Miracle-dz/UniFAR-FTUs huggingface dataset:
An extended GCD algorithm for parametric univariate polynomials and application to parametric smith normal form

An extended greatest common divisor (GCD) algorithm for parametric univariate polynomials is presented in this paper. This algorithm computes not only the GCD of parametric univariate polynomials in each constructible set but also the corresponding representation coefficients (or multipliers) for the GCD expressed as a linear combination of these parametric univariate polynomials. The key idea of our algorithm is that for non-parametric case the GCD of arbitrary finite number of univariate polynomials can be obtained by computing the minimal Gröbner basis of the ideal generated by those polynomials. But instead of computing the Gröbner basis of the ideal generated by those polynomials directly, we construct a special module by adding the unit vectors which can record the representation coefficients, then obtain the GCD and representation coefficients by computing a Gröbner basis of the module. This method can be naturally generalized to the parametric case because of the comprehensive Gröbner systems for modules. As a consequence, we obtain an extended GCD algorithm for parametric univariate polynomials. More importantly, we apply the proposed extended GCD algorithm to the computation of Smith normal form, and give the first algorithm for reducing a univariate polynomial matrix with parameters to its Smith normal form.
SAUCE: Asset Libraries of the Future

Storage and retrieval of production assets is vital for every modern VFX and animation facility. From the volume of assets being stored to the constantly changing variety and richness of the asset data, efficiently storing, indexing, finding and retrieving the assets you want is a growing challenge. This paper discusses some of the requirements of modern asset storage systems for VFX and animation, introducing two systems that were built to address these challenges as part of the collaborative EU funded “SAUCE” project; DNEG’s search and retrieval framework, and Foundry’s back-end asset storage. It also presents example use cases of the asset library from Filmakademie’s experiments in virtual production, demonstrating more artist focused and task centered systems that enable greater asset re-use.
AIS Electronic Library (AISeL) AIS Electronic An mHealth Application to Promote Diabetes Self-care Behavior An mHealth Application to Promote Diabetes Self-care Behavior among Medically Underserved Population among Medically Underserved Population

Diabetes is a chronic illness that causes serious health complications such as kidney failure, limb amputations, and often leads to premature death. Adoption of self-care behavior among diabetes patients is known to improve their health conditions and quality of life. Medically underserved populations (MUP) are disproportionately affected by diabetes. In this research we use a design science approach to develop an mHealth app to promote diabetes self-care behavior among MUPs. Using design theories on behavior change and user-centered design we articulated five key design principles. A mobile app based on these principles and AADE7 self-care behavior framework has been implemented using an Android system. We are currently in the process of evaluating its effectiveness. Our research contributes to the discourse on design for behavior change and illustrates the effectiveness of mHealth apps in promoting healthy lifestyles.
Keep it Unsupervised: Horizontal Attacks Meet Deep Learning

To mitigate side-channel attacks, real-world implementations of public-key cryptosystems adopt state-of-the-art countermeasures based on randomization of the private or ephemeral keys. Usually, for each private key operation, a “scalar blinding” is performed using 32 or 64 randomly generated bits. Nevertheless, horizontal attacks based on a single trace still pose serious threats to protected ECC or RSA implementations. If the secrets learned through a single-trace attack contain too many wrong (or noisy) bits, the cryptanalysis methods for recovering remaining bits become impractical due to time and computational constraints. This paper proposes a deep learning-based framework to iteratively correct partially correct private keys resulting from a clustering-based horizontal attack. By testing the trained network on scalar multiplication (or exponentiation) traces, we demonstrate that a deep neural network can significantly reduce the number of wrong bits from randomized scalars (or exponents). When a simple horizontal attack can recover around 52% of attacked multiple private key bits, the proposed iterative framework improves the private key accuracy to above 90% on average and to 100% for at least one of the attacked keys. Our attack model remains fully unsupervised and excludes the need to know where the error or noisy bits are located in each separate randomized private key.
Scale Features of a Network Echo Mechanism: Case Study for the Different Internet Paths

We have investigated dynamics of the Internet performance through the assessment of scaling features of a network ICMP echo mechanism or pinging. Time series of round-trip times (RTT) from the host computer to 5 destination hosts and back, recorded during three consecutive days and nights, have been used. To assess correlation and scaling features of network echo mechanism, we used method of detrended fluctuation analysis (DFA) for RTT data sets. It was shown that for different, 10 minute long periods of day and night observations, RTT data sets mostly fluctuate within a narrow range, though sometimes we observe strong sharp spikes. RTT variations mostly reveal persistent behavior. DFA fluctuation curves often are characterized by crossovers indication stronger or lesser changes in the dynamics of network performance. Distribution function of DFA scaling exponents of considered RTT time series mostly was asymmetric with long tail on the right hand side. Dynamical changes occurring in the scaling features of Internet network as assessed by RTT fluctuations do not depend on the location of the host and destination nodes. Larger delays in round-trip time responses make the scaling behavior of the RTT series complicated and strongly influence their long range correlation features.
Secure Computation over Lattices and Elliptic Curves

Traditional threshold cryptosystems have decentralized core cryptographic primitives like key generation, decryption and signatures. Most threshold cryptosystems, however, rely on special purpose protocols that cannot easily be integrated into more complex multiparty protocols. In this work, we design and implement decentralized versions of lattice-based and elliptic-curve-based public-key cryptoystems using generic secure multiparty computation (MPC) protocols. These are standard cryptosystems, so we introduce no additional work for encrypting devices and no new assumptions beyond those of the generic MPC framework. Both cryptosystems are also additively homomorphic, which allows for secure additions directly on ciphertexts. By using generic MPC techniques, our multi-party decryption protocols compute secret-shares of the plaintext, whereas most special-purpose cryptosystems either do not support decryption or must reveal the decryptions in the clear. Our method allows complex functions to be securely evaluated after decryption, revealing only the results of the functions and not the plaintexts themselves. To improve performance, we present a novel oblivious elliptic curve multiplication protocol and a new noise-masking technique which may be of independent interest. We implemented our protocols using the SCALE-MAMBA secure multiparty computation platform, which provides security against malicious adversaries and supports arbitrary numbers of participants.
A Taxonomy of Software Delivery Performance Profiles: A Taxonomy of Software Delivery Performance Profiles: Investigating the Effects of DevOps Practices Investigating the Effects of DevOps Practices

This research develops a taxonomy of Software Delivery Performance Profiles for DevOps development settings. We base the underlying Software Delivery Performance measure on the application of the Economic Order Quantity (EOQ) model to software development. Consistent with the objectives of both, development and operations departments, the measure includes attributes for throughput (release frequency and lead-time to delivery) and for stability (mean time to restore). Hierarchical cluster analysis on a global sample of 7,522 DevOps professionals results in three distinct Software Delivery Performance Profiles; in addition, the results indicate that the measures for throughput and stability move in tandem. Further analysis will show how the use of individual DevOps practices impacts Performance Profiles of development settings. When completed, the study will support the utility of DevOps and the effectiveness of individual DevOps practices.
Proxy Re-Encryption for Accelerator Confidentiality in FPGA-Accelerated Cloud

—FPGAs offer many-fold acceleration to various application domains, and have become a part of cloud-based computation. However, their cloud-use introduce Cloud Service Providers (CSPs) as trusted parties, who can access the hardware designs in plaintext. Therefore, the intellectual property of hardware designers is not protected against a dishonest cloud. In this paper, we propose a scheme for the conﬁdentiality of accelerators on cloud, without limiting CSP to maintain their resources freely. Our proposed scheme is based on Proxy Re-Encryption (PRE) which allows the developers to upload their accelerators to the CSPs under encryption. The CSPs cannot decrypt them; however, alter the encryption that allows the target FPGAs they pick to decrypt. In addition, our scheme allows metering the use of accelerators.
SoK: A Taxonomy of Cryptocurrency Wallets

The primary function of a cryptocurrency is money transfer between individuals. The wallet is the software that facilitates such transfers. Wallets are nowadays ubiquitous in the cryptocurrency space and a cryptocurrency is usually supported by many wallets. Despite that, the functionality of wallets has never been formally defined. Additionally, the mechanisms employed by the many wallets in the wild remain hidden in their respective codebases. In this work we provide the first definition of a cryptocurrency wallet, which we model as a client to a server, or set of servers. We provide a distinction of wallets in various categories, based on whether they work for transparent or private cryptocurrencies, what trust assumptions they require, their performance and their communication overhead. For each type of wallet we provide a description of its client and server protocols. Additionally, we explore superlight wallets and describe their difference to superlight clients that have appeared in recent literature. We demonstrate how new wallet protocols can be produced by combining concepts from existing protocols. Finally we evaluate the performance and security characteristics of all wallet protocols and compare them.
Water Holding Capacity of Hemicelluloses from Fruits, Vegetables and Wheat Bran

The water-holding capacities of freeze-dried bean, cabbage, lettuce, onion, peach, pear, pumpkin, tomato, wheat bran and their hemicelluloses were determined by the centrifugation method. Cabbage had the greatest water-holding capacity at 35.8g of water per gram of dry food; bran the lowest at 5.2g water per gram. The waterholding capacity (WHC) of the hemicelluloses from fruits and vegetables varied considerably from 3.3 g/g in beans to 12.0 g/g from cabbage. Hemicelluloses extracted from wheat bran showed a marked increase in WHC from 5.2 g/g of water per gram of‘whole’bran to 22.8 g/g for hemicellulose. This decreased to 15.3g of water bound by hemicellulose that had been extracted after chlorite delignification. WHC of hemicelluloses from wheat bran and cabbage may account for their ability to increase stool weight.
Understanding Employees' ISP Compliance from The Perspective of Emotion-Focused Coping Approaches

Various theories from criminology and psychology have been utilized to understand employees’ information security policy (ISP) compliance. However, the emotional perspective is less frequently studied. Drawing upon the perspective of emotion-focused coping (EFC), especially in forms of inward and outward EFC strategies, we explain people’s ISP compliance behaviors through their coping mechanisms on negative emotional reactions to security-related stress (SRS). Drawing upon D’Arcy and Teh (2019) and Liang et al. (2019), we propose that inward EFC is helpful to mitigate the SRS-aroused fatigue while outward EFC is helpful to mitigate the SRS-aroused frustration, and that outward EFC amplifies ISP compliance intention whereas inward EFC reduces ISP compliance. This study highlights that other than the cognitive rationalization (i.e., inward EFC), direct emotion mitigating mechanism (i.e., outward EFC) is another driving force when people cope with SRS. It is important to incorporate both inward and outward EFC strategies into employee stress management.
Identity identification and management in the internet of things

Henceforth, users agreed on the necessity of continuous Internet connection independently of the place, the manner, and the time. Nowadays, several elite services are accessible by people over the Internet of Things (IoT), which is a heterogeneous network defined by machine-to-machine communication. Despite the fact that the devices are used to establish the communication, the users can be considered as the actual producers of input data and consumers of the output data. Consequently, the users should be viewed as a smart object in IoT; therefore, user identification, authentication, authorization are required. However, the user identification process is too complicated because the users are worried to share their confidential and private data. on the other hand, this private data should be used by some of their devices. Accordingly, an equitable mechanism to identify users and manage their identities is necessary. In addition, the user plays an extreme important role in the establishment of rules needed for identity identification and in ensuring the continuity of receptive services.The main purpose of this paper is to develop a new framework for Identity Management System (IdMS) for IoT. The primary contributions of this paper are: the proposition of a device recognition algorithm for user identification, the proposition of a new format for the identifier, and a theoretical framework for IdMS
Continuous integration, delivery, and deployment for scientific workflows in Orlando Tools

The paper addresses issues of continuous integration in the development of scientific applications based on workflows (special case of distributed applied software packages) for heterogeneous computing environments. The preparation and carrying out of scientific computational experiments are often accompanied by intensive software modification. Thus, there is a need for the following stages: building, testing, debugging, and installation new versions of software in heterogeneous nodes of environment. These stages may take longer time overheads than computations themselves. The solution to this challenge lies in the use of tools for continuous integration of software. However, such tools require deep integration with the tools for the workflow development because of scientific workflow specifics. To this end, the paper describes the combination of the authors Orlando Tools framework for the development and use packages with the GitLab system that is widely used for continuous integration. Such combination significantly reduces the complexity of software continuous when developing and using packages.
Large-scale Analysis of Free-Text Data for Mental Health Surveillance with Topic Modelling

Autism spectrum disorder (ASD) affects 1 in 59 children in the US and costs the US economy $66 billion annually. The Center for Disease Control and Prevention (CDC) has collected a large set of EHR as part of surveillance in the US. In Arizona, the dataset contains 4480 EHR with 10 million free text tokens over ten years. It contains detailed descriptions of children with ASD-like behaviors. While the knowledge about and the diagnostic criteria of ASD have evolved, the data collected from earlier years have not been re-evaluated. To more efficiently leverage this data and uncover causes for the increase in ASD prevalence observed in epidemiological surveillance, we use Latent Dirichlet Allocation (LDA) to analyze the content of the text data automatically. Preliminary results suggest LDA can model topics in EHR content and show variations in content that are consistent with changes in the data collection effort.
A Pilot Study on Designing a Data & AI Performance Canvas

This article addresses the question of how to construct performance goals for data & AI products. We outline a design science research approach to develop an artifact that facilitates the definition of such performance goals. Furthermore, we describe a prototype of the artifact developed in a pilot study. The data & AI performance canvas is a visual tool depicting general performance categories that help to frame the collaborative construction of performance targets with different stakeholders. Based on the findings of this pilot study, we derive a substantiated problem definition as basis for a more detailed study in the future
Model transformations carried by the traceability framework for enterprises in software industry

The developmental paradigm in the software engineering industry has transformed from a programming-oriented approach to model-oriented development. At present, model-based development is becoming an emerging method for enterprises for constructing software systems and services most proficiently. In Capability Maturity Model Integration (CMMI) Level 2, i.e., Managed, we need to sustain the bi-directional trace of the transformed models for the administration of user requirements and demands. This goal is achieved by the organization after applying the particular practices suggested by CMMI level 2 process area of Requirements Management (RM). It is very challenging for software developers and testers to maintain trace, particularly during the evaluation and upgrading phases of development. In our previous research work, we proposed a traceability framework for model-based development of applications for software enterprises. This work is the extension of our previously presented research work in which we have anticipated the meta-model transformations according to the Software Development Life Cycle (SDLC). These meta-models are capable of maintaining the trace information through relations. The proposed technique is also verified using a generalized illustration of an application. This transformation practice will give a foundation to software designers to maintain traceability links in model-driven development
Sofosbuvir and Ribavirin with or Without Pegylated-Interferon in Hepatitis C Virus Genotype-2 or -3 Infections: A Systematic Review and Meta-Analysis

Background: Direct-acting antiviral agents (DAAs) have changed the treatment landscape of hepatitis C virus (HCV) infection. Sofosbuvir (SOF), as a DAA inhibiting HCV NS5B polymerase, has found a remarkable contribution to the treatment regimens of HCV genotype-2 (HCV-2) and -3 infections. Objectives: In this meta-analysis, we aimed to evaluate the eﬃcacy of the combination of SOF and Ribavirin (RBV) with or without pegylated-interferon (PegIFN) in the treatment of HCV-2 and -3 infections. Methods: In this meta-analysis, we searched electronic databases including PubMed, Scopus, ScienceDirect, and Web of Science us-ingappropriateandrelevantkeywords. Basedontheresultsof theheterogeneitytest(chi-squaredandI-squared),ﬁxed-orrandom-eﬀects models were used to calculate the pooled sustained virological response (SVR) rates. Results: Afterremovingduplicatesandscreeningof 1408articles,16studieswereincludedinthequantitativesynthesis. Thepooled SVRratescalculatedforthetreatmentof patientssuﬀeringHCV-2infectionwere92% (95% CI:87% -96%)usingtheSOF+RBVregimen for 12 weeks and 95% (95% CI: 85% - 100%) using the SOF + RBV + PegIFN regimen for 12 weeks. The pooled SVR calculated for the treatment of patients suﬀering HCV-3 infection was 55% (95% CI: 44% - 66%) using the SOF + RBV regimen for 12 weeks, 81% (95% CI: 72% - 88%) using the SOF + RBV regimen for 24 weeks, and 93% (95% CI: 85% - 99%) using the SOF + RBV + PegIFN regimen for 12 weeks. Conclusions: The combination of SOF and RBV with or without PegIFN for 12 weeks is highly eﬃcacious (> 90%) for the treatment of patients with HCV-2 infection. However, for the treatment of patients with HCV-3 infection only 12 weeks of SOF + PegIFN + RBV would result in > 90% treatment success.
Comparison of sensitivity of a Campy-Cefex dilution method and PCR in detecting Campylobacter in broilers

The aim of the study was to compare the sensitivity of a Campy-Cefex dilution method and PCR for detecting Campylobacter in broilers and to see if any traces of the bacteria were to be found by these methods during winter when the bacteria is not detectable by conventional microbiological methods. The results of our studies done in 2004 and 2005 were compared with data from 2001-2003 soon after a national surveillance of Campylobacter spp. in broilers was initiated. Faecal samples from 607 broiler flocks were pooled, 10 samples per pool, and diluted in saline for the Campy-Cefex direct plating dilution method and PCR. The PCR amplification was performed in a Peltier Thermal Cycler and the primers used were C412F and C1288R. A total of 742 pooled caecal samples were collected at slaughter. Samples from each pool of 40 caeca were diluted in saline for the Campy-Cefex direct plating dilution method. The PCR method proved to be more sensitive than the Campy-Cefex method but still did not detect any traces of Campylobacter during winter. A comparison of the results from 2001-2003 with the results from 2004-2005 indicates that the percentage of positive flocks had diminished. The study underlines the importance of using sensitive methods for detecting Campylobacter spp. in order to minimize the risk of human exposure. The finding that the sensitive PCR method was not able to detect Campylobacter during winter suggests that the seasonal pattern of campylobacteriosis is due to a new vector in the spring which carries the bacterium.
Cyber Risk Assessment and Mitigation Using Logit and Probit Models for DDoS attacks

Hackers have been employing Distributed Denial of Service (DDoS) attacks at an unprecedented rate in recent times. In 2018, a 37% rise in such DDoS attacks, wherein traffic reached a peak size of 300 Gbps per attack was alarming. DDoS attacks hinder a business by preventing legitimate customers from accessing the firm’s cyber resources (e.g. website, cloud services, streaming quality , etc.). In this study, we aim to assess and mitigate cyber-risk by computing the probability of such DDoS attacks occurring and expected losses associated with them. We use logit and probit models along with standard distribution fitting methods to ascertain the aforesaid questions. Subsequently, we also suggest ways to mitigate cyber-risk resulting due to DDoS attacks by accepting, reducing or passing it. Our study aims to aid CTOs in deciding the best strategy to handle cyber-risk due to DDoS attacks.