Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Modeling Approach to Fuzzy-Neural Networks Architecture

In this paper, as a new category of fuzzy-neural networks architecture, we propose Fuzzy Polynomial Neural Networks (FPNN) and discuss a comprehensive design methodology related to its architecture. FPNN dwells on the ideas of fuzzy rule-based computing and neural networks. The FPNN architecture consists of layers with activation nodes based on fuzzy inference rules. Here each activation node is presented as Fuzzy Polynomial Neuron(FPN). The conclusion part of the rules, especially the regression polynomial, uses several types of high-order polynomials such as linear, quadratic and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are studied. It is worth stressing that the number of the layers and the nods in each layer of the FPNN are not predetermined, unlike in the case of the popular multilayer perceptron structure, but these are generated in a dynamic manner. With the aid of two representative time series process data, a detailed design procedure is discussed, and the stability is introduced as a measure of stability of the model for the comparative analysis of various architectures.
The deconvolution of lunar brightness temperature based on the maximum entropy method using Chang'e-2 microwave data

A passive and multi-channel microwave sounder onboard the Chang'e-2 orbiter has successfully acquired microwave observations of the lunar surface and subsurface structure. Compared with the Chang'e-1 orbiter, the Chang'e-2 orbiter obtained more accurate and comprehensive microwave brightness temperature data, which are helpful for further research. Since there is a close relationship between microwave brightness temperature data and some related properties of the lunar regolith, such as the thickness, temperature and dielectric constant, precise and high resolution brightness temperature data are necessary for such research. However, through the detection mechanism of the microwave sounder, the brightness temperature data acquired from the microwave sounder are weighted by the antenna radiation pattern, so the data are the convolution of the antenna radiation pattern with the lunar brightness temperature. In order to obtain the real lunar brightness temperature, a deconvolution method is needed. The aim of this paper is to solve the problem associated with performing deconvolution of the lunar brightness temperature. In this study, we introduce the maximum entropy method (MEM) to process the brightness temperature data and achieve excellent results. The paper mainly includes the following aspects: first, we introduce the principle of the MEM; second, through a series of simulations, the MEM has been verified as an efficient deconvolution method; and third, the MEM is used to process the Chang'e-2 microwave data and the results are significant.
The Potential and Electric Fields of a Conducting Sphere in the Presence of a Charged Conducting Plane

Abstract : The solution is given to Laplace's equation for a conducting sphere above a ground plane. The solution includes the presence of a uniform electric field perpendicular to the ground plane. The arbitrary constants in the solution are determined by applying the boundary conditions on the plane and on the sphere. Formulas are given for the evaluation of the potential and electric fields at an arbitrary field point. All the resulting formulas are expressed in a style for computation. The computational results will form the basis of a future comparison report.
Hysteresis and optical bistability in degenerate four-wave mixing (A)

The intensity‐dependent refractive index leads to hysteresis and optical bistability in degenerate four‐wave mixing processes (DFWM). Bistability may be observed in the phase conjugate signal or in the ’’spontaneous’’ oscillation possible in DFWM. This bistability does not require external resonant cavities.
Rotation, magnetism and metallicity of M dwarf systems

Close M dwarf binaries and higher multiples allow the investigation of rotational evolution and mean magnetic flux unbiased from scatter in inclination angle and age since the orientation of the spin axis of the components is most likely parallel and the individual systems are coeval. Systems composed of an early-type (M0.0-M4.0) and a late-type (M4.0-M8.0) component offer the possibility to study differences in rotation and magnetism between partially and fully convective stars. We have selected 10 of the closest dM systems to determine the rotation velocities and the mean magnetic field strengths based on spectroscopic analysis of FeH lines of Wing-Ford transitions at 1 μm observed with Very Large Telescope/CRIRES. We also studied the quality of our spectroscopic model regarding atmospheric parameters including metallicity. A modified version of the Molecular Zeeman Library (MZL) was used to compute Landeg-factors for FeH lines. Magnetic spectral synthesis was performed with the SYNMAST code. We confirmed previously reported findings that less massive M dwarfs are braked less effectively than objects of earlier types. Strong surface magnetic fields were detected in primaries of four systems (GJ 852, GJ 234, LP 717-36 and GJ 3322), and in the secondary of the triple system GJ 852. We also confirm strong 2-kG magnetic field in the primary of the triple system GJ 2005. No fields could be accurately determined in rapidly rotating stars with \upsi sin i \gt 10 km s$^-1$. For slowly and moderately rotating stars, we find the surface magnetic field strength to increase with the rotational velocity \upsi sin i which is consistent with other results from studying field stars. Based on observations made with European Southern Observatory (ESO) Telescopes at the Paranal Observatories under programme ID 81.D-0189.
Ages and abundances in large-scale stellar discs of nearby S0 galaxies†

By undertaking deep long-slit spectroscopy with the focal reducer SCORPIO of the Russian 6m telescope, we studied stellar population properties and their variation with radius in 15 nearby S0 galaxies sampling a wide range of luminosities and environments. For the large-scale stellar disks of S0s, we have measured SSP-equivalent metallicities ranging from the solar one down to [Z/H]= 0.4 0.7, rather high magnesium-to-iron ratios, [Mg/Fe]> +0.2, and mostly old SSP-equivalent ages. Nine of 15 (60%±13%) galaxies have large-scale stellar disks older than 10 Gyr, and among those we find all the galaxies which reside in denser environments. The isolated galaxies may have intermediate-age stellar disks which are 7-9 Gyr old. Only two galaxies of our sample, NGC 4111 and NGC 7332, reveal SSP-equivalent ages of their disks of 2–3 Gyrs. Just these two young disks appear to be thin, while the other, older disks have scale heights typical for thick stellar disks. The stellar populations in the bulges at radii of 0.5re are on the contrary more metal-rich than the solar Z⊙, with the ages homogeneously distributed between 2 and 15 Gyr, being almost always younger than the disks. We conclude that S0 galaxies could not form in groups at z � 0.4 as is thought now; a new scenario of the general evolution of disk galaxies is proposed instead.
The Semantic Processing Pipeline: Quantifying the Network-Wide Impact of Security Tools

We present the Semantic Processing Pipeline (SPP), a component of the larger process of our Uncertainty Handling Workflow [10]. The SPP is a configurable, customizable plugin framework for computing network-wide impact of security tools. In addition, it can be used as a labeled data generation mechanism for leveraging machine learning based security techniques. The SPP takes cyber range experiment results as input, quantifies the tool impact, and produces a connected graph encoding knowledge derived from the experiment. This is then used as input into a quantification mechanism of our choice, be it machine learning algorithms or a Multi-Entity Bayesian Network, as in our current implementation. We quantify the level of uncertainty with respect to five key metrics, which we have termed Derived Attributes: Speed, Success, Detectability, Attribution, and Collateral Damage. We present results from experiments quantifying the effect of Nmap, a host and service discovery tool, configured in various ways. While we use Nmap as an example use case, we demonstrate that the SPP easily be applied to various tool types. In addition, we present results regarding performance and correctness of the SPP. We present runtimes for individual components as well as overall, and show that the processing time for the SPP scales quadratically with increasing input sizes. However, the overall runtime is low: the SPP can compute a connected graph from a 200-host topology in roughly one minute.
Odile: A scalable tracing tool for non-rooted and on-device Android phones

As Android’s popularity continues to grow among consumers and device manufacturers, it is also becoming a prime target for malware authors. Although static app analysis is quite simple to use and scale very well, it is inefficient when the app is obfuscated or the malicious code is dynamically downloaded at runtime. Runtime analysis of app behavior is thus becoming paramount for reverse engineers and app market maintainers (e.g., Google Play) to ensure that running apps do not include some malicious payload. However, dynamic binary instrumentation of apps to track on-device app behavior at runtime is very challenging: (i) it does not scale with the number of the intercepted calls as it increases the memory footprint of the instrumented app, ineluctably leading to Out-Of-Memory crash, and (ii) it can not do API-level tracing at scale. Further, most of the time it requires either to root/jailbreak devices, or the use of a modified Android system, preventing its use on any end-user phone. We introduce a new dynamic binary instrumentation tool, named Odile , to help reverse engineers to perform on-device analysis for non-rooted Android devices. Odile provides a new scalable tracing approach that we call delegated instrumentation. It leverages Android’s instrumentation module and mainly relies on ART reverse engineering. We demonstrate the effectiveness of Odile in tracing various app types (including benign apps and malware). In particular, we show how much Odile outperforms Frida , the state-of-the-art tool in the domain.
Viopolicy-Detector: An Automated Approach to Detecting GDPR Suspected Compliance Violations in Websites

To provide users with personalized services, the website collects and tracks user’s activity data. At the same time, each website uses a privacy policy to ensure the legality of these actions. The purpose of the implementation of the General Data Protection Regulation (GDPR) is to protect the privacy of user data. Because GDPR is a programmatic regulation, there is no specific guidance on what a privacy policy should contain. Therefore, there may still be potential violations on the website, thus cause a risk of leak users’ private data. In this paper, we define a violating behavior that data collected by the website without a declaration in the privacy policy is illegal. To complete the violating behavior detection, we first interpret the GDPR and analyze 1000 website privacy policies to present a personal data classification including eight categories. Based on this, we propose a privacy policy annotation scheme including these eight categories and collect 145 related Web APIs. Then we propose an automated method to detect GDPR suspected compliance violations in websites. On the one hand we use the multi-label text classification model to extract data collection stated in the privacy policy, with a precision of 0.9817. For another, we dynamically monitor the JavaScript calls of the website related to personal data collection during user visits. Finally, we compare the two results to determine whether violating behaviors appeared. We use this method to detect the European top 500 websites (actually 451 websites). A total of 159 (35.3%) websites appear in violation of the GDPR. We analyze the detection results from different perspectives, including statistics on the types of data declared in the privacy policy, statistics on data collected by the website, and which data collection is likely to cause violations. Then we classify the violating websites and find that websites in the Social category present the most violations. Finally, we count the rankings of the offending websites. Surprisingly, top-ranking sites are even more prone to breaches. There are even some globally well-known websites with violations, such as BBC, Nokia, Ebay, Google etc.
Script Tainting Was Doomed From The Start (By Type Conversion): Converting Script Engines into Dynamic Taint Analysis Frameworks

Data flow analysis is an essential technique for understanding the complicated behavior of malicious scripts. For tracking the data flow in scripts, dynamic taint analysis has been widely adopted by existing studies. However, the existing taint analysis techniques have a problem that each script engine needs to be separately designed and implemented. Given the diversity of script languages that attackers can choose for their malicious scripts, it is unrealistic to prepare taint analysis tools for the various script languages and engines. In this paper, we propose an approach that automatically builds a taint analysis framework for scripts on top of the framework designed for native binaries. We first conducted experiments to reveal that the semantic gaps in data types between binaries and scripts disturb our approach by causing under-tainting. To address this problem, our approach detects such gaps and bridges them by generating force propagation rules, which can eliminate the under-tainting. We implemented a prototype system with our approach called STAGER T. We built taint analysis frameworks for Python and VBScript with STAGER T and found that they could effectively analyze the data flow of real-world malicious scripts.
An OpenFlow based Optimal Selection Mechanism for Multiple Web Servers

Web based Internet services normally will be constructed with multiple redundant servers considering fault tolerance, load balancing and traffic distribution. As an easy and popular way Domain Name System (DNS) based round robin technology has been well used. An end client will receive multiple IP address for a web server from DNS based domain name resolution and normally the first one will be used for the access. However, the first IP address will not necessarily be the locationally nearer one or the one with lower workload. As a result, neither the service provider can provide efficient Internet service nor the end client can use the Internet service comfortably. In this paper, we propose an OpenFlow based optimal selection mechanism for multiple web servers with collaboration of DNS based domain name resolution in prior to accessing the web servers. In the proposed mechanism, the OpenFlow technology has been used for latency probing to each web server using the IP addressed received from the DNS response. A prototype system based on the proposed mechanism has been implemented in a local network environment and the functionalities have been evaluated. According to the results, we confirmed that the one with lower latency among the multiple web servers can be successfully selected for the end client and the sessions from the others can be reset with low overhead.
Bias, Consistency, and Partisanship in U.S. Asylum Cases: A Machine Learning Analysis of Extraneous Factors in Immigration Court Decisions

In this study, we introduce a novel two-pronged scoring system to measure individual and systemic bias in immigration courts under the U.S. Executive Office of Immigration Review (EOIR). We analyze nearly 6 million immigration court proceedings and 228 case features to build on prior research showing that U.S. asylum decisions vary dramatically based on factors that are extraneous to the merits of a case. We close a critical gap in the literature of variability metrics that can span space and time. Using predictive modeling, we explain 58.54% of the total decision variability using two metrics: partisanship and inter-judge cohort consistency. Thus, whether the EOIR grants asylum to an applicant or not depends in majority on the combined effects of the political climate and the individual variability of the presiding judge — not the individual merits of the case. Using time series analysis, we also demonstrate that partisanship increased in the early 1990s but plateaued following the turn of the century. These conclusions are striking to the extent that they diverge from the U.S. immigration system’s commitments to independence and due process. Our contributions expose systemic inequities in the U.S. asylum decision-making process, and we recommend improved and standardized variability metrics to better diagnose and monitor these issues.
A Survey of Convolutional Neural Network and Its Variants

With the development of deep learning and neural network technique, Convolutional Neural Network (CNN), with its large applicability to diverse types of datasets, is developing fast than ever since the award-winning AlexNet. In this paper, we have provided a wide range of the current prototypes of CNNs that people can later refer to. Such work has not been done and a summary of this type is very necessary as increasing number of publications will be in the CNN field in the incoming years. We will first walk the readers through some popular frameworks, and then dig deeper into applications of CNN in a variety of fields, including but not limited to large-scale image classifications, medical image segmentations, remote sensing segmentations. Moreover, we will further discuss the potentially even wider application of CNN combining transfer learning and interactive featuring. In the Appendices section, we have attached some explanations on definitions of commonly used functional layers and their use cases, as well as some widely used datasets.
Impact of SUSY-QCD corrections on neutralino-stop co-annihilation and the neutralino relic density

We have calculated the full O(as) supersymmetric QCD corrections to neutralino-stop coannihilation into electroweak vector and Higgs bosons within the Minimal Supersymmetric Standard Model (MSSM). We performed a parameter study within the phenomenological MSSM and demonstrated that the studied co-annihilation processes are phenomenologically relevant, especially in the context of a 126 GeV Higgs-like particle. By means of an example scenario we discuss the effect of the full next-to-leading order corrections on the co-annihilation cross section and show their impact on the predicted neutralino relic density. We demonstrate that the impact of these corrections on the cosmologically preferred region of parameter space is larger than the current experimental uncertainty of WMAP data.
Encrypted Malware Traffic Detection via Graph-based Network Analysis

Malicious activities on the Internet continue to grow in volume and damage, posing a serious risk to society. Malware with remote control capabilities is considered one of the most threatening malicious activities, as it can enable arbitrary types of cyber-attacks. As a countermeasure, many malware detection methods are proposed to identify malicious behaviours based on traffic characteristics. However, the emerging encryption and evasion techniques pose substantial barriers to the full exploitation of network information. This significantly impairs the effectiveness of existing malware detection methods relying on a singular type of characteristics. In this paper, we propose ST-Graph to resolve this issue. In addition to traditional stream attributes, ST-Graph explores spatial and temporal characteristics of network behaviours based on a graph representation learning algorithm and integrates all available information to boost the detection decision. To illustrate the effectiveness of ST-Graph, we evaluate it on two datasets. Experimental results demonstrate that ST-Graph outperforms state-of-the-art malware detection systems and also shows good performance in efficiency, generalizability, and robustness. Specifically, it achieves over 99% precision and recall, and its False Positive Rate is even two orders of magnitude lower than (nearly 0.02 times) that of baseline models. Meanwhile, the deployment of ST-Graph in two real network scenarios for around one year shows an outstanding efficiency with only 160 seconds time cost for 5-minute traffic in 1.7 Gbps bandwidth.
ULTRA: Ultimate Rootkit Detection over the Air

Rootkits are the most challenging malware threats against server and desktop systems. They are created by highly skilled actors and are deployed in advanced persistent threat attacks. Lately and even in the future, rootkits will become a real threat to billions of IoT devices. Existing malware detection techniques based on static or dynamic analysis face major shortcomings, which become more apparent when it is necessary to detect threats on IoT devices. In this paper, we propose the ULTRA framework, which can detect rootkits effectively and efficiently by operating outside of the “box” (literary device) with no resource requirement on the target device. ULTRA baits the rootkit to provoke activity, measures electromagnetic emanation with a software-defined radio, preprocesses signals, then detects and classifies rootkit behavior using machine/deep learning techniques. As use cases, we target two IoT devices with MIPS and ARM architectures. The proposed approach achieved promising results with high accuracy for detecting both known and unknown rootkits during the offline learning phase. Our experimental study involves classification of rootkit families and distinct variants, obfuscated rootkits, probe dislocation, benign noise (kernel) activities, and comparison with software-based solutions.
Keeping Researchers Updated by Automatically Enriching an Ontology in the Medical Field

Ontologies provide a common and standard dictionary of terms in some domain for researchers to easily exchange data. Forming an ontology requires several years of work performed by human experts, and an ontology for a given domain is thought to be stable for many years. Nevertheless, as scientific articles are continuously published to gather knowledge on recent findings, existing ontologies risk becoming stale, or require further human effort. Moreover, searching new articles without referring to an ontology can be very time consuming and confusing, especially for novice researchers. We propose an approach for automatically relating newly available published articles to existing ontologies. By automatically selecting relevant scientific articles and making them appear besides other data in an ontology, we aim at supporting experienced and novice researchers. Therefore, as knowledge grows and articles are available, the ontology used by researchers will also be automatically connected, allowing them to readily discover new findings. To validate the effectiveness of the proposed approach, we have enriched OBIB, an ontology for biobanking, with a selection of articles extracted from PubMed. The approach is general enough and can be applied to other ontologies or publishers.