Miracle-dz/UniFAR-FTUs huggingface dataset:
An Adaptive Protection of Flooding Attacks Model for Complex Network Environments

Currently, online organizational resources and assets are potential targets of several types of attack, the most common being flooding attacks. We consider the Distributed Denial of Service (DDoS) as the most dangerous type of flooding attack that could target those resources. The DDoS attack consumes network available resources such as bandwidth, processing power, and memory, thereby limiting or withholding accessibility to users. The Flash Crowd (FC) is quite similar to the DDoS attack whereby many legitimate users concurrently access a particular service, the number of which results in the denial of service. Researchers have proposed many different models to eliminate the risk of DDoS attacks, but only few efforts have been made to differentiate it from FC flooding as FC flooding also causes the denial of service and usually misleads the detection of the DDoS attacks. In this paper, an adaptive agent-based model, known as an Adaptive Protection of Flooding Attacks (APFA) model, is proposed to protect the Network Application Layer (NAL) against DDoS flooding attacks and FC flooding traffics. The APFA model, with the aid of an adaptive analyst agent, distinguishes between DDoS and FC abnormal traffics. It then separates DDoS botnet from Demons and Zombies to apply suitable attack handling methodology. There are three parameters on which the agent relies, normal traffic intensity, traffic attack behavior, and IP address history log, to decide on the operation of two traffic filters. We test and evaluate the APFA model via a simulation system using CIDDS as a standard dataset. The model successfully adapts to the simulated attack scenarios’ changes and determines 303,024 request conditions for the tested 135,583 IP addresses. It achieves an accuracy of 0.9964, a precision of 0.9962, and a sensitivity of 0.9996, and outperforms three tested similar models. In addition, the APFA model contributes to identifying and handling the actual trigger of DDoS attack and differentiates it from FC flooding, which is rarely implemented in one model.
An Optimization Approach for Mining of Process Models with Infrequent Behaviors Integrating Data Flow and Control Flow

Infrequent behaviors of business process refer to behaviors that occur in very exceptional cases, and their occurrence frequency is low as their required conditions are rarely fulfilled. Hence, a strong coupling relationship between infrequent behavior and data flow exists. Furthermore, some infrequent behaviors may reveal very important information about the process. Thus, not all infrequent behaviors should be disregarded as noise, and identifying infrequent but correct behaviors in the event log is vital to process mining from the perspective of data flow. Existing process mining approaches construct a process model from frequent behaviors in the event log, mostly concentrating on control flow only, without considering infrequent behavior and data flow information. In this paper, we focus on data flow to extract infrequent but correct behaviors from logs. For an infrequent trace, frequent patterns and interactive behavior profiles are combined to find out which part of the behavior in the trace occurs in low frequency. And, conditional dependency probability is used to analyze the influence strength of the data flow information on infrequent behavior. An approach for identifying effective infrequent behaviors based on the frequent pattern under data awareness is proposed correspondingly. Subsequently, an optimization approach for mining of process models with infrequent behaviors integrating data flow and control flow is also presented. The experiments on synthetic and real-life event logs show that the proposed approach can distinguish effective infrequent behaviors from noise compared with others. The proposed approaches greatly improve the fitness of the mined process model without significantly decreasing its precision.
A Mobile Malware Detection Method Based on Malicious Subgraphs Mining

As mobile phone is widely used in social network communication, it attracts numerous malicious attacks, which seriously threaten users’ personal privacy and data security. To improve the resilience to attack technologies, structural information analysis has been widely applied in mobile malware detection. However, the rapid improvement of mobile applications has brought an impressive growth of their internal structure in scale and attack technologies. It makes the timely analysis of structural information and malicious feature generation a heavy burden. In this paper, we propose a new Android malware identification approach based on malicious subgraph mining to improve the detection performance of large-scale graph structure analysis. Firstly, function call graphs (FCGs), sensitive permissions, and application programming interfaces (APIs) are generated from the decompiled files of malware. Secondly, two kinds of malicious subgraphs are generated from malware’s decompiled files and put into the feature set. At last, test applications’ safety can be automatically identified and classified into malware families by matching their FCGs with malicious structural features. To evaluate our approach, a dataset of 11,520 malware and benign applications is established. Experimental results indicate that our approach has better performance than three previous works and Androguard.
A study of quality prediction for large-scale open source software projects

Open source software (OSS) has seen remarkable progress in recent years. Moreover, OSS usage in corporate information systems has been increasing steadily; consequently, the overall impact of OSS on the society is increasing as well. While product quality of enterprise software is assured by the provider, the deliverables of an OSS are developed by the OSS developer community; therefore, their quality is not guaranteed. Thus, the objective of this study is to build an artificial-intelligence-based quality prediction model that corporate businesses could use for decision-making to determine whether a desired OSS should be adopted. We define the quality of an OSS as “the resolution rate of issues processed by OSS developers as well as the promptness and continuity of doing so. ” We selected 44 large-scale OSS projects from GitHub for our quality analysis. First, we investigated the monthly changes in the status of issue creation and resolution for each project. It was found that there are three different patterns in the increase of issue creation, and three patterns in the relationship between the increase in issue creation and that of resolution. It was confirmed that there are multiple cases of each pattern that affect the final resolution rate. Next, we investigated the correlation between the final resolution rate and that for a relevant number of months after issue creation. We deduced that the correlation coefficient even between the resolution rate in the first month and the final rate exceeded 0.5. Based on these analysis results, we conclude that the issue resolution rate in the first month once an issue is created is applicable as knowledge for knowledge-based AI systems that can be used to assist in decision-making regarding OSS adoption in business projects.
XPS investigations of graphene surface cleaning using H2‐ and Cl2‐based inductively coupled plasma

It is known that graphene surface contaminations by residues affect drastically its intrinsic properties and cannot be avoided when chemical vapor deposited (CVD) graphene is transferred on other substrates. In this work, we investigate by X‐ray photoelectron spectroscopy and work function measurements using X‐ray photoemission electron microscopy the capabilities of high‐density plasmas to clean graphene. The evolution of different chemical species at surface is monitored as a function of plasma exposure. H2 plasmas are shown to clean efficiently PMMA residues from CVD graphene on Cu. However, when the same plasma is used on graphene transferred on SiO2/Si substrate a liftoff of the graphene layer is observed before the end of cleaning procedure. These results are discussed in terms of H+ penetration through graphene and H2 formation between the SiO2 substrate and graphene. Using Cl‐based chemistries, we found that the plasma is able to etch polymeric contamination at the graphene surface. It is also found that the plasma induces spreading of the Si nanoparticle contamination that hampers the cleaning process. Copyright © 2016 John Wiley & Sons, Ltd.
Evaluation of a Human-Machine Interface for Motion Sickness Mitigation Utilizing Anticipatory Ambient Light Cues in a Realistic Automated Driving Setting

Motion sickness (MS) is a syndrome associated with symptoms like nausea, dizziness, and other forms of physical discomfort. Automated vehicles (AVs) are potent at inducing MS because users are not adapted to this novel form of transportation, are provided with less information about the own vehicle’s trajectory, and are likely to engage in non-driving related tasks. Because individuals with an especially high MS susceptibility could be limited in their use of AVs, the demand for MS mitigation strategies is high. Passenger anticipation has been shown to have a modulating effect on symptoms, thus mitigating MS. To find an effective mitigation strategy, the prototype of a human–machine interface (HMI) that presents anticipatory ambient light cues for the AV’s next turn to the passenger was evaluated. In a realistic driving study with participants (N = 16) in an AV on a test track, an MS mitigation effect was evaluated based on the MS increase during the trial. An MS mitigation effect was found within a highly susceptible subsample through the presentation of anticipatory ambient light cues. The HMI prototype was proven to be effective regarding highly susceptible users. Future iterations could alleviate MS in field settings and improve the acceptance of AVs.
Research on Multi-Target Network Security Assessment with Attack Graph Expert System Model

With the rapid development of the Internet, network attacks often occur, and network security is widely concerned. Searching for practical security risk assessment methods is a research hotspot in the field of network security. Network attack graph model is an active detection technology for the attack path. From the perspective of the attacker, it simulated the whole network attack scenario and then presented the dependency among the vulnerabilities in the target network in the way of directed graph. It is an effective tool for analyzing network vulnerability. This paper describes in detail the common methods and tools of network security assessment and analyzes the construction of theoretical model of attack graph, the optimization technology of attack graph, and the research status of qualitative and quantitative analysis technology of attack graph in network security assessment. The attack graph generated in the face of large-scale network is too complex to find the key vulnerability nodes accurately and quickly. Optimizing the attack graph and solving the key attack set can help the security manager better understand the security state of the nodes in the network system, so as to strengthen the security defense ability and guarantee the security of the network system. For all kinds of loop phenomena of directed attribute attack graph, the general method of eliminating loop is given to get an acyclic attack graph. On the basis of acyclic attack graph, an optimization algorithm based on path complexity is proposed, which takes atomic attack distance and atomic weight into consideration, and on the basis of simplified attack graph, minimum-cost security reinforcement is carried out for the network environment. Based on the ant colony algorithm, the adaptive updating principle of changing pheromone and the local searching strategy of the adaptive genetic algorithm are proposed to improve the ant colony algorithm. The experimental results show that compared with the ant colony algorithm, the improved ant colony algorithm can speed up the process of solving the optimal solution. When the number of attack paths is large, the advantages of the improved ant colony algorithm in solving accuracy and late search speed are more obvious, and it is more suitable for large-scale networks.
Sports Field Recognition Using Deep Multi-task Learning

: In this paper we propose a novel approach to build a single shot regressor, called SFLNet , that directly predicts a parameter set relating a sports ﬁeld seen in an input frame to its metric model. This problem is challenging due to the huge intra-class variance of sports ﬁelds and the large number of free parameters to be predicted. To address these issues, we propose to train our regressor in combination with semantic segmentation in a multi-task learning framework. We also introduce an additional module to exploit the spacial consistency of sports ﬁelds, which boosts both regression and segmentation performances. SFLNet can be trained with a dataset that can be semi-automatically built from human annotated point-to-point correspondences. To our knowledge, this work is the ﬁrst attempt to solve this sports ﬁeld localization problem relying only on an end-to-end deep learning framework. Experiments on our new dataset based on basketball games validate our approach over baseline methods.
A Multimodality Information Synchronization Scheme for a Multisource Information System in the Electric Grid

A multisource and heterogeneous database is an important problem that disturbs the use of the electric power information system. The existing database synchronization scheme has some problems in practical applications, such as high resource loss and poor portability. This paper presents a high-efficiency database synchronization scheme for the electric power information system. The database is monitored, and its changes are captured by the shadow table and trigger method. Thus, data could be exchanged in trusted networks and nontrusted networks. In addition, a predetermined strategy is used to avoid data conflicts and ensure consistency and reliability of data synchronization. The above method is applied in the protection system of power networks. The results show that the synchronization scheme can effectively ensure the security of the system and has higher synchronization efficiency.
Robust Frame Duplication Detection for Degraded Videos

To detect frame duplication in degraded videos, we proposed a coarse-to-fine approach based on locality-sensitive hashing and image registration. The proposed method consists of a coarse matching stage and a duplication verification step. In the coarse matching stage, visually similar frame sequences are preclustered by locality-sensitive hashing and considered as potential duplication candidates. These candidates are further checked by a duplication verification step. Being different from the existing methods, our duplication verification does not rely on a fixed distance (or correlation) threshold to judge whether two frames are identical. We resorted to image registration, which is intrinsically a global optimal matching process, to determine whether two frames coincide with each other. We integrated the stability information into the registration objective function to make the registration process more robust for degraded videos. To test the performance of the proposed method, we created a dataset, which consists of 3 subsets of different kinds of degradation and 117 forged videos in total. The experimental results show that our method outperforms state-of-the-art methods for most cases in our dataset and exhibits outstanding robustness under different conditions. Thanks to the coarse-to-fine strategy, the running time of the proposed method is also quite competitive.
Sentiment classification of review data using sentence significance score optimisation

: A significant amount of work has been done in the field of sentiment analysis in textual data using the concepts and techniques of natural language processing (NLP). In this work, unlike the existing techniques, we present a novel method wherein we consider the significance of the sentences in formulating the opinion. Often in any review, the sentences in the review may correspond to different aspects which are often irrelevant in deciding whether the sentiment is positive or negative on a topic. Thus, we assign a sentence significance score to evaluate the overall sentiment of the review. We employ a clustering mechanism followed by the neural network approach to determine the optimal significance score for the review. The proposed supervised method shows a higher accuracy than the state-of-the-art techniques. We further determine the subjectivity of sentences and establish a relationship between subjectivity of sentences and the significance score. We experimentally show that the significance scores found in the proposed method correspond to identifying the subjective sentences and objective sentences in reviews. The sentences with low significance score corresponds to objective sentences and the sentences with high significance score corresponds to subjective sentences.
Mobile Sentry Robot for Laboratory Safety Inspection Based on Machine Vision and Infrared Thermal Imaging Detection

As is known to all, university chemistry laboratories have always been considered as dangerous areas on campus, especially pharmaceutical chemistry laboratories, which are often the focus of pharmaceutical laboratory management. The innovation in safety management mode can effectively reduce or even eliminate security risks and reduce the risk of safety accidents. With the rapid development of artificial intelligence, robots have been introduced into all walks of life, replacing humans to complete part of the safety inspection. One of the core tasks of laboratory safety inspection is to find the source of fire and take measures to extinguish the fire in time. The intention of this paper is to design a sentry robot, which could take appropriate emergency measures to detect dangerous situations, including sounding alarms and carrying out firefighting, in order to minimize accidents and economic losses, based on machine vision for periodic inspection of laboratories with a temperature thermal imager to scan surroundings at the same time. The preliminary research results show that based on the machine vision, the robot identified the fire source and adjusted the spray device to carry out the fire-extinguishing experiment successfully. The development and utilization of laboratory patrol robots can share the work of laboratory patrol to a certain extent, guarantee the safety of experimental teaching in colleges and universities, and have room for sustainable research.
Electrochemistry of Metal Complexes: Applications from Electroplating to Oxide Layer Formation

Starting with general considerations on equilibria in solutions and at interfaces as well as on mass transport, the text acquaints readers with the theory and common experimental practice for studying electrochemical reactions of metals complexes. The core part of the book deals with all important aspects of electroplating, including a systematic discussion of co-deposition of metals and formation of alloys. It also discusses such related subjects as oxide layer formation and hydrogen evolution as a side reaction.
Increased Reactive Oxygen Species–Mediated Ca2+/Calmodulin-Dependent Protein Kinase II Activation Contributes to Calcium Handling Abnormalities and Impaired Contraction in Barth Syndrome

Supplemental Digital Content is available in the text. Background: Mutations in tafazzin (TAZ), a gene required for biogenesis of cardiolipin, the signature phospholipid of the inner mitochondrial membrane, causes Barth syndrome (BTHS). Cardiomyopathy and risk of sudden cardiac death are prominent features of BTHS, but the mechanisms by which impaired cardiolipin biogenesis causes cardiac muscle weakness and arrhythmia are poorly understood. Methods: We performed in vivo electrophysiology to define arrhythmia vulnerability in cardiac-specific TAZ knockout mice. Using cardiomyocytes derived from human induced pluripotent stem cells and cardiac-specific TAZ knockout mice as model systems, we investigated the effect of TAZ inactivation on Ca2+ handling. Through genome editing and pharmacology, we defined a molecular link between TAZ mutation and abnormal Ca2+ handling and contractility. Results: A subset of mice with cardiac-specific TAZ inactivation developed arrhythmias, including bidirectional ventricular tachycardia, atrial tachycardia, and complete atrioventricular block. Compared with wild-type controls, BTHS-induced pluripotent stem cell–derived cardiomyocytes had increased diastolic Ca2+ and decreased Ca2+ transient amplitude. BTHS-induced pluripotent stem cell–derived cardiomyocytes had higher levels of mitochondrial and cellular reactive oxygen species than wild-type controls, which activated CaMKII (Ca2+/calmodulin-dependent protein kinase II). Activated CaMKII phosphorylated the RYR2 (ryanodine receptor 2) on serine 2814, increasing Ca2+ leak through RYR2. Inhibition of this reactive oxygen species–CaMKII–RYR2 pathway through pharmacological inhibitors or genome editing normalized aberrant Ca2+ handling in BTHS-induced pluripotent stem cell–derived cardiomyocytes and improved their contractile function. Murine Taz knockout cardiomyocytes also exhibited elevated diastolic Ca2+ and decreased Ca2+ transient amplitude. These abnormalities were ameliorated by Ca2+/calmodulin-dependent protein kinase II or reactive oxygen species inhibition. Conclusions: This study identified a molecular pathway that links TAZ mutation with abnormal Ca2+ handling and decreased cardiomyocyte contractility. This pathway may offer therapeutic opportunities to treat BTHS and potentially other diseases with elevated mitochondrial reactive oxygen species production.
PENGARUH POLITIK ORGANISASI TERHADAP SIKAP KERJA DAN PENGARUHNYA TERHADAP ORGANIZATIONAL CITIZENSHIP BEHAVIOR

Politik organisasi merupakan sebuah fenomena yang ada di dalam organisasi. Meningkatnya persepsi politik dapat memperburuk outcome pada sebuah organisasi. Namun banyak penelitian yang menyatakan bahwa politik dapat meningkatkan outcome pada organisasi. Penelitia n ini meneliti mengenai politik organisasi dan pengaruhnya terhadap sikap kerja dan pengaruhnya terhadap OCB. Penelitian ini merupakan penelitian kuantitatif dengan jumlah responden sebanyak 40 karyawan dan dianalisis menggunakan Partial Least Square (PLS). Hasil dari penelitian ini menunjukkan bahwa seluruh hipotesis diterima. Kata kunci: politik organisasi, sikap kerja, ocb