Miracle-dz/UniFAR-FTUs huggingface dataset:
The ALASKA Steganalysis Challenge: A First Step Towards Steganalysis

This paper presents ins and outs of the ALASKA challenge, a steganalysis challenge built to reflect the constraints of a forensic steganalyst. We motivate and explain the main differences w.r. t. the BOSS challenge (2010), specifically the use of a ranking metric prescribing high false positive rates, the analysis of a large diversity of different image sources and the use of a collection of steganographic schemes adapted to handle color JPEGs. The core of the challenge is also described, this includes the RAW image data-set, the implementations used to generate cover images and the specificities of the embedding schemes. The very first outcomes of the challenge are then presented, and the impacts of different parameters such as demosaicking, filtering, image size, JPEG quality factors and cover-source mismatch are analyzed. Eventually, conclusions are presented, highlighting positive and negative points together with future directions for the next challenges in practical steganalysis.
Running Language Interpreters Inside SGX: A Lightweight,Legacy-Compatible Script Code Hardening Approach

Recent advances in trusted execution environments, specifically with Intel's introduction of SGX on consumer processors, have provided unprecedented opportunities to create secure applications with a small TCB. While a large number of SGX solutions have been proposed, nearly all of them focus on protecting native code applications, leaving scripting languages unprotected. To fill this gap, this paper presents SCRIPTSHIELD, a framework capable of running legacy script code while simultaneously providing confidentiality and integrity for scripting code and data. In contrast to the existing schemes that either require tedious and time-consuming re-development or result in a large TCB by importing an entire library OS or container, SCRIPTSHIELD keeps the TCB small and provides backwards compatibility (i.e., no changes needed to the scripting code itself). The core idea is to customize the script interpreter to run inside an SGX enclave and pass scripts to it. We have implemented SCRIPTSHIELD and tested with three popular scripting languages: Lua, JavaScript, and Squirrel. Our experimental results show that SCRIPTSHIELD does not cause noticeable overhead. The source code of SCRIPTSHIELD has been made publicly available as an open source project.
Towards a Cyber-Physical Range

Cyber-physical systems are being increasingly employed in everyday applications, including critical ones. This integration of operational technology systems, originally designed to operate in physical isolation -hence with no or little cyber security defences- with information technology systems, by default meant to be networked, dramatically increases the cyber-attack surface of the resulting composite systems. Thus, the assessment of the security posture of cyber-physical systems, as well as the evaluation of the effectiveness and efficiency of the defensive mechanisms become of paramount importance. Unfortunately, testing cyber security in live real-world cyber-physical systems is not advisable, even when it is possible; hence, the use of testbeds is a necessary alternative. This work surveys cyber-physical testbeds in five major application domains, with an eye towards identifying key features to be subsequently used as input to the process of defining requirements for future cyber-physical testbeds with cyber security posture assessment capability. We then propose a reference architecture for the next generation of cyber ranges, namely the cyber-physical ranges.
Unsupervised Co-segmentation of 3D Shapes Based on Components

The co-segmentation of a set of 3D shapes plays an important role in understanding and analyzing the 3D shapes. An unsupervised co-segmentation algorithm of 3D shapes based on components is proposed in view of the low efficiency and poor accuracy in the existing co-segmentation algorithms. Firstly, all the input models are pre-partitioned into meaningful components by calculating the conformal factor. Secondly, the 3D model is converted into a statistical model, and every column of the statistical model is used to represent different component unit, and then the corresponding relationship between different components is constructed and marked via Gaussian kernel function. Finally, dynamic k-means clustering algorithm is employed to realize meaningful co-segmentation. Experimental result demonstrates that the proposed algorithm can achieve co-segmentation of 3D model cluster with the same function but different postures and the obtained segmentation is convenient for further geometric analysis.
Can I Opt Out Yet?: GDPR and the Global Illusion of Cookie Control

The European Union's (EU) General Data Protection Regulation (GDPR), in effect since May 2018, enforces strict limitations on handling users' personal data, hence impacting their activity tracking on the Web. In this study, we perform an evaluation of the tracking performed in 2,000 high-traffic websites, hosted both inside and outside of the EU. We evaluate both the information presented to users and the actual tracking implemented through cookies; we find that the GDPR has impacted website behavior in a truly global way, both directly and indirectly: USA-based websites behave similarly to EU-based ones, while third-party opt-out services reduce the amount of tracking even for websites which do not put any effort in respecting the new law. On the other hand, we find that tracking remains ubiquitous. In particular, we found cookies that can identify users when visiting more than 90% of the websites in our dataset - and we also encountered a large number of websites that present deceiving information, making it it very difficult, if at all possible, for users to avoid being tracked.
A NOVEL MULTIBAND KOCH LOOP ANTENNA USING FRACTAL GEOMETRY FOR WIRELESS COMMUNICATION SYSTEM

The paper present a novel multi-band compact antenna designed on the theory of fractal geometry. The antenna is fabricated on a FR4 substrate. The performance of the proposed antenna design is analyzed and the results are compared with the simulations using IE-3D tool. The relevant antenna performance parameters of the proposed design viz. resonant bands, return loss, bandwidth and gain are reported and discussed. The VSWR of the antenna is less than 2 for six resonant bands in the vicinity of 1.15 GHz, 2.0 GHz, 3.17 GHz, 3.6 GHz, 4.17 GHz and 5.91 GHz. The performance results exhibited by the proposed antenna makes it extremely useful for the future generation of wireless broadband communication systems.
Enhancing Code Similarity Analysis for Effective Vulnerability Detection

Vulnerabilities may spread through different software projects via code reusing. Therefore, it is important to identify the components of a project, to reveal vulnerabilities inside a component that may affect the project. Code similarity analysis is crucial for improving the recall of component identification. The local sensitive hashing (LSH) solves the problem that the traditional hash cannot record the difference information between similar files. Two files with nuances cannot be tested by the MD5 matching algorithm, but they can generate similar fingerprints with LSH. The similarity degree between files can be measured by calculating the Hamming distance between fingerprints. The Simhash algorithm can complement the file-level homology analysis algorithm based on MD5 matching. We can combine the Simhash algorithm and the MD5 matching algorithm, i.e., quickly find all the identical clone files through MD5 matching, and then find similar files with slight modifications through the similar hash algorithm, which makes the final analysis result more accurate. In the massive data set, the process of finding the fingerprint value that is similar (in this experiment, the Hamming distance <=3) to the Simhash fingerprint generated by the code to be tested has efficiency problems. In the process of optimizing query efficiency, we generally carry out time and space trade-offs. Therefore, we leverage the optimization method used by Gurmeet to make a compromise between time efficiency and space efficiency.
Online Scheduling Strategy to Minimize Penalty of Tardiness for Real-Time Tasks in Mobile Edge Computing Systems

With the coming of big data era and 5G era, many tasks have higher and higher requirement for the latency and data, traditional cloud computing paradigm is gradually unable to handle such real-time scenarios with large number of tasks, tremendous data volume and high latency requirements. In order to solve these problems, mobile edge computing has become the focus of attention. However, although low latency is a major feature of mobile edge computing, some real-time tasks still cannot be completed on time due to the limitation of network and computing resources, which will affect the quality of experience (QoE) of users and lead the loss to the economy and reputation of enterprises.To reduce the penalty of tardiness of tasks, in this paper, we consider the mobile edge computing system as a soft real-time system and discuss how to assign the task to a server and how to schedule the task on the server. Considering the feature of real-time tasks, we have a formal description of this problem and discuss the online version of this problem. We propose a heuristic algorithm based on the urgency of deadline of tasks to reduce the loss caused by task timeout. Experiments show that our algorithm has better performance than the classic real-time task scheduling algorithm.
Colosseum: A Scalable Permissioned Blockchain over Structured Network

Permissioned blockchain protocols generally use voting based algorithms to reach consensus. Scaling such protocols is hard because of their high message complexity and thus, have a limited number of applications. Use cases like banking and healthcare blockchain require a scalable distributed protocol to maintain the consistency of the ledger and ensure its safety. This paper presents Colosseum, a scalable consensus protocol designed for permissioned blockchain systems. It is a knockout tournament based protocol over a structured ring network to reach consensus on the next set of block proposers. Participants compete in the rounds of a novel two-player game in each tournament to become eligible to propose block. Colosseum introduces Proof-of-Win as the certificate for the result of matches to have an efficient and easy way to propose trusted blocks and verify them.
An Empirical Study of Prioritizing JavaScript Engine Crashes via Machine Learning

The early discovery of security bugs in JavaScript (JS) engines is crucial for protecting Internet users from adversaries abusing zero-day vulnerabilities. Browser vendors, bug bounty hunters, and security researchers have been eager to find such security bugs by leveraging state-of-the-art fuzzers as well as their domain expertise. They report a bug when observing a crash after executing their JS test since a crash is an early indicator of a potential bug. However, it is difficult to identify whether such a crash indeed invokes security bugs in JS engines. Thus, unskilled bug reporters are unable to assess the security severity of their new bugs with JS engine crashes. Today, this classification of a reported security bug is completely manual, depending on the verdicts from JS engine vendors. We investigated the feasibility of applying various machine learning classifiers to determine whether an observed crash triggers a security bug. We designed and implemented CRScope, which classifies security and non-security bugs from given crash-dump files. Our experimental results on 766 crash instances demonstrate that CRScope achieved 0.85, 0.89, and 0.93 Area Under Curve (AUC) for Chakra, V8, and SpiderMonkey crashes, respectively. CRScope also achieved 0.84, 0.89, and 0.95 precision for Chakra, V8, and SpiderMonkey crashes, respectively. This outperforms the previous study and existing tools including Exploitable and AddressSanitizer. CRScope is capable of learning domain-specific expertise from the past verdicts on reported bugs and automatically classifying JS engine security bugs, which helps improve the scalable classification of security bugs.
A Centroid Based Text Categorization Method Using Mean Shift

Text categorization is an important research topic in Information Retrieval area and it is one of the key techniques for handling and organizing the huge amount of text data available on the Internet and other digital format in our daily life. In this paper, we propose a method for text categorization based on Mean Shift. Mean Shift algorithm is a well developed technique in computer vision researches. We extend the application of Mean Shift to text categorization by reducing the dimensions of text vector space to a proper scale. Firstly, a low-dimensional feature space is constructed using the feature selection method by the theory of information gain. Secondly, an adaptive Mean Shift algorithm is applied for detecting the centers (centroids) of each category on the feature space above. Finally, each document will be added to its most similar category by calculating the similarities between the document and the center of every category. Experimental results on 20NewsGroup and Rueters-21578 corpus show that this method can achieve higher performance than some classic text categorization method like KNN, Naive Bayes and SVM.
RIP-RH: Preventing Rowhammer-based Inter-Process Attacks

Run-time attacks pose a continuous threat to the security of computer systems. These attacks aim at hijacking the operation of a computer program by subverting its execution at run time. While conventional run-time attacks usually require memory-corruption vulnerabilities in the program, hardware bugs represent an increasingly popular attack vector. Rowhammer represents a vulnerability in the design of DRAM modules that allows an adversary to modify memory locations in physical proximity to attacker-controlled memory on the module without accessing them. This is a serious threat to real-world systems, since DRAM is used as main memory on virtually all platforms. Recent research proposed defenses against rowhammer, such by patching the memory controller in hardware, or statically partitioning physical memory to protect the operating system kernel from a user space adversary. However, sharing DRAM memory securely between a number of different entities currently remains as an open problem. In this paper, we present RIP-RH, a DRAM-aware memory allocator that allows for dynamic management of multiple user-space processes. RIP-RH ensures that the memory partitions belonging to individual processes are physically isolated. In our detailed evaluation we demonstrate that our prototype implementation of RIP-RH incurs a modest run-time overhead of 3.17% for standard benchmarks and offers practical performance in a number of real-world scenarios.
Spectrum Allocation of Cognitive Radio Network Based on Improved Cuckoo Search Algorithm

In order to effectively solve the discrete optimization problem of cognitive radio network spectrum allocation, a new binary adaptive cuckoo search (BACS) algorithm is proposed, which introduces reverse learning in the initialization stage of traditional cuckoo search (CS), it increases the diversity of the population. Second, the adaptive discovery probability balances the ability of global and local optimization. Finally, a cognitive wireless network spectrum allocation method based on binary adaptive cuckoo algorithm is proposed, and compared with the classical spectrum allocation algorithm for network benefit function and fairness. The simulation shows that BACS has better optimization ability and can be used to maximize the network benefits and fairness between users when applied to spectrum allocation.
THE HISTORY OF THE OHAAKI GEOTHERMAL FIELD – IN 3 D

Geothermal drilling of the Ohaaki Geothermal Field (New Zealand) started in 1965, and subsequently involved several phases of exploration, delineation and production/injection drilling, culminating in the commissioning of the Ohaaki Power Station in 1988. Since that time, exploration, development and monitoring activities have continued, with ongoing collation of geophysical survey and reservoir engineering data, and new geological insights from recent drilling operations. The acquisition of new geoscientific information demands a combined interpretation of the geothermal data sets which can be challenging. As such, 3D modelling has been increasingly used by Contact Energy Ltd. and GNS Science for geothermal data management, analysis and interpretation. To demonstrate the value of 3D modelling and data integration, five 3D geological models of the Ohaaki Geothermal Field have been constructed, for five different stages of field development, using Leapfrog Geothermal 3D visualisation and modelling software. Comparison of the respective 3D models with (historical) interpretations made at each development phase highlights the utility and effectiveness of 3D geological modelling in producing robust and consistent interpretations. We demonstrate how 3D geological models and numerical interpolations can be easily updated and refined, with testing of alternative interpretations accompanying revision of the conceptual and geological models. As a result of increased efficiency and reliability, 3D models can be used on a routine basis to assist in the development of drilling strategies and to advance data integration for field management.
Are Nonrespondents Necessarily Bad Reporters ? Using Imputation Techniques to Investigate Measurement Error of Nonrespondents in an Alumni Survey

Declining response rates in household surveys speak to the increasing difficulty in recruiting sampled persons to respond to a survey request. Survey organizations are spending increasingly more effort on contacting and persuading sampled persons with a low response propensity in an attempt to increase response rates and to minimize nonresponse error. A major concern, however, is that, if sampled persons with a low response propensity turned out to be bad reporters, the costly extra recruitment effort could reduce nonresponse error at the expense of increasing measurement error. However, this concern is difficult to address since the measurement error property of nonrespondents is unknown. This paper treats this issue as a missing data problem and imputes for values of survey reports for nonrespondents. The results showed that the relationship between response propensity and measurement error depends on the cause of nonresponse. The report quality was not worse for nonrespondents due to non-contact. However, nonrespondents due to refusal tend to produce less stable reports than cooperative respondents.
Detection method for the sources of change in estimates

In the context of a survey, there is often a need to know and to quantify the main sources of changes observed between two estimates over time. We can then explain these changes or identify the outlier units having substantial impact on the change. Depending on the estimator used, the type of data or the number of units, this exercise can become highly complex. We will describe a method based on partial derivatives to find the sources of a change. This method can be used in several contexts. Using the estimator formula, we consider the independent variables as functions over time, calculate the partial derivatives of the estimator and approximate the impacts of the units. We can then estimate the impact on the change of each variable and each unit. The main advantage of this simple method is that it can be used to easily and accurately identify which, among all the data, have had a significant impact.
THE ANALYSIS OF FREQUENCY DATA BASED ON AN ALTERNATIVE FORM OF CONSTRUCTING A CONTINGENCY TABLE

The effects of obtaining a test statistic for the test of independence when the data are obtained from a certain common complex sampling scheme is examined in this paper. The data are summarized in a two way contingency table where the cell entries are not necessarily an integer value. This alternative method of obtaining a two way table based on ratio estimators is compared to the traditional summing procedure in the construction of contingency tables. A Wald test statistic based on Wald (1943) for the test of independence is obtained for this particular sampling scheme and compared to these two forms of table construction techniques. This paper shows that an alternative approximation to the Wald test statistic for independence is to construct a Pearson type statistic based on the alternative table presented here rather than constructing a Pearson Statistic on the usual contingency table, as is sometimes done in complex sampling schemes.
SURVEY DESIGN IN THE PRODUCER PRICE INDEX

BACKGROUND INFORMATION : First published in 1902 as the Wholesale Price Index (WPI) and now known as the Producer Price Index (PPI), this survey is the oldest continuous statistical series published by the Bureau of Labor Statistics (BLS). In 1978 the survey was redesigned with new theoretical underpinnings and a new methodology. A comprehensive revision replaced the commodity-based indexes of the WPI with the industry-based structure in which indexes are based on the Standard industrial Classification (SIC) scheme as developed by the Office of Management and Budget (OMB). The entire U.S. economy is divided into ten sectors. Current Producer Price Indexes cover all of the Mining and Manufacturing (M & M) sectors with ongoing expansion into the Service-related sectors. There are 493 SIC classifications (in the 1972 manual) included within the scope of the M{nlng and Manufacturing sectors of the economy. The BLS is currently publishing indexes for nearly all of these industries. Currently there are more than 75,000 price quotes in repricing, representing approximately 18,000 reporters. The BLS has begun sampling some of these industries for the second time in the process of replacing outdated samples. The first time that each industry was sampled under the revised methodology is referred to as Cycle I, with Cycle II denoting the second time that an SIC is sampled. Cycle I was basically completed before Cycle II began, but since the frequency with which an industry is resampled is dependent on many variables, at some point in time, one industry will have been sampled three times, while another industry will have been sampled only once.
One-Versus Two-Step Approaches to Survey Nonresponse Adjustments

In a series of papers, Little and Vartivarian (2003, 2005) argued that basing survey nonresponse adjustments on propensity to respond could increase the sampling variance of the estimates while not reducing bias, if the predictors of response were unrelated to key survey outcomes. Applying this idea to a 2014 military workforce survey, RAND researchers used machine learning approaches to develop a two-step method for nonresponse adjustment. The two step method comprises (1) a model for the key outcome variables based on respondents and (2) a response propensity model using the predicted key outcome variables as predictors for all sampled units. At the 2016 JSM, we presented simulation results assessing the predictive performance of competing machine learning algorithms in the first of the two steps. In this paper, we investigate the circumstances necessary for the two-step method to outperform nonresponse approaches in common practice, most of which can be regarded as single-step methods.