Miracle-dz/UniFAR-FTUs huggingface dataset:
NLP-based Entity Behavior Analytics for Malware Detection

In this research, we formulate malware detection as a large-scale data-mining problem within Security Information and Event Management (SIEM) systems. We hypothesize that behavioral analysis of executable/process activities, such as file reads/writes, process creations, network connections, or registry modifications, enables the detection of advanced stealthy malware. To achieve this detection, we model processes behaviors as a set of directed acyclic graph streams and identify outliers in the set of graph streams. We enable this detection by conversion of the behavioral graph streams into documents, embedding using state-of-the-art Natural Language Processing model, and eventually performing novel outlier detection on the high dimensional vector representation of the documents. We evaluate our approach in a real-world setting, next to the SIEM system of a large-scale international enterprise (over 3TB of EDR logs). The proposed method has shown the capability to detect previously unknown threats.
Image forgery detection review

With the wide spread of digital document use in administrations, fabrication and use of forged documents have become a serious problem. This paper presents a study and classification of the most important works on image and document forgery detection. The classification is based on documents type, forgery type, detection method, validation dataset, evaluation metrics and obtained results. Most of existing forgery detection works are dealing with images and few of them analyze administrative documents and go deeper to analyze their contents.
UAV Antenna Automatic Tracking System Based on Arduino

To address the problems of short data link signal distance, poor pointing accuracy, and the real-time performance of ordinary directional antennas during the flight of small and medium-sized unmanned aerial vehicles (UAVs), an Arduino-based UAV antenna automatic tracking system is proposed in this study. An ATMEG328P main controller is used to control the overall system, GPS is employed to obtain the real-time position of a UAV, a coordinate system with the center of the earth as the origin is established to calculate the relative position of the aircraft and the ground, and virtual GPS frame data are generated via NMEAGNE for the tracking simulation. Moreover, a radio is utilized to collect and test the POS data. The experimental results show that the data link is stable, and the range-increasing effect is obvious when the system is tracking. The optimization program achieves high-precision tracking for aerial targets within 0.5 m and keeps the image signal clear up to 10 km through 100 MW data transmission. Moreover, the data link signal can be maintained above 90%.
Multi-variable modeling and system identification of an interleaved boost converter

The resonant frequency region characteristics of non-minimum phase (NMP) DC-DC converter systems in the presence of input voltage and load current disturbances are crucial for controller synthesis. However, finding the high fidelity models of an interleaved boost converter system with disturbance inputs is difficult. The main goal of this study is to propose a convenient multi-variable boost converter system modeling and identification scheme. The small-signal approach is adopted to obtain the transfer functions from disturbances to the output of a 2-level interleaved boost converter. Then, an open-loop experimental system identification technique is proposed to accurately extract frequency response curves by using pseudo-random binary sequences (PRBS) input signals. We show the efficacy of this approach both in simulation and experimentally on a 200-W converter.
Identification of Cotton Growing Stage Based on Faster-RCNN

With computer vision booming ahead on the high-way, image classification and object detection have greatly improved, which are able to accurately classify large datasets even better than human beings. There are many fields in which computer vision gets involved, including terrestrial and aerial mapping of natural resources, crop monitoring, precision agriculture, quality control and classification of processing lines, and process automation. The application of computer vision in agriculture has increased considerably in recent years. However, traditional computer vision methods require complex preprocessing and algorithms, which is time-consuming and labor-intensive. In particular, the effectiveness of this method relies mainly on the accuracy of the artificial design features and the learning algorithm. In this paper, we propose a object detection algorithm based on Faster-RCNN to identify the stage of the cotton leaf(a period that cotton leaves grow). The algorithm consists of three main parts: convolutional neural network based on ResNet101, the extraction of candidate regions of the target based on RPNs, and the identification of detection object. Based on many experiments, the algorithm in this paper is able to identify the cotton leaf stage, and the precision surpasses 80%.
Segmentation of Images using Automatic Fuzzy Clustering Framework

Clustering is grouping up of data points. Using clustering algorithms, the data points can be grouped with similar properties. Fuzzy clustering is grouping of data points of clusters of one or more clusters. Density Peak (DP) clustering can find the clusters but when the sum of clusters is increased, it suffers memory overflow, because when a normal size image is used for image segmentation which contains a greater number of pixels, it results in a high degree of similarity matrix. To avoid this, Automatic Fuzzy Clustering Framework (AFCF) for segmentation of image could be introduced. This framework contributes in three ways. To begin with, the Density Peak method is used for the concept of Super Pixel, which decrements the similarity matrix size and there by enhances DP algorithm. Secondly, the Density Balance technique generates a stable decision graph, which enables the DP algorithm for a fully autonomous clustering. Lastly, to improve image segmentation outcomes, the system which works on prior entropy employs a Fuzzy c-means clustering. Through this, information of pixels in spatial neighbors are considered and can see improved segmentation results. In the present work, an attempt is made to develop and explain the segmentation of images using Automatic Fuzzy Clustering Framework.
FIGCPS: Effective Failure-inducing Input Generation for Cyber-Physical Systems with Deep Reinforcement Learning

Cyber-Physical Systems (CPSs) are composed of computational control logic and physical processes, which intertwine with each other. CPSs are widely used in various domains of daily life, including those safety-critical systems and infrastructures, such as medical monitoring, autonomous vehicles, and water treatment systems. It is thus critical to effectively test them. However, it is not easy to obtain test cases which can fail the CPS. In this work, we propose a failure-inducing input generation approach FIGCPS, which requires no knowledge of the CPS under test or any history logs of the CPS which are usually hard to obtain. Our approach adopts deep reinforcement learning techniques to interact with the CPS under test and effectively searches for failure-inducing input guided by rewards. Our approach adaptively collects information from the CPS, which reduces the training time and is also able to explore different states. Moreover, our approach is the first attempt to generate failure-inducing input for CPSs with both continuous action space and high-dimensional discrete action space, which are common for some classes of CPSs. The evaluation results show that FIGCPS not only achieves a higher success rate than the state-of-the-art approaches but also finds two new attacks in a well-tested CPS.
Rate-Optimizing Beamsteering for Line-of-Sight Directional Radios with Random Scheduling

Fifth Generation (5G) mobile networks aim to exploit the huge amount of spectrum in the millimeter wave (mmWave) or Terahertz bands to increase communication capacity. mmWave and Terahertz radios fundamentally differ from existing wireless systems in terms of directivity, propagation loss, and blockage sensitivity. Traditional sub-6 GHz radios are not as sensitive to physical obstruction or blockage since they are equipped with omni-directional antennas; however, they cannot attain the service quality provided by systems equipped with directional antennas with high directive gains and low directional propagation loss. We explore high frequency mmWave/Terahertz antennas as part of a network, where nodes/antennas communicate with each other by steering their beams, following a random communication schedule. We systematically structure the random scheduling of nodes and design the corresponding throughput expressions. We investigate the result of optimally steering main lobe beams of these antennas, and its effect on the network capacity.
Parking Tracking System Using Raspberry Pi

Based on rapidly growing population we face a lot of difficulties in our day to day life. One such issue is when we have to search a parking space for our vehicles in parking area. Four wheeler users are very common in this era and with huge number of vehicles, we need a better system to provide a clear idea about the availability of parking space and at the have time have to ensure the safety. There are many researches which provide a solution on this problem but those researches failed to provide accurate results. In this paper, the system designed clearly enables the parking tracking facility with good accuracy and at the same it also establishes a safer parking lot system that consist of fire detection alarm and petrol leak alarm. This system consists of Raspberry Pi setup which is used to give the data about the availability of the parking space. If the car is pre book the slot, it allows the car into the parking slot allotted for it, if the car is not pre booked the slot the parking amount will be debited from the amount in the card. The details will be displayed in the LCD. If any fire accident or any fuel leakage occurs in the parking area, the alarm will be on to alert the persons around the parking area. This paper proposes All vehicle information is recorded in the database, and the relevant tags and tag ids are supplied to users so that the system may access all information.
Graphenized Papertronic Devices using Blue Laser ablated Polyimide Resin Paper

Herein, a new method for fabricating graphenized material on paper coated with a microfilm thick polyimide (PI) resin for multifunctional applications is presented. Rather than using a traditional infrared CO2 laser, an inexpensive, low-power 450 nm blue light laser was used to create highly conductive graphenized paper within minutes. The PI resin was coated on Whatman grade 1 paper prior laser ablation. The resulting Laser-Induced Graphene (LIG) material was thoroughly characterized and electrical conductivity of >2000 S/m was measured. As a proof of concept for wearable electronic applications, the generated versatile graphenized conductive substrate was harnessed for strain sensing application.
More Love, More Behavioral Intention: Understanding Elderly's Acceptance of Smartphone Applications

In recent years, smartphones have been widely available, and an increasing number of people begin to use various applications to facilitate their daily life. Meanwhile, the aging of the world population is becoming more and more serious. Therefore, it is necessary to study the acceptance of smartphone applications among the elderly. In this study, an extended TAM model was established and 655 adults aged 55 and over were surveyed with questionnaires. Finally, 496 questionnaires were used. The results showed that anxiety, facilitating conditions, social influence, and self-efficacy have a significant impact on the elderly's acceptance of smartphone applications. In addition, social influence has been proved to be a key factor that affects use intention. Hence, providing the elderly with encouragement and support from family and friends to use smartphone applications may improve their behavioral intention. The results of this study provide suggestions and directions for future interaction design.
Defeating Program Analysis Techniques via Ambiguous Translation

This research explores the possibility of a new anti-analysis technique, carefully designed to attack weaknesses of the existing program analysis approaches. It encodes a program code snippet to hide, and its decoding process is implemented by a sophisticated state machine that produces multiple outputs depending on inputs. The key idea of the proposed technique is to ambiguously decode the program code, resulting in multiple decoded code snippets that are challenging to distinguish from each other. Our approach is stealthier than previous similar approaches as its execution does not exhibit different behaviors between when it decodes correctly or incorrectly. This paper also presents analyses of weaknesses of existing techniques and discusses potential improvements. We implement and evaluate the proof of concept approach, and our preliminary results show that the proposed technique imposes various new unique challenges to the program analysis technique.
Microwave and Radar Engineering

Introduction Propagation through Waveguides Microwave Cavity Resonators Microwave Components Microwave Measurements Introduction to Microwave Tubes Microwave Tubes: Klystrons and reflex Klystrons Microwave Tubes: Helix Travelling Wave Tube and Backward Wave Oscillator (BWO) Microwave Tubes: Magnetrons Microwave Semiconductor Devices: Tunnel Diodes Varactor, Parametric Amplifiers and p-i-n Diodes Microwave Semiconductor Devices: IMPATT and TRAPATT Diodes Microwave Semiconductor Devices: Gunn Devices Principles of Radar Radar Transmitters and Devices
An Automated Pipeline for Privacy Leak Analysis of Android Applications

We propose an automated pipeline for analyzing privacy leaks in Android applications. By using a combination of dynamic and static analysis, we validate the results from each other to improve accuracy. Compare to the state-of-the-art approaches, we not only capture the network traffic for analysis, but also look into the data flows inside the application. We particularly focus on the privacy leakage caused by third-party services and high-risk permissions. The proposed automated approach will combine taint analysis, permission analysis, network traffic analysis, and dynamic function tracing during run-time to identify private information leaks. We further implement an automatic validation and complementation process to reduce false positives. A small-scale experiment has been conducted on 30 Android applications and a large-scale experiment on more than 10,000 Android applications is in progress.
Application of Hybrid Algorithm in Vehicle Routing Problem

Research vehicle routing problem.Logistics distribution requires the timely delivery and low transportation cost,and the vehicle routing is the key to solve the logistics distribution problem.The traditional optimization method has the defects of low searching efficiency,time-consuming and high cost.To improve the vehicle route optimization efficiency and reduce logistics cost,this paper proposed a hybrid algorithm for vehicle routing optimization method.Firstly,vehicle routing mathematical model was established,and then genetic algorithm was used to find a feasible solution quickly.Then the solution was converted into the initial pheromone of ant colony algorithm.Finally,ant colony algorithm was used to find the optimal solution from the feasible path.The simulation results show that,compared with other optimization methods,the proposed method can improve vehicle routing optimization and reduce the cost of logistics distribution effectively.
Finding A Needle in a Haystack: Automated Mining of Silent Vulnerability Fixes

Following the coordinated vulnerability disclosure model, a vulnerability in open source software (OSS) is sug-gested to be fixed "silently", without disclosing the fix until the vulnerability is disclosed. Yet, it is crucial for OSS users to be aware of vulnerability fixes as early as possible, as once a vulnerability fix is pushed to the source code repository, a malicious party could probe for the corresponding vulnerability to exploit it. In practice, OSS users often rely on the vulnerability disclosure information from security advisories (e.g., National Vulnerability Database) to sense vulnerability fixes. However, the time between the availability of a vulnerability fix and its disclosure can vary from days to months, and in some cases, even years. Due to manpower constraints and the lack of expert knowledge, it is infeasible for OSS users to manually analyze all code changes for vulnerability fix detection. Therefore, it is essential to identify vulnerability fixes automatically and promptly. In a first-of-its-kind study, we propose VulFixMiner, a Transformer-based approach, capable of automatically extracting semantic meaning from commit-level code changes to identify silent vulnerability fixes. We construct our model using sampled commits from 204 projects, and evaluate using the full set of commits from 52 additional projects. The evaluation results show that VulFixMiner outperforms various state-of-the-art baselines in terms of AUC (i.e., 0.81 and 0.73 on Java and Python dataset, respectively) and two effort-aware performance metrics (i.e., EffortCost, Popt). Especially, with an effort of inspecting 5% of total LOC, VulFixMiner can identify 49% of total vulnerability fixes. Additionally, with manual verification of sampled commits that were identified as vulnerability fixes, but not marked as such in our dataset, we observe that 35% (29 out of 82) of the commits are for fixing vulnerabilities, indicating VulFixMiner is also capable of identifying unreported vulnerability fixes.
A Smart Home Demand Response System based on Artificial Neural Networks Augmented with Constraint Satisfaction Heuristic

Distributing the peak load and alleviating grid stress by considering hourly electricity prices are some of the main research problems for current smart grid systems. This paper deals with the scheduling problem of home appliances' operating hours in smart grids, which aims to achieve minimum cost in user-defined operation intervals. To this end, scheduling via Artificial Neural Networks Augmented with Constraint Satisfaction Heuristic (ANN-AH) method that emulates the operation of the optimization for smart home demand response is developed. Our results show that a home demand response via ANN-AH achieves close to optimal performance with 10 times lower execution time than the optimal scheduling. These results suggest that the ANN-AH based demand response is highly successful and practical, and it is promising for future applications in micro-grid and decentralized renewable energy systems.
Improving Mutation-Based Fault Localization with Plausible-code Generating Mutation Operators

This paper proposes a new mutation operator using neural network to generate plausible code elements to improve performance of mutation-based fault localization on omission faults. Unlike the existing mutation operators, the proposed mutation operator synthesizes new code elements at a given mutation site with a neural language model. We extended MUSE to use the proposed mutation operator, and conducted a case study with 3 omission faults found in JFreeChart of Defects4J. As a result, the accuracy of MUSE with the new mutation operator increased significantly in all three faults.
Design and Realization of FPGA-based DRFM with High Instantaneous Bandwidth

Modern target simulators adopt the DRFM as the core architecture to quickly and accurately reproduce and transmit complicated radar signals. This paper introduces an FPGA-based DRFM architecture with high instantaneous bandwidth, which utilizing efficient polyphase decimating and interpolating technique with sampling rate up to 5GSPS to design the parallel digital down-conversion (DDC) and digital up-conversion (DUC) modules, realizing the digital demodulation and modulation of the baseband signals. The introduced DRFM architecture can achieve the radar target simulation by receiving the broadband signals and then transmitting them after digital processing. Moreover, integrating the simulation functions of target echo signals and interference signals in FPGA, this architecture can realize multi-target and multi-functional design of radar target simulation, facilitating to evaluate of the anti-interference performance of the radar system.
Double Compressed AMR Audio Detection Using Spectral Features With Temporal Segmentation

Double compressed (DC) AMR audio detection is an important audio forensic problem which is used to authenticate the originality of an auido recording. Majority of the existing studies use audio features extracted from the AMR encoder parameters such as linear prediction (LP) coefficients. Recently, we proposed to use the long-term average spectrum (LTAS) features for DC AMR audio detection and promising results were achieved. In this paper, we propose a novel feature extraction techniques which does not require any prior knowledge about the details of the encoding and decoding processes of the AMR codec. The proposed features are extracted from the temporal segmentation of the short-term Fourier transform (STFT) representation of the audio signal. The proposed features are then classified using deep neural network (DNN) classifier. Experimental results conducted on two different databases show that the proposed features considerably outperform the long-term average spectrum (LTAS) features. The average detection rate is improved from 92.44% to 96.48% on MDSVC dataset and from 80.95% to 83.67% on TIMIT database with the proposed features.
Evaluating Semantic Autocompletion of Business Processes with Domain Experts

Process modeling can benefit from automation using knowledge mined from collections of existing processes. One promising technique for such automation is the recommendation of the next elements to be added to the processes under construction. In this paper, we review an autocompletion engine that is based on the semantic similarity of business processes. To assess its efficiency in practical settings, we conduct a user study where domain experts are asked to rate the suggestions made by the engine for a commercial product. Their ratings are then compared to the engine’s accuracy measured by metrics from the natural language processing field. Our study shows a strong correlation between the expert ratings and some of these metrics. We confirm the usefulness of such an autocompletion engine, and enumerate potential improvements to any process autocompletion technique.