Miracle-dz/UniFAR-FTUs huggingface dataset:
Software Requirements Management

Requirements are defined as the desired set of characteristics of a product or a service. In the world of software development, it is estimated that more than half of the failures are attributed towards poor requirements management. This means that although the software functions correctly, it is not what the client requested. Modern software requirements management methodologies are available to reduce the occur-rence of such incidents. This paper performs a review on the available literature in the area while tabulating possible methods of managing requirements. It also highlights the benefits of following a proper guideline for the requirements management task. With the introduction of specific software tools for the requirements management task, better software products are now been developed with lesser resources.
Key Technologies for Security Enhancing of Payment Gateway

The secure payment gateway is necessary for ensuring the development of e-commerce. The breaking down of hash algorithm of MD5 and the like have imposed great potential security hazard on payment gateway. We have brought forward a kind of solution for enhancing the security of payment gateway and expounded the major key technique for security enhancing centering on the payment protocol which blends SSL and SET: optimize and realize AES algorithm and integrate it into SSL protocol, establish secure hash algorithm based on the optimized AES and integrate it into SSL protocol, design and implement security proxy and micro authority certificate (CA) system.
Updating citizenship? The effects of digital media use on citizenship understanding and political participation

ABSTRACT Is there a connection between increased use of digital media and changing patterns of political participation? This study tests how the use of online media for different purposes (social interaction, creative expression, online news use, social media news use) is related to three types of political participation. It examines whether mobilizing effects are partly indirect due to different understandings of citizenship (dutiful, optional, individual, collective) that may be fostered by digital media use. The study is based on a survey of a sample of the Danish population (n = 1322), including data from two online survey waves and a smartphone-based media diary that documents respondents’ social media use. Results indicate support for a new pathway to participation, but the relationship depends on whether citizens are socialized in a digital media environment.
Design and Implementation of a Hardware Assisted Security Architecture for Software Integrity Monitoring

The increasing complexity of software and hardware layers makes them likely to include vulnerabilities. Recent research has shown that subtle attacks are able to successfully exploit (through compromised peripherals performing DMA attacks for instance) vulnerabilities in low-level software, even running in the most privileged mode of the processors. Therefore, the security of such systems should not be solely based on components running on the processor. This paper describes the design and the implementation of a security architecture that is designed to securely execute integrity checks of any software running on top of this architecture. It is composed of a security hypervisor, running in the most privileged level of the processor, assisted by a trusted hardware component, autonomous and independent of the processor, regularly checking the integrity of the security hypervisor itself. The design, the implementation of this security architecture, as well as experiments showing the relevance of our approach, are detailed in this paper.
A Learning Algorithm for Change Impact Prediction

Change impact analysis (CIA) consists in predicting the impact of a code change in a software application. In this paper, the artifacts that are considered for CIA are methods of object-oriented software; the change under study is a change in the code of the method, the impact is the test methods that fail because of the change that has been performed. We propose LCIP, a learning algorithm that learns from past impacts to predict future impacts. To evaluate LCIP, we consider Java software applications that are strongly tested. We simulate 6000 changes and their actual impact through code mutations, as done in mutation testing. We find that LCIP can predict the impact with a precision of 74%, a recall of 85%, corresponding to a F-score of 64%. This shows that taking a learning perspective on change impact analysis let us achieve good precision and recall in change impact analysis.
Multi-output Galois Field Sum of Products synthesis with new quantum cascades

Galois Field Sum of Products (GFSOP) leads to efficient multi-valued reversible circuit synthesis using quantum gates. In this paper, we propose a new generalization of ternary Toffoli gate and another new generalized reversible ternary gale with discussion of their quantum realizations. Algorithms for synthesizing ternary GFSOP using quantum cascades of these gates are proposed In both the synthesis methods, 5 ternary shift operators and ternary swap gate are used We also propose quantum realizations of 5 ternary shift operators and ternary swap gate. In the cascades of the new ternary gates, local mirrors, variable ordering, and product ordering techniques are used to reduce the circuit cost. Experimental results show that the cascade of the new ternary gates is more efficient than the cascade of ternary Toffoli gates.
Recent Climate-Driven Increases in Vegetation Productivity for the Western Arctic: Evidence of an Acceleration of the Northern Terrestrial Carbon Cycle

Abstract Northern ecosystems contain much of the global reservoir of terrestrial carbon that is potentially reactive in the context of near-term climate change. Annual variability and recent trends in vegetation productivity across Alaska and northwest Canada were assessed using a satellite remote sensing–based production efficiency model and prognostic simulations of the terrestrial carbon cycle from the Terrestrial Ecosystem Model (TEM) and BIOME–BGC (BioGeoChemical Cycles) model. Evidence of a small, but widespread, positive trend in vegetation gross and net primary production (GPP and NPP) is found for the region from 1982 to 2000, coinciding with summer warming of more than 1.8°C and subsequent relaxation of cold temperature constraints to plant growth. Prognostic model simulation results were generally consistent with the remote sensing record and also indicated that an increase in soil decomposition and plant-available nitrogen with regional warming was partially responsible for the positive produc.
Psychological Models of Professional Decision Making

People are often expected to make decisions based on all of the relevant information, weighted and combined appropriately. Under many conditions, however, people use heuristic strategies that depart from this ideal. I tested the ability of two models to predict bail decisions made by judges in two courts. In both courts, a simple heuristic proved to be a better predictor of judicial decisions than a more complex model that instantiated the principles of due process. Specifically, judges were “passing the buck” because they relied on decisions made by the police, prosecution, and previous bench. Problematically, these earlier decisions were not significantly related to case characteristics. These findings have implications for the types of models researchers use to capture professional decision-making policies.
Rule-based business process modeling and execution

A process model is called rule-based if the semantics of its case data and activity flows are expressed by means of rules. Rules have been recognized before as powerful representation forms that can potentially define the semantics of data and process resources. To date, however, there is no consensus on how to link the enforcement of rules, the manipulation of data and the execution of processes. Moreover, it is witnessed that complex data and process resource descriptions in the form of a number of constraints, deduction and reaction rules lack expressivity and comprehensibility. In this paper, we set out for using process and rule set metamodels to concisely represent the semantics of case data and activity flows in business process models. In addition we show how to generate a syntactically verified and semantically validated corpus of definite Horn clauses that can be used in the execution of the modeled process.
Video frame extraction for 3D reconstruction

This paper addresses the problem of using live and pre-recorded video sources in 3D reconstruction systems. Typical photogrammetry packages use images generated from digital photography cameras such as DSLRs. 3D reconstruction algorithms require the intrinsic information to be stored in the EXIF header of a digital photograph. Live video systems don't store this information and more importantly don't produce a concise sequence of distinct frames. This paper presents an approach to using both live and pre-recorded video sources with 3D Structure from Motion systems through the implementation of a pre-processor. This approach filters the video feed with the aid of the Laplacian operator and feature-based methods to create an optimal sequence for both on-line and off-line 3D reconstruction systems.
1 Mobility and Security

We discuss the computational aspects of wide area networks, and we describe various facets of a process calculus devised to embody mobility, security, and wide area network semantics. These lecture notes are an abridged version of [8, 11, 27, 12, 13]. Part I: Wide Area Computation
Learning Deep Sigmoid Belief Networks with Data Augmentation

Deep directed generative models are developed. The multi-layered model is designed by stacking sigmoid belief networks, with sparsity-encouraging priors placed on the model parameters. Learning and inference of layer-wise model parameters are implemented in a Bayesian setting. By exploring the idea of data augmentation and introducing auxiliary P olya-Gamma variables, simple and ecient Gibbs sampling and meaneld variational Bayes (VB) inference are implemented. To address large-scale datasets, an online version of VB is also developed. Experimental results are presented for three publicly available datasets: MNIST, Caltech 101 Silhouettes and OCR letters.
Implementation of Bayesian recursive state-space Kalman filter for noise reduction of speech signal

Noise reduction of speech signals plays an important role in telecommunication systems. Various types of speech additive noise can be introduced such as babble, crowd, large city, and highway which are the main factor of degradation in perceived speech quality. There are some cases on the receiver side of telecommunication systems, where the direct value of interfering noise is not available and there is just access to noisy speech. In these cases the noise cannot be cancelled totally but it may be possible to reduce the noise in a sensible way by utilizing the statistics of the noise and speech signal. In this paper the proposed method for noise reduction is Bayesian recursive state-space Kalman filter, which is a method for estimation of a speech signal from its noisy version. It utilizes the prior probability distributions of the signal and noise processes, which are assumed to be zero-mean Gaussian processes. The function of Kalman filter is assessed for different types of noise such as babble, crowd, large city, and highway. The noise cancellation is implemented for each of aforementioned noises which their powers vary in a range of values. This method of noise reduction yields better speech perceived quality and efficient results compared to Wiener filter.
Spectral Correlation of Multicarrier Modulated Signals and Its Application for Signal Detection

Spectral correlation theory for cyclostationary time-series signals has been studied for decades. Explicit formulas of spectral correlation function for various types of analog-modulated and digital-modulated signals are already derived. In this paper, we investigate and exploit the cyclostationarity characteristics for two kinds of multicarrier modulated (MCM) signals: conventional OFDM and filter bank based multicarrier (FBMC) signals. The spectral correlation characterization of MCM signal can be described by a special linear periodic time-variant (LPTV) system. Using this LPTV description, we have derived the explicit theoretical formulas of nonconjugate and conjugate cyclic autocorrelation function (CAF) and spectral correlation function (SCF) for OFDM and FBMC signals. According to theoretical spectral analysis, Cyclostationary Signatures (CS) are artificially embedded into MCM signal and a low-complexity signature detector is, therefore, presented for detecting MCM signal. Theoretical analysis and simulation results demonstrate the efficiency and robustness of this CS detector compared to traditionary energy detector.
Effects of Sensor Properties on Power Consumption in Wireless Sensor Network

Building a wireless sensor network first of all requires the constituting nodes to be developed and available. These nodes have to met the requirements that come from the specific requirements of a given application. They might be small, cheap (energy efficient), they have to be equipped with the right sensors, the necessary computation and memory resources, and they need adequate communication facilities, joining them into wireless sensor network. Event mobility is one of the mobility types in the wireless sensor network. In this paper a study of the sensor properties which have direct effect on the power of the sensor node itself and the total power of the network. Sensor Radius, Sensor Period and Sensor Energy Cost are important properties related to the sensor, these properties have different effects on the power consumption in the network. These properties also have the effect on the number of packets received by the observer in the network.
Experiences with discriminating TCP loss using K-Means clustering

Protocols such as TCP depend on loss detection and recovery algorithms to provide a reliable data delivery service. TCP detects loss events using either retransmission timeout or receipt of duplicate acknowledgements. Since, TCP does not have any explicit knowledge about the cause of packet loss, it always treats it as a congestion indication and then adjusts sending rate conservatively to maintain fairness. This often compromises achievable throughput under wireless loss conditions. This problem can be solved by making the TCP source intelligent so that on detecting a packet loss, it will be able to distinguish what type of loss it is (a Congestion loss or a Wireless loss) and react accordingly. This paper presents an online-learning solution to discriminate wireless loss from congestion loss solely using the information available at the TCP layer and at the source only. Initial results obtained from a simulation based study show that the proposed algorithm which combines K-Means clustering approach together with heuristics is capable of classifying loss types to a higher degree of accuracy under various loss scenarios and provides significant performance improvements under high wireless loss conditions.
Expression of virulence genes by Listeria monocytogenes J0161 in natural environment

Majority of studies concerning the gene expression of Listeria monocytogenes have been done on pure culture states. Our objective was to study L.monocytogenes in a co-cultured state and to understand if microbes in their natural state of existence are different in their expression than that of the purely cultured lab grown forms. For a long period discussions have been on the expression of prfA, (which is a virulence gene regulator) in a mammalian host and its role in causing the switch from a saprophytic to pathogenic form of L.monocytogenes. We, in this paper for the first time report the expression of prfA and other virulence genes by L.monocytogenes under different extracellular conditions, and also as a pure culture biofilms, that is different from the previous reports. We also report that the expression of prfA seems to vary considerably when co-cultured with Bacillus subtilis.
The Importance of Imprinting in the Human Placenta

As a field of study, genomic imprinting has grown rapidly in the last 20 years, with a growing figure of around 100 imprinted genes known in the mouse and approximately 50 in the human. The imprinted expression of genes may be transient and highly tissue-specific, and there are potentially hundreds of other, as yet undiscovered, imprinted transcripts. The placenta is notable amongst mammalian organs for its high and prolific expression of imprinted genes. This review discusses the development of the human placenta and focuses on the function of imprinting in this organ. Imprinting is potentially a mechanism to balance parental resource allocation and it plays an important role in growth. The placenta, as the interface between mother and fetus, is central to prenatal growth control. The expression of genes subject to parental allelic expression bias has, over the years, been shown to be essential for the normal development and physiology of the placenta. In this review we also discuss the significance of genes that lack conservation of imprinting between mice and humans, genes whose imprinted expression is often placental-specific. Finally, we illustrate the importance of imprinting in the postnatal human in terms of several human imprinting disorders, with consideration of the brain as a key organ for imprinted gene expression after birth.
A Parallel Computing Hybrid Approach for Feature Selection

The ultimate goal of feature selection is to select the smallest subset of features that yields minimum generalization error from an original set of features. This effectively reduces the feature space, and thus the complexity of classifiers. Though several algorithms have been proposed, no single one outperforms all the other in all scenarios, and the problem is still an actively researched field. This paper proposes a new hybrid parallel approach to perform feature selection. The idea is to use a filter metric to reduce feature space, and then use an innovative wrapper method to search extensively for the best solution. The proposed strategy is implemented on a shared memory parallel environment to speedup the process. We evaluated its parallel performance using up to 32 cores and our results show 30 times gain in speed. To test the performance of feature selection we used five datasets from the well known NIPS challenge and were able to obtain an average score of 95.90% for all solutions.
Active Listening in a Bat Cocktail Party: Adaptive Echolocation and Flight Behaviors of Big Brown Bats, Eptesicus fuscus, Foraging in a Cluttered Acoustic Environment

In their natural environment, big brown bats forage for small insects in open spaces, as well as in vegetation and in the presence of acoustic clutter. While searching and hunting for prey, bats experience sonar interference, not only from densely cluttered environments, but also from calls of conspecifics foraging in close proximity. Previous work has shown that when two bats compete for a single prey item in a relatively open environment, one of the bats may go silent for extended periods of time, which can serve to minimize sonar interference between conspecifics. Additionally, pairs of big brown bats have been shown to adjust frequency characteristics of their vocalizations to avoid acoustic interference in echo processing. In this study, we extended previous work by examining how the presence of conspecifics and environmental clutter influence the bat's echolocation behavior. By recording multichannel audio and video data of bats engaged in insect capture in open and cluttered spaces, we quantified the bats' vocal and flight behaviors. Big brown bats flew individually and in pairs in an open and cluttered room, and the results of this study shed light on the different strategies that this species employs to negotiate a complex and dynamic environment.
Statistical evaluation of synchronous spike patterns extracted by frequent item set mining

We recently proposed frequent itemset mining (FIM) as a method to perform an optimized search for patterns of synchronous spikes (item sets) in massively parallel spike trains. This search outputs the occurrence count (support) of individual patterns that are not trivially explained by the counts of any superset (closed frequent item sets). The number of patterns found by FIM makes direct statistical tests infeasible due to severe multiple testing. To overcome this issue, we proposed to test the significance not of individual patterns, but instead of their signatures, defined as the pairs of pattern size z and support c. Here, we derive in detail a statistical test for the significance of the signatures under the null hypothesis of full independence (pattern spectrum filtering, PSF) by means of surrogate data. As a result, injected spike patterns that mimic assembly activity are well detected, yielding a low false negative rate. However, this approach is prone to additionally classify patterns resulting from chance overlap of real assembly activity and background spiking as significant. These patterns represent false positives with respect to the null hypothesis of having one assembly of given signature embedded in otherwise independent spiking activity. We propose the additional method of pattern set reduction (PSR) to remove these false positives by conditional filtering. By employing stochastic simulations of parallel spike trains with correlated activity in form of injected spike synchrony in subsets of the neurons, we demonstrate for a range of parameter settings that the analysis scheme composed of FIM, PSF and PSR allows to reliably detect active assemblies in massively parallel spike trains.
Subconductance block of single mechanosensitive ion channels in skeletal muscle fibers by aminoglycoside antibiotics

The activity of single mechanosensitive channels was recorded from cell- attached patches on acutely isolated skeletal muscle fibers from the mouse. The experiments were designed to investigate the mechanism of channel block produced by externally applied aminoglycoside antibiotics. Neomycin and other aminoglycosides reduced the amplitude of the single-channel current at negative membrane potentials. The block was concentration-dependent, with a half-maximal concentration of approximately 200 microM. At high drug concentrations, however, block was incomplete with roughly one third of the current remaining unblocked. Neomycin also caused the channel to fluctuate between the open state and a subconductance level that was also roughly one third the amplitude of the fully open level. An analysis of the kinetics of the subconductance fluctuations was consistent with a bimolecular reaction between an aminoglycoside molecule and the open channel (kon = approximately 1 x 10(6) M-1s-1 and koff = approximately 400 s-1 at -60 mV). Increasing the external pH reduced both the rapid block of the open channel and the frequency of the subconductance fluctuations, as if both blocking actions were produced by a single active drug species with a pKa = approximately 7.5. The results are interpreted in terms of a mechanism in which an aminoglycoside molecule partially occludes ion flow through the channel pore.