virtualization (NFV). One of the biggest hurdles of adopting NFV is to provide a similar performance what a service provider is accustomed to with the traditional network hardware. First, we design an end-to-end service provider network with NFV data-centers. To provide highly-available services, we create an ILP framework intending to maximize the service provider's profit. We then propose a robust optimization extension for handling traffic uncertainty. Next, we propose a service function chain (SFC) realization theory that compares the requirements of an SFC request to the available resources in NFV infrastructure while providing availability and delay guarantees for the dynamic arrival of requests. Finally, we present an NFV use-case in the virtualized slicing of 5G networks. We propose a dynamic network slicing algorithm that creates/deletes/modifies slices while optimizing underlying virtualized resources. To handle uncertainty in the traffic requests, a stochastic optimization extension is also proposed.
RECUP Net: RECUrsive Prediction Network for Surrounding Vehicle Trajectory Prediction with Future Trajectory Feedback

In order to predict the behavior of human drivers accurately, the autonomous vehicle should be able to understand the reasoning and decision process of motion generation of human drivers. However, most of the conventional prediction methods overlook this and focus on improving the prediction results using the given data, the historical information. In contrast, human drivers not only depend on the historical motion but also consider future predictions when handling interactions with other vehicles. In this paper, we propose a novel recursive RNN encoder-decoder prediction model that takes the initial future prediction results as inputs of second prediction computation. This feedback mechanism can be interpreted as a network sharing, which allows the model to refine or correct the predicted results iteratively. We use two encoders to analyze both of the historical information and future information, and the attention mechanism is employed to interpret interaction. Our experimental results with the NGSIM dataset demonstrate the recursive structure enhances prediction results effectively compare to the baselines based on the ablation study and state-of-the-art methods. Furthermore, we observe that the results improve successively as the model iterates.
HomoTR: Online Test Recommendation System Based on Homologous Code Matching

A growing number of new technologies are used in test development. Among them, automatic test generation, a promising technology to improve the efficiency of unit testing, currently performs not satisfactory in practice. Test recommendation, like code recommendation, is another feasible technology for supporting efficient unit testing and gets increasing attention. In this paper, we develop a novel system, namely HomoTR, which implements online test recommendations by measuring the homology of two methods. If the new method under test shares homology with an existing method that has test cases, HomoTR will recommend the test cases to the new method. The preliminary experiments show that HomoTR can quickly and effectively recommend test cases to help the developers improve the testing efficiency. Besides, HomoTR has been integrated into the MoocTest platform successfully, so it can also execute the recommended test cases automatically and visualize the testing results (e.g., Branch Coverage) friendly to help developers understand the process of testing. The demo video of HomoTR can be found at https://youtu.be/_227EfcUbus.
Solutions to Data Reception with Improve Blind Source Separation in Satellite Communications

In satellite network, when the data is transmitted to the nearest base station on the ground with satellite cross-links, the attack appears to come from one satellite, but it's actually from multiple satellites. Especially in non-cooperative communication, the separation of received signals is a difficult task. In this article, solve the problem with blind source separation method is feasible. The contributions in this paper is as follows, firstly, signal sparsity is completed by computing the Short Time Fourier Transform (STFT), secondly, we separate the mixed source signals by improved PSO clustering. At last, we verify the proposed method by several simulations. The experimental results demonstrate the effectiveness of the proposed method.
Coil Shape Antenna Design To Enhance The Performance Of 1 Pair Capacitive Coupling For Implant Medical Device Application

This paper introduces a method to increase capacitive coupling between transmitter antenna and receiver antenna using coil shape design and a method to reduce the traditional 2 pairs capacitive coupling to 1 pair coupling by using different equipotential zone to allow current circulation in receiver circuit. The performance of the proposed methods had shown better capacitive coupling than using sheet plate and constant coupling in distance misalignment.
Evaluation of Responsibility-Sensitive Safety (RSS) Model based on Human-in-the-loop Driving Simulation

Safety is an important challenge in the development of automated vehicles (AVs). To help with the challenge of achieving higher safety in the decision making of AVs, Intel and Mobileye have proposed a parameterized model named Responsibility-Sensitive Safety (RSS). Previous studies have demonstrated that RSS has the potential to improve the safety performance of automated vehicles. However, RSS could lead to a considerable car-following distance depending on the parameter values chosen for the model, which could reduce traffic efficiency. To improve the efficiency of RSS applied to Adaptive Cruise Control (ACC) systems, previous work proposed an efficiency-optimal (referred as “Efficiency-optimal RSS”) variation of the RSS model that involves different triggering conditions of a proper response. Therefore, in this paper a human-in-the-loop driving simulation experiment was conducted to evaluate the performance and acceptance of different safety methods. The RSS model and the efficiencyoptimal variant were embedded in an ACC system based on Model Predictive Control (MPC) algorithm. Two car-following scenarios with a sudden deceleration of lead vehicle at various time headways were simulated to evaluate the human perception and response of the different models. Results show that the original RSS model improves subjective safety judgment of human drivers. While the Efficiency-optimal RSS variant has a lower subjective safety score when compared to original RSS, it significantly reduces driver’s emergency braking reactions when compare to an ACC only system.
Biomechanical Analysis of Common Postures during Mobile Phone Use

The use of mobile phones for long periods of time has been associated with musculoskeletal and vision symptoms. This work uses simplified biomechanical models of the head, neck, back, and forearms to estimate the forces and net moments at the joints required to maintain static equilibrium during mobile phone use. We conducted a survey to identify the most common postures adopted by university students when using the mobile phone and selected two of them to perform the biomechanical analysis. The results show that postures that allow comfortable viewing angles may cause higher loading moments at the joints, which can produce musculoskeletal discomfort.
Layout Optimization of Printed Planar Coil with Variable Trace Width and Spacing

Printed planar coil in standard manufacture is commonly used in the wireless power transmission (WPT) systems. Improving coils’ quality factor is one of important ways to make WPT more efficient. Varying the trace width and turn-to-turn spacing in a new layout method is proposed to optimize the quality factor of planar coils. An analytical model comprising the parasitic resistance and capacitance is also established. The proposed method and the model are verified to be effective through simulations and experiments.
Detection of Fingerprint Authenticity Based on Deep Learning Using Image Pixel Value

Research on fingerprints has been done a lot, this is because of so many uses of fingerprints as an access tool to enter a system. This method is used to ensure the authenticity of authorized users. Fingerprints are used as biometric identification because fingerprints have a unique pattern that is different from every human fingerprint. The many uses of fingerprint biometric systems also cause many threats to the system, fingerprint forgery occurs so that it can be used to access the system illegally. Therefore this study proposes a system to be able to recognize the authenticity of a fingerprint. CNN is generally designed for object recognition of an image, making it suitable for recognizing fingerprint images to determine if a fingerprint is genuine or fake. The results of the evaluation of several experiments conducted obtained the highest accuracy value of 95.32% for determining the authenticity of fingerprints.
Novel Parallel Serpentine Coils with High Lateral Misalignment Tolerance and Power Transfer Efficiency Optimization

Inductive wireless power transfer (WPT) systems are achieved by novel parallel serpentine coils. By considering the uniform magnetic field distribution generated by parallel serpentine coil, high lateral misalignment tolerance can be achieved by little mutual inductance variance. Furthermore, a method to improve the efficiency of WPT using serpentine coils is also presented in this paper. The measured efficiency is 49% when two coils are aligned, and still maintains 39% when 35 mm lateral misalignment occurs. Comparing with the conventional spiral coil, efficiency decay rate (EDR) suffers 57% degradation at lateral misalignment of 35 mm. The proposed coil improves lateral misalignment tolerance for only 20% EDR at the same condition.
Procedural Generation of Favela Layouts on Arbitrary Terrains

The procedural generation of content for virtual worlds is a technique widely explored by the game industry since it allows a reduction in the development costs and time. Procedural city generation is a very common topic, given the growth in the size of scenarios in games and simulations. However, traditional city-generation techniques do not represent some particular variations found in urban spaces, such as informal urban settlements. This paper proposes a method for generating procedural favelas, containing its road system, division of building lots, and alleys. The road system uses the A* algorithm and is based on the heightmap, having the characteristics of favelas on uneven terrains. The method allows zigzag roads to adapt to the steep elevations, a higher number of dead ends, and large blocks, fitting several buildings, and an alley system. The generation of building lots and alley system is based on the quadtree algorithm. Each block is subdivided to fit its building lots. The buildings are positioned on their lot, leaving a free space for the alleys. The alley system connects all the buildings that are inside the city block to the road. Our purpose is to generate the layout of a favela on arbitrary terrain procedurally. The results can be used in games, animations, and different types of simulations involving this scenario, such as traffic analysis, natural disasters, and future urban growth.
LAFuzz: Neural Network for Efficient Fuzzing

Fuzzing is a well-known technique for efficiently finding software vulnerabilities. Unfortunately, due to syntax check, even the state-of-the-art fuzzers are not very efficient at discovering hard-to-trigger bugs in applications that expect highly structured inputs. Grammar-based fuzzers, while effective, often require expert knowledge and incur significant computational overhead. In this paper, we present LAFuzz, an automated fuzzer that generates high-quality seed inputs, which utilizes a variety of deep neural network model with different setup to efficiently fuzz programs that expect structured or unstructured inputs. We achieve this by combining mutation-based fuzzing and generation-based fuzzing offline. Our evaluation on 8 popular real-world applications demonstrated that LAFuzz-LSTM and LAFuzz-Attention significantly outperform AFL, a state-of-the-art fuzzer, on most cases both at discovering more crashes and achieving higher code coverage. In total, LAFuzz-LSTM and LAFuzz-Attention can effectively improve the code coverage over AFL by 7.55% and 7.67%; and both fuzzers can consistently discover 30.19% as well as 82.39% more unique crashes. Furthermore, extensive evaluation also showed that LAFuzz provides a great compatibility and expansibility.
Artificial Neural Network for Classification of Possible Cardiovascular Risk Using Indexes of Heart Rate Variability

Prevention is a key factor to avoid chronic diseases and premature death. The assessment of risk of cardiovascular disease through the Framingham score could help in taking action on time. This paper proposes the use of an Artificial Neural Network as a classifier of risk based on the indexes of Heart Rate Variability. 60 electrocardiographic records from the database of the PhysioBC® project are used to calculate time domain, frequency domain and nonlinear indexes. These parameters, in addition to age and body mass index, will be used to classify 4 levels of risk. These levels are established using the Framingham score. The proposed architecture has a training efficiency of 91.7 %, 100 % using test vectors and 95 % with validation vectors.
Speeding up GUI Testing by On-Device Test Generation

When generating GUI tests for Android apps, it typically is a separate test computer that generates interactions, which are then executed on an actual Android device. While this approach is efficient in the sense that apps and interactions execute quickly, the communication overhead between test computer and device slows down testing considerably. In this work, we present DD-2, a test generator for Android that tests other apps on the device using Android accessibility services. In our experiments, DD-2 has shown to be 3.2 times faster than its computer-device counterpart, while sharing the same source code.
Predicting Driving Conditions at Mountain Crossings Using Deep Learning

Predicting driving conditions is a complex endeavour, both in terms of defining a metric that reliably describes driving conditions as well as for quantifying and predicting anticipated future conditions. In this paper, we introduce the concept of using measured vehicle speeds as a ground truth for driving conditions at specific locations at mountain crossings, and present a prediction model that estimates future expected driving conditions using deep neural networks. Our network inputs consist of time series measurements from weather stations and vehicle speeds together with weather predictions from national forecasts. Applying multi-step hybrid Convolution and Long Short-Term Memory networks, we predict vehicle speeds six hours into the future to be used as an indicator for the expected driving conditions. The model is trained on historical data and then applied to real-time weather and vehicle data to set up a live system that can be used as a decision-making tool for road entrepreneurs to help them assess whether or not extraordinary measures, such as convoy driving or road closure, may need to be implemented.