Miracle-dz/UniFAR-FTUs huggingface dataset:
Tackling Big Data and Black Swans through Fractal Approach and Quantum Technology

Since the dawn of time, man has always tried to predict the future. Inserted in an environmental context, the knowledge of the variables that influenced his life allowed him to reap daily benefits and ultimately ensured his survival. Weather forecasts, bets on sports results, financial analysis, estimation of life span probabilities, to name just a few examples, are based on increasingly accurate estimates thanks to increasingly efficient statistical techniques and detection tools. Risk and uncertainty, however, although increasingly limited, represent an essential variable of any future event. The possibility of measuring and preventing (even if close to their occurrence) unlikely, but potentially catastrophic events, can determine extraordinary competitive advantages or even just guarantee the survival of a business or human existence. Unlikely events, but catastrophic, are the so-called "black swans" [1], and represent the nightmare of those who rely on the Gaussian approach, since, even if they fall into the tails of the bell, they represent a non-negligible threat. Studies on the black swan, especially after the events linked to the outbreak of the COVID-19 pandemic, have brought to light the so-called fractal approach that comes closest to the occurrence of most natural events. The analysis of big data, focused on the identification of the black swan, can follow different paths, in any case, the "normal" Gauss curve, as demonstrated, does not lend itself to this type of analysis, therefore most of the statistical tools which are based on this are not suitable for these analyses. This research highlights and tries to demonstrate how the fractal approach, combined with quantum technology, could really represent a great advance in the reliability of future predictions and the detection of black swans.
REAL-GUARD: A Machine Learning based Real-time Mechanism for Combining Packet and Flow Features to Mitigating Network Attacks in SDN

Software Defined Network (SDN) is a new networking technology with the advantages of separating data forwarding plane from the control plane, and a growing number of traditional network attacks are left to this new network architecture. However, current solutions only concentrate on several special attacks in SDN and bring out a variety of overhead. In this paper, we consider two levels detection in data forwarding plane: packet level and flow level. We proposed an efficient, effective, real-time and machine learning based mechanism, called REAL-GUARD, to detect and defend network security threats with decision tree methods and without any extra devices. The experiments prove that our mechanism can defend scanning attacks and detect flooding attacks effectively with low additional performance overhead.
The extraction of image texture features by constructing Gabor filter based on intelligent algorithm

Based on image processing, feature extraction was a key part of against too much when Gabor filter to extract the texture feature parameters, difficult to find the appropriate parameters of the problem, after the analysis of mechanism of Gabor filter and various parameters, considering the characteristics of the intelligent algorithm is good at finding the optimal solution, designed by genetic algorithm selection, crossover and mutation operation, find out the objective function of the fitness of Gabor parameters, so as to realize the structure of the Gabor filter through the simulation, the scheme of the correctness of the Gabor filter.
Siamese NestedUNet Networks for Change Detection of High Resolution Satellite Image

Change detection is an important task in remote sensing (RS) image analysis. With the development of deep learning and the increase of RS data, there are more and more change detection methods based on supervised learning. In this paper, we improve the semantic segmentation network UNet++ and propose a fully convolutional siamese network (Siam-NestedUNet) for change detection. We combine three types of siamese structures with UNet++ respectively to explore the impact of siamese structures on the change detection task under the condition of a backbone network with strong feature extraction capabilities. In addition, for the characteristics of multiple outputs in Siam-NestedUNet, we design a set of experiments to explore the importance level of the output at different semantic levels. According to the experimental results, our method improves greatly on a number of indicators, including precision, recall, F1-Score and overall accuracy, and has better performance than other SOTA change detection methods. Our implementation will be released at https://github.com/likyoo/Siam-NestedUNet.
Convolutional Neural Network based Timbre Classification

Artificial intelligent technologies have been widely adopted in image classification, natural language processing, etc. However, few works have been conducted in timbre classification of human voice. The challenges of timbre classification lay in the difficulty of audio denoising and feature applicability. In this paper, we propose a convolutional neural network (CNN) based method to classify the timbre of human voice. We utilize the Mel Frequency Cepstral Coefficient (MFCC) feature to better represent each voice segment. Classic denoise and purify methods are adopted for the preprocessing, and then we use the CNN for feature fusion. The proposed network could classify the voice into eight categories, including voice of middle-aged women, young girls, middle-aged men, young boys. Based on a human voice dataset which contains more than 10,000 voice segments, the experiment results show that our method could achieve 63.5% classification accuracy. As this is a new direction to classify the timbre of human voice, we hope this paper could stand for a pioneering work in the field.
Deep Learning Reconstruction Method of Meteorological Radar Echo Data based on Satellite Data

In the field of meteorology, weather forecasting requires meteorological radar echo data as support. However, the lack of radar data due to malfunctions or lack of radar deployment has greatly affected the stability of weather forecasting. In order to solve this problem, this paper proposes a meteorological radar echo data reconstruction technology based on satellite data realized by deep learning neural network technology, so as to supplement the missing meteorological radar echo data. In order to realize this technology, we constructed a new dataset, designed a neural network structure suitable for it, and obtained a model with great effect after training. After the final test, it is found that this model can reconstruct radar data well and has practical value.
Named entity recognition of legal judgment based on small-scale labeled data

An important task in intelligent justice is to extract the relevant part of the case description, that is, case elements. Correspondingly, named entity recognition technology is an effective method to extract the elements of the cases. However, most of the existing methods for named entity recognition require large-scale training data, which is a very labor-intensive process to label all data manually, especially for different judgement cases as well as their elements. To this end, this paper proposes a method based on small-scale labeled data in legal judgment. Specifically, first we label data with bootstrapped pattern learning from small-scale manual labeling data, expand the seed dictionary during iteration to obtain sufficient labeled data, and then embed the words by Google BERT pre-trained Chinese model. Finally, CRF+Bilstm named entity recognition model is leveraged to extract case elements. Through the detailed experiments, our method can achieve better entity recognition based on small-scale manual labeling data.
Non-Functional Requirements Classification for Aligning Business with Information Systems

Non-Functional Requirements (NFR) are defined as the desired quality requirements, such as availability, that restrict software product being developed where some external restrictions may apply. Since information systems have been introduced, organizations in the business world align their functional activities with systems without paying attention to quality-based alignment. Few research works have been conducted in order to classify and integrate the NFR with business or system models. But these classifications and integrations are only confined to either the business side or the system side, which in turn have caused in having a gap in mapping the classifications between the two sides. Because business models and system models mutually affect each other in many ways, their NFR integration and classification should be aligned with each other. Having a NFR alignment-based classification between business and information systems contributes to assist the stakeholders in reflecting the quality requirements at the business side for a particular task on the related tasks integrated with NFRs at the systems side. Also having an alignment-oriented classification contributes to trace quality/NFR-based changes from the business organization to its systems and vice versa. In this research, we propose a NFR classification for aligning quality requirements in business with their NFRs in information systems. The work in business side is represented through business process models designed using Business Process Model and Notation (BPMN) where the use case models represents the system side in this research. The proposed classification is demonstrated in both business and systems using the academic advising and registration case study at Applied Science University in Jordan.
Fly-Manager: Enabling Efficient and Reliable Management of High-performance Network Chip

Realizing efficient and reliable management of network chip plays an important role in improving testability, controllability and flexibility of network. Recently, Software Defined Network (SDN) has promoted the further decoupling of control plane and data plane, the interaction between controller and network equipment becomes more frequent, which puts forward higher requirements for efficiency, reliability and latency performance of network update. However, there are some problems in the bus which is widely used in the management of network chips, such as low efficiency, high delay and not conducive to chip back-end design. In this paper, we propose Fly-Manager, a new management mechanism of high-performance network chip. Fly-Manager realizes hardware resource access based on multi-chain hardware architecture and the management information is transmitted by packet inside the chip, which realizes the parallel and pipelined processing of management request. Besides, Fly-Manager provides fine-grained flow control strategy aiming at differentiated management requirements of hardware modules. We prototype Fly-Manager on FPGA to evaluate the resource consumption and performance under different scenarios. Experimental results show that Fly-Manager can support 600Kpps hardware resource update with extremely low hardware resources, 2 orders higher than existing common SDN switches.
Structures of [PhCNSSN]2[Pt(mnt)2] and [(p-ClC6H4CNSSN)2Cl][Pt(mnt)2] (mnt = maleonitriledithiolato ligand)

4-Phenyl-1,2-dithia-3,5-diazolium bis(maleonitriledithiolato)platinum(II), [PhCNSSN] 2 [Pt(mnt) 2 ] (1), and bis-[4-(4-chlorophenyl)-1,2-dithia-3,5-diazolium] chloride-bis(maleonitriledithiolato)platinum(III), [p-ClC 6 H 4 CNSSN) 2 Cl][Pt(mnt) 2 ] (2), contain essentially planar [Pt(mnt) 2 ] n- anions [n=2 for complex (1), n=1 for complex (2)], with square-planar coordination of Pt. The Pt-S bonds are slightly, but significantly, shorter for the Pt III complex (2). The cations contain planar CNSSN five-membered rings with an aryl substituent Ar (Ph or p-ClC 6 H 4 ) on C
Does An Insider Give More Suggestions?: A Moderated Mediation Model

Employee voice is an essential behavior in today's organizations to facilitate improvements and make constructive changes in the way that work is conducted. Therefore, how to encourage employee voice behavior is the important issue in the academic field. This paper explores he relationship between Chinese employees' perception of insider status on and their voice behaviors, as well as the potential mediating effect role of job embeddedness and the moderating effect role of commitment-based human resource management practices (CBHRMPs) with data from a sample of 136 subordinate-supervisor dyads based multiple regression analysis method. The results indicate that perceived insider status is positively related to employee voice behavior. Job embeddedness functions as a partial mediator between perceived insider status and employee voice behavior and CBHRMPs moderate the relationship between job embeddedness and employee voice behavior. The findings of this research are of great guiding importance for enterprises to encourage employee voice behavior.
Measurement of grinding surface roughness based on image energy distribution

Aiming at the problem that the current machine vision detection roughness mainly uses image gray value information for statistical analysis, does not make full use of color information and ignores the subjective evaluation of the human visual system, a surface roughness detection method based on image energy distribution is proposed. According to the difference in the energy distribution formed by the color patches on the surface of different grades of roughness, and theoretically analyze it, the image energy distribution index is used as the basis for evaluating the surface roughness. The experimental results show that under the conditions of the same illumination conditions and the orientation of the surface texture of the sample, the CDI index tends to decrease obviously with the increase of roughness, indicating that the proposed method has a certain feasibility. By establishing the mathematical relationship model between CDI index and roughness, it is found that the prediction result of the calibration sample is more accurate and the detection range is relatively wide, which shows that the proposed method is a reasonable and feasible method for surface roughness detection.
Healthor: Protecting the Weak in Heterogeneous DLTs with Health-aware Flow Control

Permissionless distributed ledger technologies (DLTs) utilize an underlying peer-to-peer network to disseminate transactions. These types of networks have been shown to be highly heterogeneous. However, current DLTs fail to consider this heterogeneity which can render low-end nodes to be unable to participate in consensus. In this paper we introduce Healthor, a novel heterogeneity-aware flow-control mechanism that formalizes this heterogeneity in a notion of health of a node. In our early simulation results we show that Healthor enables high-end nodes to protect their weaker neighbors by buffering at their end while incurring only minimal overhead.
A Cloud Robotic Application Platform Design Based on the Microservices Architecture

The paradigm of cloud robotics points out a direction for the future development of robots. By deploying robotic applications in the cloud, the workload and cost of local robots are greatly reduced. The rise of microservices and cloud-native technology provides conveniences and guarantees for the development and deployment of cloud applications. This paper proposes a cloud robotic application platform design based on microservices. With the help of Robot Operating System (ROS), we can use the existing rich and diverse robot software packages and deploy them in the cloud without extra modifications. Through the microservices architecture and container technology, robotic applications can be further decoupled in the cloud. That improves the flexibility and compatibility of the platform and embodies the core idea of microservices. In the end, we present a demonstration to cooperate with a simulated robot to complete the simultaneous localization and mapping (SLAM) task, which verifies the feasibility of our design.
Usability Analysis of Educational Information Systems from Student's Perspective

The COVID-19 is declared as a world emergency and it is widespread in countries and territories which affected around 91% students worldwide that they are unable to attend their educational institutions physically where the numbers are unprecedented and implications are massive. The continuity in teaching and learning while ensuring the protection and safety of students and staff, brought new challenges in this epidemic. Institutes around the globe started online education and students were motivated to become part of E-Learning through educational information systems. This study is investigating the impact of online teaching, educational information systems, learning outcomes from students' perspective. By using the user Centered Design methodology, around two hundred students and 30 faculty members from five different countries of Europe and Asia were involved in this research. Their feedback was taken through questionnaires, interviews and focus group methods. A comprehensive questionnaire, containing six different questions regarding understanding about educational information systems and another six questions related to students-instructor interaction, were asked from students. The instructors were interviewed about their previous exposure or training for designing online courses. The results showed that students' satisfaction on online teaching varied country wise while course objectives are not being fulfilled as per their perception. The students showed their concern that online activities are not much engaging, showed less satisfaction in lab work while in some cases felt stressed, overburdened and they found insufficient technical support being offered by their institute. The study is unique as it covered vast factors impacting online teaching and the results provide guidelines for all stakeholders, policy makers, educators and management.
BagMeLiF: stable boosting-based hybrid-ensemble feature selection algorithm for high-dimensional data

The problem of selecting features for a data set with a small number of objects is one of the most complex ones. Significant features selected for such data sets can vary quite a lot depending on how sub-sampling was performed during validation. This effect is called low feature set stability and signals on low reliability of the selected features. We propose a feature selection algorithm that is based on bagging procedure of feature selection filters quality measures ensemble and allows to obtain more stable feature sets, than would be obtained by running conventional algorithms, called BagMeLiF. This algorithm is based on MeLiF algorithm and will outperform original algorithm both in F1 score and stability with hyperparameter k around 0.7–0.9 if the dataset is well-balanced, but if it is not, then k around 0.1–0.2 will the best which is a quite straightforwardly applicable result.
Examination the nonlinear relationship between demand changes and cost behavior: a case study from Iraq

The paper tried to examine the effect of demand change on cost behavior and determine the type of relationship based on economic theory for asymmetry phenomenon, which implies that costs are more likely to increase when demand increases are greater than demand decreases. Study methodology tests the hypotheses of nonlinear relationships among the variables used in the proposed model. The proposed model has been tested by using multiple regression analyses depended on data have collected from Iraqi factories of cement manufacturing in ten years. The results showed that cost associated with demand changes on nonlinear relationships asymmetrically which degree of cost asymmetry drives via demand, costs arise relative to demand is higher than costs fall. The value of research is the first to present and examine the economic model of asymmetric cost behavior by demand changes in Iraq. A little literature has tested the effects of demand fluctuation on cost behavior, which is changed the organizational understanding from considerations of accounting to economics within a competitive environment.
Gossiping with Append-Only Logs in Secure-Scuttlebutt

Middlewares for building social applications, in which infrastructure is provided by participants, are currently developed by open-source communities. Among those, Secure-Scuttlebutt has pioneered the use of replicated authenticated single-writer append-only logs, i.e., chains of ordered immutable events specific to each participant, replicated by gossip algorithms that are driven by social signals, to build eventually-consistent social applications. The use of persistent append-only logs removes parameters traditionally required to be tuned for gossiping. We present two gossip models that can be used for replication: a new open model, simpler than the current SSB implementation, that works best in small and trusted groups; and the transitive-interest model practically deployed by SSB, that scales to thousands of participants and is spam- and sybil-resistant. We also present limitations of both to motivate further research.
Research on Credit Risk Assessment of Online Network Credit Based on GBDT

As an innovative business model in the Internet + finance field, online loans are still immature at home and abroad, and there are few studies on quantitative models of personal credit risk assessment applied to. Based on the loan data released by the Lending club online loan platform, this paper builds a online loan credit risk assessment indicator system, innovatively introduces the "loan / annual income" debt yield indicator, and builds a gradient-based decision tree (GBDT) -based online loan personal credit risk assessment model, used to predict the default rate of borrowers. Finally, empirical research shows that the gradient prediction tree model has good prediction accuracy, and the prediction accuracy rate for the borrower is actually not overdue and the forecast is not overdue (true classification) is as high as 87.31%, and the borrower is actually overdue and predicted overdue The true-negative category) has a prediction accuracy of 79.88 %.
A Systemic Review of Kernel Fuzzing

As an automatic vulnerability discovery method, fuzzing has received more and more attention from academia and industry. To address the weaknesses of existing fuzzers or to apply them into different scenarios, researchers have proposed various solutions to the problem. In order to apply fuzzing, a technique with excellent performance in vulnerability discovery, to kernel vulnerability discovery, security researchers have come up with a number of effective and efficient solutions. This paper aims to discuss the application of fuzzing in operating systems kernel. First, we introduce popular the concept of kernel vulnerability and the importance of kernel vulnerability discovery. Second, we discuss the development and classification of fuzzing technology. Finally, we discuss the recent developments in kernel fuzzing and their ideas for solving the problem in detail.