Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Role-Based Access Control Model and Framework Using Semantic Business Roles in Multi-Domain Environments

Today’s rapidly developing communication technologies and dynamic collaborative business models made the security of data and resources more crucial than ever especially in multi-domain environments like Cloud and Cyber-Physical Systems (CPS). It enforced the research community to develop enhanced access control techniques and models for resources across multi-domain distributed environments so that the security requirements of all participating organizations can be fulfilled through considering dynamicity of changing environments and versatility of access control policies. The popularity of Role-Based Access Control (RBAC) model is irrefutable because of low administrative overhead and large-scale implementation in business organizations. However, it does not incorporate the dynamically changing policies and lacks semantically meaningful business roles which could have a diverse impact upon access decisions in multi-domain business environments. This paper describes our proposed novel access control framework that uses semantic business roles and intelligent agents through implementation of our Intelligent RBAC (I-RBAC) model. It encompasses occupational entitlements as roles for multiple domains. We use the dataset of original occupational roles provided by Standard Occupational Classification (SOC), USA. The novelty of the paper lies in developing a core I-RBAC ontology using real-world semantic business roles and intelligent agent technologies together for achieving required level of access control in highly dynamic multi-domain environment. The intelligent agents use WordNet and bidirectional LSTM deep neural network for automated population of organizational ontology from unstructured text policies. This dynamically learned organizational ontology is further matched with our core I-RBAC ontology in order to extract unified semantic business roles. The proposed I-RBAC model is mathematically described and the overall I-RBAC framework and its implementation architecture is explained. At the end, the I-RBAC model is validated through the implementation results that show a linear runtime trend of the model in presence of a large number of permission assignments and multiple queries.
Fast and Efficient Image Retrieval via Fully-Convolutional Hashing Network

With the rapid development of information technology, content-based image retrieval and related technologies have become increasingly important. The hash method can represent an image with a sequence of binary codes consisting of 0 and 1, while the application of a convolutional neural networks can learn directly from the image to a discriminative binary code. We propose a deep hash algorithm based on full convolutional neural network, which can reduce the number of network parameters and have the retrieval performance not lower than the cutting-edge method in this field. The proposed network structure uses convolutional layers and the global average pooling layer to replace fully-connected layers in current deep hashing network structures, which significantly reduces the complexity of the network and improved its training performance. This method is called Fully-convolutional hashing networks (FCHN), and experiments were carried out on several publicized datasets to verify the effectiveness of the method.
Open-Source Big Data Analytics Architecture for Businesses

Unaware of existing big data technologies, organizations fail to develop a big data capability despite its disruptive impact on today’s competitive business environment. To determine the shortcomings and strengths of developing a big data architecture with open-source tools from technical and managerial perspectives, this study (1) systematically reviews the available open-source big data technologies to present a comprehensive picture, and (2) proposes an open-source architecture for businesses to take as a reference while developing big data analytics capabilities. Lastly, we discuss technical, domain-specific, and firm-specific soft challenges related to establishing a big data architecture in an organization, and how these challenges are reshaping the big data research domain.
Research and application of security video labeling platform

With the rapid development of science and technology, the intelligent security technology has developed rapidly. As far as the deep learning method used in the field of intelligent security is concerned, image marking is a heavy and complicated work. The current security video is usually manually completed, which increases the operation and maintenance cost of security video system. For this reason, this paper proposes a semi-automatic annotation method based on neighborhood rough set for the complexity of distorted image annotation. The division of theoretical domain and the definition of correlation degree are redone. The aim is to start with the manual annotation of the data source, improve the quality of the annotation, and solve the difficulty of the annotation caused by the standard deviation of the subjective evaluation of the annotator and the blurring of the boundary between the annotated words. The application example shows that the security video labeling platform can realize semiautomatic video labeling and significantly improve the accuracy rate of security video labeling.
Research on Text Classification for Identifying Fake News

In the big data environment, massive news can always lead people to make their own judgments about events happening in society. The wrong guidance of fake news will lead to a negative effect on society. It is necessary to distinguish between real and fake news. In the traditional text categorization method, using the TF-IDF information of the words in the document as the weight matrix and applying it to the classifier. Inevitably, TF-IDF contains limited information, limiting the effect of classification. This paper proposed a method based on TF-IDF and Word2vec for identifying fake news, using SVM to verify its validity.
Integrated Airborne Particle Matter Detector

This paper describes the first results of a research project that aims to the development of a miniaturized atmospheric particulate matter sensor. The detector relies on a gravimetric selection of the particle size and on an integrated capacitive sensing interface. In this communication, in particular, preliminary results regarding the electronic readout are presented.
Approximate Sum of Absolute Transformed Differences Hardware Accelerator

Motion Estimation and mode decision are the most compute-intensive tasks in recent video encoders. Sum of Absolute Transformed Differences (SATD) is a distortion metric used inside ME and mode decision to decide the best block to be used as prediction for each block to be encoded. To reduce the complexity of SATD calculation, this work proposes an approximate SATD hardware accelerator based on 1D $4\times 4$ Hadamard Transform, in contrast with the precise SATD hardware accelerator that contains a 2D $4\times 4$ Hadamard Transform. The proposed approximate SATD accelerator reduces power dissipation in 66.86% compared to the precise SATD accelerator while increasing BD-BR only by less than 0.748%, which is a better result than SAD accelerator that increases BD-BR in up to 0.830% compared to precise SATD.
Query Reconstruction in Medical Case Description Using Query Performance Predictors

As an important branch of clinical decision support track, medical literature retrieval receives the electronic medical record as an input to obtain the acquired information from a large amount of medical literature. However, the description of clinical record is rather complicated and ambiguous in semantic way, therefore it is necessary for us to perform query reconstruction for a better retrieval performance. In this paper, we take the advantage of query quality predictors to process long clinical notes with redundant content, and predict query intent to reconstruct the original query. Experimental results on the standard Text REtrieval Conference (TREC) CDS track dataset confirm the superior performance of the proposed method.
Proof of concept for Scenario-in-the-Loop (SciL) testing for autonomous vehicle technology

The paper presents a novel simulation concept for autonomous and highly automated road vehicles, called Scenario-in-the-Loop (SciL) testing. SciL can contribute to a more efficient development, testing and validation of driverless cars, which is a pressing question of our days. SciL based testing introduces a new approach capable to simulate and control realistic traffic scenarios around the autonomous vehicle under test realizing a Digital Twin technology for testing. For realistic traffic generation a high fidelity microscopic traffic simulator (SUMO) and for visualization the Unity 3D game engine are involved. The proposed testing methodology was proved with a real world autonomous car. As a test environment for SciL demonstration ZalaZONE Smart City Zone was used. Two different traffic scenarios (platooning and valet parking with pedestrian dummy) have been successfully tested and demonstrated.
A Comparison of Facial P300-Speller Paradigm Based on Famous Face and the Familiar Face

Some studies about face recognition showed the human face with more relevant to oneself induces larger amplitude ERPs than the famous face. The famous face stimulus has been used to the P300-speller paradigm and improved the performance of P300-speller system. In our study, we designed a novel P300-speller paradigm using familiar face as stimulus, and the famous face P300-speller paradigm was as the control paradigm. We compared the ERPs and the classification accuracy between the two paradigms. The results showed that larger ERPs amplitude in the proposed paradigm than that in the control paradigm, and the average classification of proposed paradigm was greater than that in the control paradigm, while we didn't find the significant difference for classification accuracy between the two paradigms.
Behavioral Entropy Towards Detection of Metamorphic Malwares

Recent metamorphic malware detection methods based on statistical analysis of malware code and measuring similarity between codes are by far more superior compared with signature-based detection methods; yet, lacking against code obfuscation methods including insertion of garbage codes similar to benign files and replacing instructions with equivalent instructions. This paper proposes a method on improved detection of metamorphic malwares based on activity and behavior analysis of executable files. The process involves two stages: initially, behavior of the file is analyzed during runtime and the behavioral pattern is obtained; then, in the second stage, behavioral patterns of the malware files are compared with the sample file in order to determine the level of similarity. The stage on analyzing behavior of the file is accomplished in a monitored environment and then malicious behavioral features of the file are extracted. The second stage involves determining level of similarity between malwares registered into the database in the first stage and the sample files. The obtained experimental results show that the proposed method, by determining the similarity level of behavioral patterns, significantly improves detection of metamorphic malwares and along with no false positives.
Discovery of Frequent Patterns of Episodes Within a Time Window for Alarm Management Systems

The sequential pattern mining field is expanding through numerous researches and has a large number of applications such as language processing, alarms management and event management on a broader scale. Its use began with processing items baskets to learn patterns and have a directed marketing strategy but it is generalized to telecommunication alarms management with several works. Our work is in line with this, as it tries to locate patterns and identify them to make predictive statements about certain patterns. It is axed around providing a way to break sequences into episodes and assigning them a value of confidence and support, more precisely in the discovery of frequent patterns of episodes within a time window. Experimental results have shown the effectiveness of our sequential pattern mining approach and its adaptability to alarm management and analytics.
Fast Algorithm for CU Partitioning and Mode Selection in HEVC Intra Prediction

Aiming at the high complexity problem of the new generation of high efficiency video coding (HEVC) intra-frame coding, a fast CU partitioning algorithm based on intra-frame neighbor information and an improved intra mode selection algorithm are proposed. The algorithm predicts the depth range by using the depth information of neighboring CUs, and then uses the improved mode selection algorithm to reduce the computational complexity of the overall intra prediction process. Compared with the HM16.1 standard model, the intra-frame coding time is reduced by 54%, the peak signal-to-noise ratio (PSNR) loss is about 0.0139 dB, and the code rate is increased by 0.7% on average. Experimental data shows that the proposed algorithm significantly shortens the intra-frame coding time while maintaining the image quality, which effectively reduces the complexity of the intra-frame coding algorithm.
Virtual Reality Therapy and Machine Learning Techniques in Drug Addiction Treatment

With the continuous development of machine learning technology, machine learning methods have broad application prospects in medical data mining. This paper proposes a method of using virtual reality technology to assist drug addiction treatments with a sample size of 58 drug abusers in a drug rehabilitation center located in Zhejiang province, China. In the process of drug addicts watching VR videos in different scenes, physiological data of concentration and heart rate are collected. A hybrid machine learning technique that combines principal component analysis (PCA) and K-Means++ clustering algorithm is used to cluster drug addicts to find out the relationship between cardiac physiological characteristic data and treatment effect. The experimental results show that after VR treatment, the mean and standard deviation heart rate of the patients have decreased significantly, which provides a firm evidence that the patient's response to drug stimulation is severely reduced after VR treatments. The conclusion was drawn that the virtual reality is indeed effective in assisting detoxification.
TSIM: A Two-Stage Selection Algorithm for Influence Maximization in Social Networks

The influence maximization problem is aimed at finding a small subset of nodes in a social. /network to maximize the expected number of nodes influenced by these nodes. Influence maximization plays an important role in viral marketing and information diffusion. However, some existing algorithms for influence maximization in social networks perform badly in either efficiency or accuracy. In this paper, we put forward an efficient algorithm, called a two-stage selection for influence maximization in social networks (TSIM). Moreover, a discount-degree descending technology and lazy-forward technology are proposed, called DDLF, to select a certain number of influential nodes as candidate nodes. Firstly, we utilize the strategy to select a certain number of nodes as candidate nodes. Secondly, this paper proposes the maximum influence value function to estimate the marginal influence of each candidate node. Finally, we select seed nodes from candidate nodes according to their maximum influence value. The experimental results on six real-world social networks show that the proposed algorithm outperforms other contrast algorithms while considering accuracy and efficiency comprehensively.
Classification of Chromosome Karyotype Based on Faster-RCNN with the Segmatation and Enhancement Preprocessing Model

Prenatal screening of chromosomal abnormalities is an important means of ensuring the healthy survival rate of newborns. The complex information and tedious workload of chromosome karyotype image analysis is a major difficulty in medical diagnosis. In this paper, a preprocessing model with object segmentation and feature enhancement is proposed. Combined with the framework of deep learning network, an automatic classification model for karyotype recognition of chromosomes is constructed. The preprocessing model studies the extraction of chromosome karyotype images at the pixel level and the feature enhancement of chromosome karyotype images. The model aims at providing more interpretable information for the deep learning network. In this paper, the algorithm analysis of chromosome karyotype preprocessing is carried out, the classification recognition network is built, and the detection results of the network verify the positive role of the preprocessing model. The model of chromosome karyotype automatic analysis based upon deep learning network may provide accurate reference information for doctors and reduce the workload of repeated diagnoses, which has very high application values.
Digital Signal Processing Accelerator for RISC-V

In this work, we present a configurable accelerator for the RISC-V processor oriented to digital signal processing applications for energy efficient Internet-of-Things devices. The supported operations in the accelerator are addition, multiplication, and linear combination. The accelerator can support different applications: mono-dimensional and bi-dimensional filtering and pattern matching. The results show that the configurable accelerator offers better performance and lower energy consumption when compared to the software execution of the same application on the RISC-V.
Design and Testing of the Space Floating Potential Detector

A space floating potential detector (FPD) is an instrument being developed to study the charging of spacecraft structures. It measures the floating potential that arises as the spacecraft interacts with the low Earth orbit (LEO) plasma as well as the Earth’s magnetic field. The FPD belongs to the active potential control system and consists of three functional modules. Leakage current-eliminating and zero-clearing techniques are used in the detector design to ensure the measurement of the floating potential over the long term within the range from −150 to 0 V and at a voltage resolution of less than 1 V. In order to investigate the performance of the FPD, a typical LEO space plasma environment is simulated in the laboratory. The calibrations of the FPD show that the output voltage is basically linear with the input bias voltage, and the maximum nonlinear error is less than 0.9%.
Evaluating the Trade-offs in the Hardware Design of the LEDAcrypt Encryption Functions

The confidentiality provided by widely employed asymmetric cryptosystems relying on the hardness of factoring large integers or computing discrete logarithms in a cyclic group will be jeopardized by the availability of quantum computers. As a consequence, the design of quantum-computing resistant cryptographic primitives has gained traction lately, especially thanks to the U.S. National Institute of Standards and Technology initiative, which aims at selecting a portfolio of primitives for standardization. A prime position in the set of asymmetric encryption primitives is occupied by the ones relying on decoding random linear error correction codes as their trapdoor. Among these primitives, the LEDAcrypt cryptosystem was recently announced to be admitted to the second round of the standardization initiative, where the investigation of the implementation performance is the main focus. In this paper we analyze the possible hardware designs to implement fast polynomial multiplications tailored to the encryption functions of the LEDAcrypt key encapsulation mechanism, and public key encryption primitives. In particular, we describe two designs for binary polynomial multiplications and analyze the advantages provided by exploiting the sparse nature of one of the operands in LEDAcrypt multiplications, validating our designs on a Xilinx Artix 100.
Design and Implementation of a Novel R-Peak Detection Algorithm

As a crucial part of electrocardiogram (ECG) signal processing, the R-peak detection algorithm is important for the calculation and analysis of cardiac-specific parameters in heart health monitoring and heart disease diagnosis. To achieve high-quality R-peak detection, this paper proposes a novel algorithm consisting of four parts. The digital filtering is used to remove noise interference. The Shannon energy envelope is adopted to achieve QRS complexes enhancement and noise reduction simultaneously. The curve length transformation based on window division is used to suppress pseudo R-peaks. The Hilbert transform achieves no threshold settings and no prior knowledge of detected R-peaks. The algorithm is evaluated by using the MIT-BIH arrhythmia database and achieves average detection accuracy of 99.86%, precision of 99.97%, recall of 99.89%.
Performance Evaluation of Material Decomposition for the Dual-Energy CT Images Reconstructed by MAP-EM Algorithm

Dual energy computed tomography (DECT) plays a significant role in medical application and public security. The accuracy of the decomposition relies much on the quality of the reconstructed images. Images reconstructed by traditional methods generally suffer from significant noise, leading to the low accuracy of the material decomposition and identification. In order to solve the above problem, the maximum a posteriori expectation maximization (MAP-EM) is employed to reconstruct the CT images for the material decomposition, and the performance of the material decomposition is evaluated. A dual source DECT system with 140/80 kVp are simulated by Geant4. The MAP-EM algorithm is used to reconstruct the images from the collected projection at two different tube voltages. And then the basis material decomposition coefficient images are obtained with the basis material decomposition coefficient equations. Comparing with the commonly used filtered back projection (FBP) algorithm, the MAP-EM algorithm reduced noise in polyethylene region on the reconstructed images at two tube voltages with 80 kVp and 140 kVp up to 33.23%, 26.43% respectively. The contrast-to-noise ratios (CNRs) of the aluminum images were improved by 54.13% and 41.02%, that of the HA images by 52.75% and 40.47%, and that of the salt water images by 52.84% and 40.22% at the above two tube voltages. Compared with DE-FBP, the decomposition error of DE-MAP was reduced by 95.94% to 99.09%. In conclusion, the result shows superior performance on material decomposition and identification with high CNRs and low decomposition errors with the DECT images reconstructed by MAP-EM algorithm.