Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Attack Path Discovery Algorithm Through Compact Graph Planning

Attack path discovery plays an important role in protecting network infrastructure. Unfortunately, previous attack path discovery algorithms are difficult for applying in reality because of the high computational complexity problem. To achieve effective attack path discovery, we proposed a compact graph planning algorithm to incorporate goal states related information into attack path discovery. Our model extracts goal states related information by calculating closure of goal states, and then construct planning graph structure given the closure, after which the backward search algorithm is used to extract the attack path solution. The experiments were done on the typical enterprise network, comparing the effectiveness of attack path discovery algorithms with existing known methods. The result turns out that our proposed compact graph planning algorithm shows great improvement in discovering attack paths.
Identification System For Calculating Carbohydrates By Image Classification Methods

Computer vision based sustenance affirmation could be used to measure sugar content for diabetic patients. This paper proposes a method for modified sustenance affirmation, in perspective of the Bag of Features (BoF) appear. This paper was synchronized for the unique confirmation and upgrade of the best performing parts related with the BoF construction, and the estimation of the looking at parameters. For the arrangement and appraisal of the model system, a visual dataset with around 5000 sustenance pictures was made and dealt with into 11 classes. The proposed system forms thick adjacent features, using the Scale Invariant Feature Transformation on the hsv shading space, manufactures a visual vocabulary of 10000 visual words by using the different leveled k suggests gathering finally masterminds the sustenance pictures with a straight Support Vector Machine classifier. The structure achieved gathering precision of the demand of 78%.
Method for Detecting Nodes Influence Who Occupy Structural Holes in Temporal Network

With the study of social networks, in research for the network evolution and information transmission in social network, the research of detecting the influential nodes becoming important and meaningful. Recently, more and more researchers pay attention to the temporal networks. Specifically, we find that the key influential nodes who occupy structural holes are easier to earn benefits and control benefits in temporal networks. Based on the Burt's structural holes theory, we summarizes the existing structural holes measuring methods and provides a new improved method according to the features of temporal network with considering nodes topological, temporal path and temporal subgraph between the nodes in this paper. Furthermore, sufficient simulations are conducted on the real temporal networks, which investigate that the providing method is accurate and useful. Our research has a meaningful research for the detecting the influential nodes in temporal networks.
Boosting SIMD Benefits through a Run-time and Energy Efficient DLP Detection

Data Level Parallelism has been improving performance-energy tradeoff of current processors by coupling SIMD engines, such as Intel AVX and ARM NEON. Special libraries and compilers are used to support DLP execution on such engines. However, timing overhead on hand coding is inevitable since most software developers are not skilled to extract DLP using unfamiliar libraries. In addition, DLP detection through compiler, besides breaking software compatibility, is limited to static code analysis, which compromises performance gains. In this work, we propose a runtime DLP detection named as Dynamic SIMD Assembler, which transparently identifies vectorizable code regions to execute in the ARM NEON engine. Due to its dynamic fashion, DSA keeps software compatibility and avoids timing overhead on software developing process. Results have shown that DSA outperforms ARM NEON auto-vectorization compiler by 32% since it covers wider vectorized regions, such as Dynamic Range, Sentinel and Conditional Loops. In addition, DSA outperforms hand-vectorized code using ARM library by 26% reducing 45% of energy consumption with no penalties over software development time.
SAR Analysis of Antenna Implanted Inside Homogeneous Human Tissue Phantom

In this paper, specific absorption rate (SAR) field of planer inverted F antenna (PIFA) inside the numerical homogeneous human muscle phantom model is investigated. Two antennas; meander PIFA and wrinkle PIFA are implanted and simulated to resonate at 2.4 GHz. The size of the proposed design is 25 × 22.4 mm on 20mil thick Rogers RT/ duroid 5880 substrate. The proposed design claims ultra wide band property with more than 1.5 GHz bandwidth. The SAR value of different simulating human tissue is obtained up to 30 mm away from antenna. The maximum average SAR value of antenna inside human muscle tissue is obtained to be 0.87W/kg and 2.1W/kg at 1mm distance from antenna plane. The design conveys quite good gain inside conducting lossy tissue comes to be −21.57 dB and −22.3dB.
Decouple and Stretch: A Boost to Channel Pruning

Deep Neural Networks (DNNs) have shown superior performance on a variety of artificial intelligence problems. Reducing the resource usage of DNN is critical to adding intelligence on Internet of Things (IoT) devices. Channel pruning based network compression shows effective reduction simultaneously on storage, memory and computation without specialized software on general platforms. But limited by pruning flexibility, channel pruning methods have relatively low compression rate for a given target performance. In this paper, we demonstrate that channel pruning becomes more robust to decision errors by reducing the granularity of filters. Then we propose a Decouple and Stretch (DS) scheme to enhance channel pruning. Under this scheme, each filter in a specific layer is decoupled into two small spatial-wise filters, and the spatial-wise filters are stretched into two successive convolutional layers. Our scheme obtains up to 49% improvement on compression and 35% improvement on acceleration. To further demonstrate hardware compatibility, we deploy pruned networks on the FPGA, and the network produced by Decouple and Stretch scheme is more hardware-friendly with latency reduced by 42%.
ARMED: How Automatic Malware Modifications Can Evade Static Detection?

Modifying existing malicious software until malware scanners misclassify it as clean is an attractive technique for cybercriminals. In particular, fully automatizing the process can bring adversaries to generate faster effective threats. Recent studies suggest that injecting successful malware modifications could lead to corrupt executable files despite of detection. Therefore, we propose ARMED - Automatic Random Malware Modifications to Evade Detection - to bypass classifiers by automatizing valid malware generation based on detected threats. The goal is to understand how successful automatic perturbations can be used to avoid detection. In order to reach this goal, we take portable executable malware and add a number of small random injections to evade detection without affecting the malware structure. Our experiments proved that only six perturbations are required to create new functional malware samples exhibiting exactly the same behavior yet with up to 80% less detections based on original malware that was previously detected. We show that within a few minutes an adversary could take a previously detected malware and convert it in a clean new mutation bypassing static malware scanners.
Groundwater Quality Assessment for Irrigation Use in Vadodara District, Gujarat, India

This study was conducted to evaluate factors regulating groundwater quality in an area with agriculture as main use. Under this study twelve groundwater samples have been collected from Padra taluka, Dabhoi taluka and Savli taluka of Vadodara district. Groundwater samples were chemically analyzed for major physicochemical parameter in order to understand the different geochemical processes affecting the groundwater quality. The analytical results shows higher concentration of total dissolved solids (16.67%), electrical conductivity (25%) and magnesium (8.33%) for pre monsoon and total dissolved solids (16.67%), electrical conductivity (33.3%) and magnesium (8.33%) for post monsoon which indicates signs of deterioration as per WHO and BIS standards. On the other hand, 50% groundwater sample is unsuitable for irrigation purposes based on irrigation quality parameters. The study revealed that application of fertilizer for agricultural contributing the higher concentration of ions in aquifer of Vadodara district. Keywords—Groundwater pollution, agricultural activity, irrigation water quality, sodium adsorption ratio (SAR).
Propagation Pattern as a Telltale Sign of Fake News on Social Media

This exploratory study examines how fake news differs from real news in terms of propagation pattern on social media. It analyzed 40 news (20 fake news + 20 real news) surrounding the 2016 US presidential election that emerged on Twitter. Three major observations were made. First, fake news appeared to be posted more than real news. Second, with time, the growing volume of tweets for fake news indicated its sustainable propagation to achieve a wider reach. Third, the tweet volume of real news dropped drastically after the first day. However, it was not true for fake news. The observations are discussed in light of the related literature.
Impact of Plug-in Electric Vehicles Load Planning to Load Factor and Total Generation Cost in a Power System

This paper presents impact of plug-in electric vehicles load planning to load factor and total generation cost of the power system. PEVs load is investigated with information of PEV specification based on manufacturer’s datasheet, and user behavior data obtained from National Household Travel Survey 2009. Three scenarios of PEVs load implementing in the power system are compared without PEVs load, with two different numbers of PEVs at 50,000 and 100,000 vehicles. These scenarios include the case of PEVs charging without load scheduling, and case of load factor improvement by defining of charging and discharging criteria of PEVs’ battery considering load profile of the power system. The economic load dispatch is used to calculate the total generation cost of the power system, solved by Bee Algorithm. Results compared between each case studies show that plug-in electric vehicles have the impact to both load factor and total generation cost of the power system in both positive and negative way. The effect could be more prominent with higher numbers of plug-in electric vehicles connected to the system.
Enterprise Class Deep Neural Network Architecture for recognizing objects and faces for surveillance systems

Building security systems is improving at a mammoth rate since the past decade, to cope with the threats of unauthorized access and fraudulent intentions. In high security public places like airports, embassies, corporate offices etc. only facial recognition for verification does not suffice full proof security. To predict the intention of an individual we must capture the objects around the subject/individual and his interactions with them in real time environment with good accuracy besides recognizing them. In this paper we designed an Enterprise Class Deep Neural Network (EcDNN) architecture built on the base architecture of YOLO network. Our proposed multitask learning network architecture recognizes the faces of registered individual as well as objects in the person’s vicinity at one shot which achieves significant improvement in performance in terms of speed and model size without loss of precision, if it would have done separately in a cascaded model architecture. Our proposed single network architecture employing multitask learning is achieving state of the art recognition accuracy of 79 mAP at 40 fps with 33 % reduction in model size and an approximately 4x speedup with respect to the benchmark state of the art architectures, validated on standard dataset of PASCAL VOC 2012, FDDB and custom office dataset.
Autonomous Machine Learning Modeling using a Task Ontology

Recently, many researchers are intensely engaged in investigation on the artificial intelligence technology that recognizes, learns, inferences, and acts on external information in a wide range of fields by combining technologies of computing, big data and machine learning algorithms. The artificial intelligence technology is currently used in almost all industries, and many machine learning experts are working on integrating and standardizing various machine learning tools so that non-experts can easily apply them to their domain. The researchers are also studying an autonomous machine learning as well as ontology construction for standardizing the machine learning concepts. In this paper, we classify typical problem solving steps for autonomous machine learning as tasks, and present a problem solving process. We propose the modeling method of an autonomous machine learning using a processes of the task execution on machine learning such as workflow. The proposed task ontology-based machine learning model defines a task-based process grouping scheme of UML activities. And it will automatically generate and extend the machine learning models by transformation rules based on common elements and structures (relationships and processes between elements).
Power Optimization using Spectrum Sharing for 5G Wireless Networks

The rapid increase in the number of subscribers demanding high data rate applications have resulted in the need to shift from the 4G to the 5G networks. The next generation (5G) wireless communication networks (WCN’s) are required to fulfill these rising requirements, hence aiming to utilize the available spectrum as efficiently as possible. Also this is leading to a detrimental effect on the ecological balance of the environment as the transmit power levels increase correspondingly in the atmosphere. Hence power optimization has also become a major concern. Various technologies such as massive MIMO, spectrum sharing, device to device communication (D2D), GREEN communication have gained significant attention in aiding spectrum utilization along with power optimization. This proposal intends to optimize power using spectrum sharing for the NGN’s to achieve high spectrum and energy efficiency for both the primary and secondary system without introduction of a secondary transmitter. The performance of the proposed model has been compared with the opportunistic spectrum sharing model and other popular resource allocation algorithms. The results obtained confirm the efficiency of the proposed scheme for increased performance of the system.
Multi Area Economic Dispatch Using Distributed Intelligence

This paper presents an intelligent, agent-based economic dispatch for a multi-area power system, where intelligence is distributed over the grid. In this paper, a multi-agent system utilizes the consensus + innovation method which eliminates the need for a master controller and reduces the computational effort done by an agent. This will allow shifting of decision making to a lower hierarchical level, taking advantage of the ability of distributed agents to self-serve and cooperate towards an optimal solution. Simulation results are presented in this document for multi-areas initially operating in isolated mode as well as sequentially connected mode, making larger power network, showing scalability of proposed method for complex power networks where structure and topology is always subject to change.
Analysis of User Acceptance Factors for Mobile Apps Browser Using Unified Theory of Acceptance and Use of Technology (UTAUT) and Task Technology Fit (TTF) on Generation Y

UC Browser as a service provider of mobile browser has market share of 28.51% in Indoensian market that drove it into the most popular mobile browser. Many people have done review of UC Browser and they have personal argument based on the methodology. The data on google play describe that user complained about the problems occured in UC Browser. This impacts on the decrease of UC Browser monthly page view in 2016. Therefore, it is necessary to research on the user acceptance factor that affect the usage of UC Browser. This research combine Task Technology Fit (TTF) and Unified Theory of Acceptance and Use of Technology (UTAUT) with construct Attitude. Data collection was done using online questionnaires. For generation Y task characteristics, technology characteristic, task-technoogy fit, task comfortance, performance expectation, effort expectation, social influence, and usage intention have an effect on UC Browser usage. For Y generation, performance expectation, facilitating condition, and user attitude of UC Browser are not the things that affect UC Browser usage. Based on the test results, the acceptance of UC Browser of Y generation using the model TTF and development UTAUT with Attitude construct showed that the model is able to explain 54.9% of the variance in the formation of UC Browser usage behavior.
Modelling and Charactrization of the MEMS based filters for the spectroscopic imaging applications

In this work we present the theoretical optical modelling of full width at half maximum and free spectral range of the microelectromechanical systems (MEMS) based based Fabry Perot filters. These filters use silicon and silicon oxide based distributed Bragg reflectors. We also show the impact of variation of silicon thin film properties and structural deformation on the performance of filters. Finally we present fabrication and optical characterization of the fixed cavity SWIR wavelength range FP filters. The fabricated filters showed 39 nm FWHM and above 80% transmittance.
Robust Facial Recognition Using Extended Local Binary Patterns

This article presents an automatic face recognition approach that uses a new descriptor based on local binary models, the new descriptor is an extension of the LBP texture descriptor.In this work, we show how to improve the performance of facial recognition by choosing a solution inspired by Honeycombs, while exploiting the LBP descriptor that will be applied to the hexagonal representation of the cells of a hive. This exploitation allows to give rise to a new descriptor called Honeycombs-Local Binary Pattern (Ho-LBP). We assessed the effectiveness of the proposed approach on ORL, Extended Yale B and FERET datasetand found that our approach significantly outperformed other well-known methods in some cases. Experimental results show that our approach is robust in solving the problem of poses variation and luminance change for face recognition.
FacGraph: Frequent Anomaly Correlation Graph Mining for Root Cause Diagnose in Micro-Service Architecture

Micro-service architecture is a promising paradigm to develop, deploy and maintain applications using independent and autonomous cloud services. Nowadays, increasingly applications are embracing this model. However, it is difficult and time-consuming to diagnose and identify the actual root cause when anomalies occurs in micro-service architecture due to various factors. This paper introduces a novel framework for anomaly investigation and root cause identification in micro-service architecture. The novelty in our work lies on: (1) Different from existing solutions, in our framework, we propose a frequent pattern mining algorithm on anomaly correlation graph, named FacGraph, to discover root cause services. (2) We leverage breadth first ordered string (BFOS) to reduce the time-consumption of the frequent graph mining (FSM) (3) We further develop a distributed version of FacGraph to improve its paralleled computing efficiency. We evaluate our framework in real production environment IBM Bluemix. Result demonstrate that FacGraph outperforms other methods in diagnosis accuracy and offers a fast identification of root cause service when an anomaly occurs.
Both metabolic and hormonal imbalances contribute to the pathogenesis of diabetic vascular diseases , and loss of ' Winner of the Hirosi Kuriyama Avvard 2005

Macroand microvascular disease states currently represent the principal causes of morbidity and mortality in patients with type I or type II diabetes mellitus. Abnormal vasomotor responses and impaired endothelium-dependent vasodilation have been demonstrated in various beds in different anirnal models of diabetes and in humans with type I or type II diabetes. Several mechanisms leading to endothelial dysfunction have been reported, including changes in substrate avai1 ability, impaired release of NO, and increased destruction ef NO. The principal mediators of diabetes-associated en othelial dysfunction are (a) increases in oxidized low density lipoprotein, endothelin-1, angiotensin II, oxidative stress, and (b) decreases in the actions of insulin or growih factors in endothelial cells. An accurnulating body of evidence indicates that abnormal regulation of the phosphatidylinositol 3-kinase (PI3-K)IAkt pathway may be one of several factors contributing to vascular dysfunction in diabetes. The PI3-K pathway, which activates serinelthreonine protein kinase Akt, enhances NO synthase phosphorylation and NO production. Several studies suggest that in diabetes the relative ineffectiveness of insulin a d the hyperglycemia act together to reduce activity in the insulin-receptor substrates aRS)IPI3-KIAkt pathway, resulting in impairments of both IRSIPI3-KIAkt-mediated endothelial function and NO production. This article summarizes the PI3-KIAkt pathway-mediated contraction and relaxation responses induced by various agents in the blood vessels of diabetic anirnals.
Influences on the adoption of mobile learning in saudi women teachers in higher education

Although mobile devices are ubiquitous on university campuses, teacher-readiness for mobile learning has yet to be fully explored in the non-western nations. This study shows that two main factors affect the adoption and use of m-learning among female teachers within a university in Saudi Arabia-resistance to change and perceived social culture. These determinants of the current use and intention to use of m-learning were revealed through the analysis of an online questionnaire completed by 165 female faculty members. This study reveals several important issues for m-learning research and practice. The results further extend the body of knowledge in the field of m-learning, with the findings revealing that resistance to change and perceived social culture are significant determinants of the current use of and the intention to use m-learning.
Ultra Low-Power Sensor Networks for Next Generation Wildlife Monitoring

We discuss challenges and solutions to enable ultra low-power communication systems. In the field of sensor networks, this question has been investigated for multiple decades now. However, only the combination of technological and algorithmic methods eventually helps designing next generation systems. Using wildlife monitoring as an application scenario, in particular monitoring the social behavior of bats, we explore novel solutions for low-power communications given the high reliability requirements and weight restrictions. We go stepwise through a series of hardware, signal processing, and coding solutions that we developed, in order to illustrate our advancements. Our concepts have been evaluated both in large-scale simulation as well as in first field experiments.