Miracle-dz/UniFAR-FTUs huggingface dataset:
CONFIRM: Evaluating Compatibility and Relevance of Control-flow Integrity Protections for Modern Software

Xiaoyang Xu, Masoud Ghaffarinia, Wenhao Wang, Kevin W. Hamlen, and Zhiqiang Lin. “CONFIRM: Evaluating Compatibility and Relevance of Control-flow Integrity Protections for Modern Software. ” In Proc. 28th USENIX Security Symposium, August 2019. CONFIRM (CONtrol-Flow Integrity Relevance Metrics) is a new evaluation methodology and microbenchmarking suite for assessing compatibility, applicability, and relevance of control-flow integrity (CFI) protections for preserving the intended semantics of software while protecting it from abuse. Although CFI has become a mainstay of protecting certain classes of software from code-reuse attacks, and continues to be improved by ongoing research, its ability to preserve intended program functionalities (semantic transparency) of diverse, mainstream software products has been under-studied in the literature. This is in part because although CFI solutions are evaluated in terms of performance and security, there remains no standard regimen for assessing compatibility. Researchers must often therefore resort to anecdotal assessments, consisting of tests on homogeneous software collections with limited variety (e.g., GNU Coreutils), or on CPU benchmarks (e.g., SPEC) whose limited code features are not representative of large, mainstream software products. Reevaluation of CFI solutions using CONFIRM reveals that there remain significant unsolved challenges in securing many large classes of software products with CFI, including software for market-dominant OSes (e.g., Windows) and code employing certain ubiquitous coding idioms (e.g., eventdriven callbacks and exceptions). An estimated 47% of CFIrelevant code features with high compatibility impact remain incompletely supported by existing CFI algorithms, or receive weakened controls that leave prevalent threats unaddressed (e.g., return-oriented programming attacks). Discussion of these open problems highlights issues that future research must address to bridge these important gaps between CFI theory and practice.
Development of multilayer films with improved aroma barrier properties for durian packaging application

The symmetrical A/B/A structure of multilayer blown films was fabricated in this study. The immiscible low‐density polyethylene/polylactic acid (LDPE/PLA) blend was set as a core (B) layer and LDPE was used as skin (A) layers. The compositions of PLA in the core layer were varied from 20 to 50 wt%. The thickness of each layer was 10 μm (total film thickness of ~ 30 μm). In a blown film co‐extrusion process, the morphology of the fiber/ribbon‐like structures of LDPE/PLA blend was developed. Such structures had interesting effects on gas permeability and aroma barrier properties of the films. For instance, multilayer LDPE films containing 40 and 50 wt% PLA (P40 and P50) showed the reduction of oxygen permeability (PO2) approximately 20% and 43%, respectively, compared with the neat LDPE film. A long tortuous path for gas and aroma transportation through film thickness was created from the developed ribbon‐like structures of the PLA minor phase. For durian packaging application, fresh‐cut durian of 300 g was packed in the developed multilayer films, LDPE, and HDPE (Control), stored at 4°C for 7 days. Results demonstrated that the steady‐state condition of 10% to 13% O2 and 8% to 10% CO2 was achieved in all packages except in the HDPE. Moreover, the P40 and P50 films exhibited an outstanding aroma barrier property for three major durian volatiles: diethyl sulfide, ethyl propanoate, and 2‐ethyl‐1‐hexanol. Overall results clearly indicated that the multilayer LDPE films containing PLA exhibited a significantly improved aroma barrier performance with optimum gas permeability desirable for modified atmosphere packaging to retain quality of fresh‐cut durian throughout the storage period.
Morphology and spatial refractive index distribution of the retina accessed by hyperspectral quantitative phase microscopy

In ophthalmologic imaging, the optical properties of the retina are essential parameters. The retina’s refractive index (RI) determines the light propagation inside the tissue towards the photoreceptors and its spatial distribution reflects biophysical tissue properties. In addition, information about the RI’s wavelength dependency is crucial for optical imaging, as it has to be considered, e.g., for dispersion compensation in high resolution optical coherence tomography (OCT). However, the spatial RI distribution in retinal tissue is difficult to access. We explored the capabilities of quantitative phase imaging (QPI) for RI characterization of murine retina utilizing digital holographic microscopy (DHM). Multispectral QPI was achieved by a Michelson interferometer-based DHM configuration that was combined with the light from a tunable supercontinuum laser light source.
2 Motivation and Problem Statement 2 . 1 Motivation

We introduce a new concept called brokered delegation. Brokered delegation allows users to flexibly delegate credentials and rights for a range of service providers to other users and third parties. We explore how brokered delegation can be implemented using novel trusted execution environments (TEEs). We introduce a system called DELEGATEE that enables users (Delegatees) to log into different online services using the credentials of other users (Owners). Credentials in DELEGATEE are never revealed to Delegatees and Owners can restrict access to their accounts using a range of rich, contextually dependent delegation policies. DELEGATEE fundamentally shifts existing access control models for centralized online services. It does so by using TEEs to permit access delegation at the user’s discretion. DELEGATEE thus effectively reduces mandatory access control (MAC) in this context to discretionary access control (DAC). The system demonstrates the significant potential for TEEs to create new forms of resource sharing around online services without the direct support from those services. We present a full implementation of DELEGATEE using Intel SGX and demonstrate its use in four real-world applications: email access (SMTP/IMAP), restricted website access using a HTTPS proxy, e-banking/credit card, and a third-party payment system (PayPal).
Modeling models of others ’ mental states : characterizing Theory of Mind during cooperative interaction

Humans are experts in cooperation. To effectively engage with others they have to apply Theory of Mind (ToM), that is they have to model others beliefs, desires, and intentions and predict their behavior from these mental states. Here, we investigate ToM processes during real-time reciprocal coordination between two players engaging in a cooperative decision game. The game consists of a noisy and unstable environment. To succeed participants have to model the state of the world and their partner’s belief about it and integrate both pieces of information into a coherent decision. Thereby the game combines social and non-social learning into a single decision problem. To quantify the learning processes underlying participants’ actions, we modeled the behavior with Interactive Partially Observable Markov Decisions Processes (I-POMDP). The I-POMDP framework extends single agent action planning under uncertainty to the multi-agent domain by including intentional models of other agents. Using this framework we successfully predicted interactive behavior. Furthermore, we extracted participants’ beliefs about the environment and their beliefs about the mental states of their partners, giving us direct access to the cognitive operations underling cooperative behavior. By relating players’ own beliefs with their partners’ model of themselves we show that dyads whose beliefs are more aligned coordinate more successfully. This provides strong evidence that behavioral coordination relies on mental alignment.
Defend Against Application-Layer Distributed Denial-of-Service Attacks Based on Session Suspicion Probability Model

Mitigating Distributed Denial-of-Service(DDoS)attacks becomes more challenging with increasing available resources and techniques for attackers.Current network-layer security devices fail to counter application-layer DDoS(App-DDoS)attacks for the normal traffic feature on the network layer.In this paper,to handle App-DDoS attacks,a novel defense model is proposed.App-DDoS attack is divided into 5 types based on the attack URL generating way.Based on the differences between normal sessions and attack sessions,the paper proposes the session behavior suspicion parameters and the session suspicion model,which can be used to differentiate normal sessions from App-DDoS sessions accurately.The model is combined with 3 forwarding policies,including First-Come First-Serve(FCFS),Low Suspicion First(LSF)and Round Robin respectively to defend against 5 types of App-DDoS attacks.Simulation result with real Web trace shows that these forwarding policies perform well when the forwarding rate equals to the maximum normal request arrival rate,and FCFS and Round Robin perform better than LSF on the normal request response delay.
Syddansk Universitet Temporal accumulation of oriented visual features

In this paper we present a framework for accumulating on-line a model of a moving object (e.g., when manipulated by a robot). The proposed scheme is based on Bayesian filtering of local features, filtering jointly position, orientation and appearance information. The work presented here is novel in two aspects: first, we use an estimation mechanism that updates iteratively not only geometrical information, but also appearance information. Second, we propose a probabilistic version of the classical n-scan criterion that allows us to select which features are preserved and which are discarded, while making use of the available uncertainty model. The accumulated representations have been used in three different contexts: pose estimation, robotic grasping, and driver assistance scenario. 2010 Elsevier Inc. All rights reserved.
Online Truthful Mechanisms for Combinatorial Auctions

We study combinatorial auctions in online environments with an objective to maximize social welfare. In this problem, items arrive on each day and must be sold before the closing date of the market which is unknown to the seller and the buyers. We consider two settings, one where items must be sold immediately on arrival (called ImmediateSale), and the other where items can be stored and sold later (called DeferredSale). In the ImmediateSale setting, we show the following results: - for submodular valuations, we give a O ( log m ) -competitive mechanism for adversarial valuations and an O ( 1 ) -competitive mechanism for Bayesian valuations, where m is the total number of items. Both of our mechanisms are efficient and universally truthful for myopic agents (i.e., agents who do not know the future); - there is no online mechanism that can achieve Ω (cid:16) ( m / log m ) 1 / 3 (cid:17) approximation for XOS (and therefore subadditive) valuations, even in a Bayesian setting. Our lower bound holds even if we do not require truthfulness and/or efficiency of the mechanism. In the DeferredSale setting, we provide a reduction that converts offline mechanisms into online mechanisms while preserving the approximation ratio, for the general class of subadditive valuations. This allows us to infer approximation bounds of O ( log m loglog m ) and O (cid:0) ( loglog m ) 3 (cid:1) for subadditive and XOS valuations in the prior-free setting, respectively, and O ( log m ) and O ( 1 ) for subadditive and XOS valuations in the Bayesian setting, respectively, from previous work on offline mechanisms.
What Works at Scale? A Framework to Scale Up Workforce Development Programs

Workforce development policymakers have access to a growing catalog of training programs evaluated with rigorous randomized controlled trials. This evidence base identifies programs that work in specific geographic and temporal contexts but may not necessarily work in other contexts or at a scale sufficient to meet regional workforce needs. The author examines a sample of recent randomized controlled trials of workforce development programs and reports to what extent this body of evidence informs policymakers about what works at scale. The author finds that most programs are implemented at a small scale, use nonrandom samples from the population of interest, and are concentrated in the most populous urban areas and U.S. states. The author then discusses a method to help state and local policymakers, technical colleges, training providers, and other workforce development organizations adopt evidence-based policies in their local contexts and at scale. The two-step method includes a check on the assumptions in a program's theory of change and an assessment of the sensitivity of projected results to violations in assumptions such as program completion rates. The author provides an example of the method applied to a hypothetical metropolitan area that seeks to adopt an evidence-based training program for youth with barriers to employment.
Primary and Secondary Virtual Learning in New Zealand: Examining Barriers to Achieving Maturity.

This article describes the organisational development of virtual learning in networked rural schools in New Zealand, specifically the obstacles that e-learning clusters of rural schools face in their journey to sustainability and maturity through the lens of the Ministry’s Learning Communities Online Hand-book. Analysis of a nationwide purposeful sample identified three common barriers: a lack of a coherent vision; difficulty in sustaining necessary funding and resources; and of the need for more collaboration within and between clusters. Based on these findings, it is recommended that VLN e-learning clusters develop specific strategies to encourage greater collaboration between schools and work towards greater consistency be-tween their activities, including professional and organisational development and also of the approaches to virtual learning.
Avanço das pesquisas envolvendo Aspergillus niger e bagaço da cana-de-açúcar como fonte de carbono visando à produção de celulases: Uma análise bibliométrica

RESUMO Uma variedade de estudos tem sido realizados com o intuito de produzir celulases de baixo custo para serem utilizadas na produção de álcool de segunda geração (2G) a partir de resíduos lignocelulósicos. Dentre eles destaca-se o uso de bagaço de cana-de-açúcar como fonte de carbono para o crescimento dos microrganismos. Baseado nisso, este artigo teve como objetivo realizar uma revisão bibliométrica sobre o uso do Aspergillus niger empregando como fonte de carbono o bagaço de cana-de-açúcar, pretendendo a produção de celulase, através do estudo dos avanços científicos e tecnológicos sobre os temas. Para efetuar a revisão, recorreu-se ao banco de dados Web of Science, inserindo-se quatro palavras-chave: “Aspergillus niger”, “cellulase”, “cane” e “submerged” entre os anos de 1945 e 2018. A partir desta pesquisa, foi possível apontar os autores, periódicos e grupos de pesquisas mais importantes neste assunto. Identificou-se um aumento do número de publicações ao longo dos anos por diversos países, revelando o interesse pela produção de celulases de menor custo para geração de álcool 2G. O Brasil foi o país com o maior número de publicações sobre o tema, devido a grande capacidade de plantio de cana-de-açúcar e produção de álcool. Pela análise das redes de co-citação dos autores verificou-se a existência de uma relação dos grupos de pesquisa no Brasil, entre as cidades de Campinas, São Paulo e Curitiba e outra associação na Dinamarca, entre as cidades de Lyngby e Copenhagen.
Diagonal Recurrent Neural Network Algorithm for Extreme Short Prediction of Ship Motion

The modeling method of the ship extreme short-term motion prediction by applying neural network theory is discussed. The diagonal recurrent neural network model and algorithm of ship motion prediction is given. With the algorithm, real ship motion prediction is simulated, and the simulation method and procedure are introduced in detail. The simulation results were compared with the real ship data and those from auto-regress method and periodic chart method, which proved the algorithm to be feasible。
Simple and Efficient Approach for Achieving End-to-End Anonymous Communication

Anonymous communication, that is secure end-to-end and unlinkable, plays a critical role in protecting user privacy by preventing service providers from using message metadata to discover communication links between any two users. Techniques, such as Mix-net, DC-net, time delay, cover traffic, Secure Multiparty Computation and Private Information Retrieval techniques, can be used to achieve anonymous communication. However, the existing solutions are very complex and difficult to implement in practice. More importantly, they do not offer security against malicious adversaries who can arbitrarily deviate from normal protocol execution, e.g., refusing to participate and modifying messages exchanged in the protocol. In this paper, we propose a simple and novel approach to establishing anonymous communication, easily implementable with servers having only communication and storage related capabilities. Our approach offers stronger security guarantee against malicious adversaries without incurring a great deal of extra computation and communication costs. We formally prove the security guarantee of the proposed solution and analyze its pros and cons comparing to the existing work.
Development of Industrial Nanocomposite Permanent Magnets : A Review

Development of industrial nanocomposite permanent magnets (NCPM) is reviewed with a particular emphasis on rare-earth-iron-based materials. After a brief description of the basic concepts concerning NCPM, leading discoveries of iron-based NCPM are reviewed. The impact of recent development of high-coercivity Nd-Fe-B-Ti-C isotropic NCPM powders that are industrially produced by applying strip casting process for rapid solidification is described. Short comments on bulk NCPM and empirical efforts toward anisotropic NCPM are also included. Application aspects of the industrial NCPM are discussed.
Visual‐Interactive Preprocessing of Multivariate Time Series Data

Pre‐processing is a prerequisite to conduct effective and efficient downstream data analysis. Pre‐processing pipelines often require multiple routines to address data quality challenges and to bring the data into a usable form. For both the construction and the refinement of pre‐processing pipelines, human‐in‐the‐loop approaches are highly beneficial. This particularly applies to multivariate time series, a complex data type with multiple values developing over time. Due to the high specificity of this domain, it has not been subject to in‐depth research in visual analytics. We present a visual‐interactive approach for preprocessing multivariate time series data with the following aspects. Our approach supports analysts to carry out six core analysis tasks related to pre‐processing of multivariate time series. To support these tasks, we identify requirements to baseline toolkits that may help practitioners in their choice. We characterize the space of visualization designs for uncertainty‐aware pre‐processing and justify our decisions. Two usage scenarios demonstrate applicability of our approach, design choices, and uncertainty visualizations for the six analysis tasks. This work is one step towards strengthening the visual analytics support for data pre‐processing in general and for uncertainty‐aware pre‐processing of multivariate time series in particular.
A Systematic Approach to Predict Unbiased Branches

Sibiu, who were actively involved in implementing the simulator presented in Chapter 6, to our colleague Marius OANCEA, who has also contributed in the unbiased branch research, his results being presented in paragraph 3.2. 1, respectively to Mihai MUNTENAŞ, MSc, who brought contributions in paragraphs 3.2. 2 and 4.6. 1.
Ultrasonic array imaging for multi-layer materials in non-destructive testing applications.

Ultrasonic imaging for a multi-layer medium is a common challenge in seismology, medical diagnosis, and non-destructive testing. One application for multi-layer imaging is ultrasonic immersion test where the material under test and transducer array are immersed in water. The main imaging challenge in immersion test (or in any multi-layer medium) is that since the sound wave propagates with different speeds in different layers, the homogeneity assumption is not valid. Thereby calculating the sound travel time for the backscattered received signal is complicated. In this dissertation, we propose a new approach to model the array received signals in order to image the material under test. In the first approach, we propose a distributed reflector modeling approach to characterize the interface between water and the solid as well as any crack inside the solid test sample. This approach relies on incoherently distributed reflector modeling. A distributed reflector can be modeled as infinitely many point sources located close to each other. We use distributed reflector modeling in order to estimate the shape of the reflectors. To do so, we present our data model in a two-dimensional coordinate system, and then develop a covariance fitting based approach to parametric estimation of the shape of the interface between the two media and that of a crack inside the test material. Numerical computer simulations show the accuracy of the proposed approach. However the proposed approach is a parametric localization method which needs the repetition of the ultrasonic test. In the second approach we present a data model to use for image reconstruction of a multi-layer medium without need to repeat the ultrasonic experiment. In this ix
GENETIC DIVERSITY OF 12 CHINESE LOCAL CHICKEN BREEDS

In this study, genetic diversity of 12 Chinese indigenous chicken breeds were evaluated at the molecular level. In total, 448 individuals from the 12 Chinese breeds including Luyuan chicken (LY), Gushi chicken (GS), Tibetan chicken (TC), Baier chicken (BE), Xianju chicken (XJ), Chahua chicken (CH), Dagu chicken (DG), Beijing Fatty chicken (BF), Langshan chicken (LS), Henan Game chicken (HG), Taihe Silkies (TS) and Xiaoshan chicken (XS), were genotyped at 29 microsatellite loci. The F-statistics indices (WRIGHT, 1978), FIT, FST, and FIS were estimated using FSTAT program (GOUDET, 2002) to quantify the genetic variation within and among populations. A phylogenetic tree was constructed based on the Reynolds’ genetic distance and the Neighbor-Joining method (FELSENSTEIN, 1995). A Bayesian approach for deducing population structure from multi-locus data, as implemented in the software package structure (PRITCHARD et al. 2000), were used to assess the relationship of the chicken breeds under study.
REQUEST AWARE STRENGTH OF CHARACTER OF INDEFINITE OBJECTS

The goal should be to create a deterministic representation of probabilistic data that maximizes the grade of in conclusion-application built on deterministic data. We explore this type of determination problem poor two different computer tasks triggers and selection queries. A much better approach ought to be to design customized determination techniques that pick a determined representation which maximizes the grade of in conclusion-application. Probabilistic data may be created by automated data analysis/enrichment means of example entity resolution, information extraction, and speech processing. Rather, we produce a query-aware strategy and show its advantages over existing solutions employing a comprehensive empirical evaluation over real and artificial datasets. The legacy system may match pre-existing web programs for instance Flickr, Picasa, etc. This paper views the problem of exercising probabilistic data allowing such data to acquire stored in legacy systems that accept only deterministic input. We show way of example thresholding or top-1 selection typically useful for determination lead to suboptimal performance for such programs.