Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of the Q Method as a Public Engagement Tool in Examining the Preferences of Residents in Metal Mining Areas

In this study, we describe and present an evaluation of how the Q Method was used to engage members of local communities, to examine how those living in former metal mining landscapes value their heritage and understand their preferences for the long-term management of mine waste. The evaluation focused on the participants' experiences, thoughts and views of the Q Method, as a method of collecting individual preferences. The Q Method is for discourse analysis, allowing a systematic analysis of the subjective perspectives of participants. This paper presents a small-scale evaluation of the Q Method. The results indicate that although this method is time-consuming (both for the researchers and for the participants) and demanding, it is a suitable and successful engagement strategy. The Q Method helped the participants feel that their opinion was being sought and valued, and allowed them to express their views on mining heritage in the context of their lives. The method was also a valuable tool in challenging the participants' views and it reinforced the complexity of the decision-making process.
Ordinary Least Squares Estimate of the Fractional Di erencing Parameter Using Wavelets as Derived from Smoothing Kernels

This paper develops a consistent OLS estimate of a fractionally integrated processes' di erencing parameter, using continuous wavelet theory as constructed from smoothing kernels. We show that a log-log linear relationship exists between the variance of the wavelet coe cient and the level at which the fractionally integrated processes is smoothed. This linear relationship occurs because the self-simularity property of the fractionally integrated process and the self-similarity of the wavelet causes the smoothing level to continually appear in the wavelet transformation. Since the wavelet coe cient can be interpreted as the kth order details of the series at some level of smoothing, we also show that the above log-log relationship can be derived from the variance of the 1st order derivative of the time series smoothed by a kernel that is well localized in both time and frequency space. Lastly, we derive the asymptotic biasness and variance of the OLS estimate and test our consistent estimate with a number of Monte Carlo experiments.
The EM Method in a Probabilistic Wavelet-Based MRI Denoising

Human body heat emission and others external causes can interfere in magnetic resonance image acquisition and produce noise. In this kind of images, the noise, when no signal is present, is Rayleigh distributed and its wavelet coefficients can be approximately modeled by a Gaussian distribution. Noiseless magnetic resonance images can be modeled by a Laplacian distribution in the wavelet domain. This paper proposes a new magnetic resonance image denoising method to solve this fact. This method performs shrinkage of wavelet coefficients based on the conditioned probability of being noise or detail. The parameters involved in this filtering approach are calculated by means of the expectation maximization (EM) method, which avoids the need to use an estimator of noise variance. The efficiency of the proposed filter is studied and compared with other important filtering techniques, such as Nowak's, Donoho-Johnstone's, Awate-Whitaker's, and nonlocal means filters, in different 2D and 3D images.
Adaptive Acceleration of Sparse Coding via Matrix Factorization

Sparse coding remains a core building block in many data analysis and machine learning pipelines. Typically it is solved by relying on generic optimization techniques, that are optimal in the class of first-order methods for non-smooth, convex functions, such as the Iterative Soft Thresholding Algorithm and its accelerated version (ISTA, FISTA). However, these methods don’t exploit the particular structure of the problem at hand nor the input data distribution. An acceleration using neural networks was proposed in [12], coined LISTA, which showed empirically that one could achieve high quality estimates with few iterations by modifying the parameters of the proximal splitting appropriately. In this paper we study the reasons for such acceleration. Our mathematical analysis reveals that it is related to a specific matrix factorization of the Gram matrix of the dictionary, in which unitary transformations leverage near diagonalisation with small perturbations of the `1 norm. When this factorization succeeds, we prove that the resulting splitting algorithm enjoys an improved convergence bound with respect to the non-adaptive version. Moreover, our analysis also shows that conditions for acceleration occur mostly at the beginning of the iterative process, consistent with numerical experiments.
A modified direct torque control (DTC) for induction motor sensorless drive

Direct torque control (DTC) is known to produce a quick and robust response in AC drives. However, during steady state, notable torque, flux and current pulsations occur. They are reflected in speed estimation, speed response and also in increased acoustical noise. The paper introduces a new direct torque and flux control based on space vector modulation (DTC-SVM) for induction motor sensorless drives. It is able to reduce the acoustical noise, the torque, flux, current and speed pulsations during steady state. The DTC transient merits are preserved, while better quality steady state performance is produced in sensorless implementation for a wide speed range. The flux and torque estimator is presented and an improved voltage-current model speed observer is introduced. The proposed control topologies, simulations, implementation data and test results with DTC and DTC-SVM are given and discussed. It is concluded that the proposed control topology produces better results for steady state operation than the classical DTC.
Rare Association Rule Mining for Network Intrusion Detection

In this paper, we propose a new practical association rule mining algorithm for anomaly detection in Intrusion Detection System (IDS). First, with a view of anomaly cases being relatively rarely occurred in network packet database, we define a rare association rule among infrequent itemsets rather than the traditional association rule mining method. And then, we discuss an interest measure to catch differences between interesting relations and uninteresting ones, and what interest there is, and develop a hash based rare association rule mining algorithm for finding rare, but useful anomaly patterns to user. Finally, we define a quantitative association rule in relational database, propose a practical algorithm to mine rare association rules from network packet database, and show advantages of it giving a concrete example. Our algorithm can be applied to fields need to mine hidden patterns which are rare, but valuable, like IDS, and it is based on hashing method among infrequent itemsets, so that it has obvious advantages of speed and memory space limitation problems over the traditional association rule mining algorithms. Keywords: rare association mining algorithm, infrequent itemsets, quantitative association rule, network intrusion detection system, anomaly detection
Model sensitivity to North Atlantic freshwater forcing at 8.2 ka

Abstract. We compared four simulations of the 8.2 ka event to assess climate model sensitivity and skill in responding to North Atlantic freshwater perturbations. All of the simulations used the same freshwater forcing, 2.5 Sv for one year, applied to either the Hudson Bay (northeastern Canada) or Labrador Sea (between Canada's Labrador coast and Greenland). This freshwater pulse induced a decadal-mean slowdown of 10–25% in the Atlantic Meridional Overturning Circulation (AMOC) of the models and caused a large-scale pattern of climate anomalies that matched proxy evidence for cooling in the Northern Hemisphere and a southward shift of the Intertropical Convergence Zone. The multi-model ensemble generated temperature anomalies that were just half as large as those from quantitative proxy reconstructions, however. Also, the duration of AMOC and climate anomalies in three of the simulations was only several decades, significantly shorter than the duration of ~150 yr in the paleoclimate record. Possible reasons for these discrepancies include incorrect representation of the early Holocene climate and ocean state in the North Atlantic and uncertainties in the freshwater forcing estimates.
Embedded DRAM: Technology platform for the Blue Gene/L chip

The Blue Gene®/L chip is a technological tour de force that embodies the system-on-a-chip concept in its entirety. This paper outlines the salient features of this 130-nm complementary metal oxide semiconductor (CMOS) technology, including the IBM unique embedded dynamic random access memory (DRAM) technology. Crucial to the execution of Blue Gene/L is the simultaneous instantiation of multiple PowerPC® cores, high-performance static random access memory (SRAM), DRAM, and several other logic design blocks on a single-platform technology. The IBM embedded DRAM platform allows this seamless integration without compromising performance, reliability, or yield. We discuss the process architecture, the key parameters of the logic components used in the processor cores and other logic design blocks, the SRAM features used in the L2 cache, and the embedded DRAM that forms the L3 cache. We also discuss the evolution of embedded DRAM technology into a higher-performance space in the 90-nm and 65-nm nodes and the potential for dynamic memory to improve overall memory subsystem performance.
Task Grain Scheduling for Hypervisor-Based Embedded System

The emergence of functional embedded systems such as cell-phones and digital appliances brought up a new issue, building a system supporting both real-time and rich services. One of the solutions is leveraging a hypervisor to integrate an RTOS and a commodity OS into a single device. However, this approach induces the limitation of application deployment; all the high priority tasks should reside in the RTOS. In this paper, we propose a task grain scheduling for a real-time hypervisor, which enables a flexible application deployment between an RTOS and a commodity OS. We constructed a prototype system with an existing hypervisor, an RTOS, and a commodity OS. We measured some basic overheads, and fixed some tasks which were missing their deadlines using the task grain scheduling to meet their deadlines. The overheads were small and the task grain scheduling achieved a flexible real-time scheduling for the hypervisor based system.
Analyzing Mixed Event Triggered / Time Triggered Systems

In today’s distributed embedded hard real-time systems there is an ongoing trend to move from traditional event triggered systems to time triggered systems, mainly driven by the unpredictability of communication media like CAN. However, many systems do not fit into this strict periodic framework due to sporadic triggering of tasks. Therefore, on task level it is common to mix event and time triggered systems, supported by a mixed real-time operating system. This mixed application cause problems in the verification of complex time properties, because traditional timing analysis over-approximates the time consumed by the time triggered parts. In this paper we present an analysis method for the event triggered part of mixed event triggered and time triggered systems. An accurate response time calculation investigates all necessary critical instants and we show the benefit of this analysis in comparison to the traditional calculation scheme. Furthermore, we present an adaptation of this method in order to calculate the response time of event triggered messages on time triggered or mixed bus systems, like FlexRay or TTCAN.
PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments

PAL2NAL is a web server that constructs a multiple codon alignment from the corresponding aligned protein sequences. Such codon alignments can be used to evaluate the type and rate of nucleotide substitutions in coding DNA for a wide range of evolutionary analyses, such as the identification of levels of selective constraint acting on genes, or to perform DNA-based phylogenetic studies. The server takes a protein sequence alignment and the corresponding DNA sequences as input. In contrast to other existing applications, this server is able to construct codon alignments even if the input DNA sequence has mismatches with the input protein sequence, or contains untranslated regions and polyA tails. The server can also deal with frame shifts and inframe stop codons in the input models, and is thus suitable for the analysis of pseudogenes. Another distinct feature is that the user can specify a subregion of the input alignment in order to specifically analyze functional domains or exons of interest. The PAL2NAL server is available at .
A Method for Testing Software Systems Based on State Design Pattern Using Symbolic Execution

The paper reports a new testing method working with state pattern designed software systems. The tests are performed in terms of symbolic execution aiming to identify conditions and values of some input parameters that violate assertions at runtime. The state based architecture of such systems allows a direct mapping of the methods to the transitions of the underlying finite state machine (FSM). In order to identify the methods that contain failing assertions, the Java Path Finder Symbolic Execution framework extension (JPF-SE) is used for an out of context execution of each method. We propose a new algorithm to compute a transition path from the initial state of the system to each faulty transition. The computation is carried out using a backward traversal scheme of the FSM support graph where the JPF-SE symbolically executes each transition of the path. The transition execution performed by JPF-SE yields to the backward propagation of the conditions imposed on the input parameters. The overall capabilities of the proposed algorithm are illustrated with an example.
The Question of Land Access and Ownership by Women in Cameroon: A Case Study of the Bakweri Women

ABSTRACT This article reports on a study that examined the Bakweri women and the land question in Cameroon in terms of access and ownership. This is in view of the observed deprivation of women from land ownership due to socio-cultural practice resulting from the patriarchy that dominates most of Cameroon. This is so in spite of increased involvement of women in food crop production. The question of the Bakweri women and their access to and ownership of land is significant given the fact that Cameroon has a National Gender Policy which recognises the problem, but does not provide a solution. The focus of the article was also necessitated by the fact that extant studies on women and land tenure in Cameroon were done in other parts of the country with little or nothing on the Bakweris in the Fako Division of the South West Region. Therefore, in seeking to address this gap, the study interrogated whether or not the Bakweri women were recipients of land or were only observers in the land distribution to the indigenes. The study used a qualitative exploratory approach whereby both observations and in-depth interviews were used for data collection from purposively sampled Bakweri women and village councillors in three villages that recently acquired land from the government.
Local-spectrum-based distinction between handwritten and machine-printed characters

In this paper, we propose a method to distinguish between handwritten and machine-printed characters with no need to locate character or text-line positions. We transform a local region in a document image into frequency domain to extract feature values including fluctuations caused by handwriting. We feed the feature values to an optimized multilayer perceptron (MLP) to get likelihood of handwriting. We call this method the spectrum-domain local fluctuation detection (SDLFD) method. Experimental results show that our method distinguishes handwritten characters from machine-printed ones with no need of text-line position information. We also found that the scheme is robust against the change in scanning resolution.
SpiderNet: An interaction tool for predicting malicious web pages

Malicious code injection poses a serious security issue over the Internet or over the Web application. In malicious code injection attacks, hackers can take advantage of defectively coded Web application software to initiate malicious code into the organization's systems and network. The vulnerability persevere when a Web application do not properly sanitize the data entered by the user on a Web page. Attacker can steal confidential data of the user like password, pin number, and etc., these attacks resulting defeat of market value of the organization. This research work is model the malicious Web page prediction as a classification task and provides a convenient solution by using a powerful machine learning technique such as Support Vector Machine (SVM), Extreme Learning Machine (ELM). The main aim of this research work is to predict the type of the malicious attack like Redirect, Script injection and XSS using the machine learning approaches; in this case, the prediction time is taken into consideration. The supervised learning algorithms such as SVM and ELM are employed for implementing the prediction model.
Image analysis in medical imaging: recent advances in selected examples

Medical imaging has developed into one of the most important fields within scientific imaging due to the rapid and continuing progress in computerised medical image visualisation and advances in analysis methods and computer-aided diagnosis. Several research applications are selected to illustrate the advances in image analysis algorithms and visualisation. Recent results, including previously unpublished data, are presented to illustrate the challenges and ongoing developments.
High But Not Low Probability of Gain Elicits a Positive Feeling Leading to the Framing Effect

Human risky decision-making is known to be highly susceptible to profit-motivated responses elicited by the way in which options are framed. In fact, studies investigating the framing effect have shown that the choice between sure and risky options depends on how these options are presented. Interestingly, the probability of gain of the risky option has been highlighted as one of the main factors causing variations in susceptibility to the framing effect. However, while it has been shown that high probabilities of gain of the risky option systematically lead to framing bias, questions remain about the influence of low probabilities of gain. Therefore, the first aim of this paper was to clarify the respective roles of high and low probabilities of gain in the framing effect. Due to the difference between studies using a within- or between-subjects design, we conducted a first study investigating the respective roles of these designs. For both designs, we showed that trials with a high probability of gain led to the framing effect whereas those with a low probability did not. Second, as emotions are known to play a key role in the framing effect, we sought to determine whether they are responsible for such a debiasing effect of the low probability of gain. Our second study thus investigated the relationship between emotion and the framing effect depending on high and low probabilities. Our results revealed that positive emotion was related to risk-seeking in the loss frame, but only for trials with a high probability of gain. Taken together, these results support the interpretation that low probabilities of gain suppress the framing effect because they prevent the positive emotion of gain anticipation.
Hash-Based Access Control in an Arbitrary Hierarchy ∗

We give the first solution to the problem of access control in an arbitrary n-node hierarchy G (e.g., RBAC) where all of the following hold: (i) only hash functions are used for a node to derive a descendant’s key from its own key, as opposed to the use of RSA public-key cryptography in many previous schemes (which requires slow modular exponentiations); (ii) the space complexity of the public information is the same as that of storing graph G (which is asymptotically optimal), as opposed to the quadratic space complexity of some other schemes; (iii) the derivation by a node of a descendant’s access key takes O(n) bit operations in the worst case, as opposed to O(n) bit operations in some of the previous schemes; (iv) updates are handled locally in the hierarchy and do not “propagate” to descendants or ancestors of the affected part of the tree; and (v) the scheme is resistant to collusion in that no subset of nodes can conspire to gain access to any node that is not already a descendant of one of the conspirators (hence legally accessible). Similar to a number of previous schemes, the private information at a node consists of a single key associated with that node. The security of our scheme relies on the existence of cryptographic one-way hash functions and the random oracle model. Another (more minor) property of our scheme is that it does not require access graph G to be free of directed cycles. We provide simple modifications to our scheme so it can handle Crampton’s extensions of the standard hierarchies to “limited depth” and reverse inheritance [6].
Generating domain specific ontology for retrieving Hidden Web contents

Tremendous information present on the Internet is available only when the user type set of keywords for search for getting the result pages. Those pages are often referred as Hidden Web. The components like hidden Web crawler which wants to retrieve this hidden information has to fill the search interfaces automatically. For automatically filling these types of interfaces precise and relevant information is needed. A database that stores semantic information about objects and their relations may solve this purpose. This database can be defined with the help of Ontology defines common vocabulary. The work presented here mainly focuses on generating domain specific ontology for retrieving hidden Web contents. In this paper a knowledge base of Book domain has created using Protégé. All information stores as knowledge base in form of RDF triples <;subject predicate object>. This Knowledge base may be used in automatically filling up search interfaces for retrieving hidden Web data.
An Effective Counter Based Adaptive Broadcasting Scheme for Mobile Ad Hoc Networks

2 Abstract: Mobile Adhoc Networks (MANETs) are in desperate need of an effective broadcasting scheme to transmit a data packet from the sender node to destination node and the rest of the network nodes. This paper proposes an effective counter-based broadcasting scheme to accomplish efficient broadcasting by adaptive threshold based on the local density information. The counter identifies nodes with duplicate data packet using threshold values and node removes the redundant message. Probabilistic schemes do not require global topological information of the network to make a rebroadcast decision. In the proposed work, we use random assessment delay (RAD) to evaluate the network congestion level based on the packets received per second at each node. The performance evaluation shows that the proposed broadcasting scheme outperforms the existing methods in terms of reachability, routing overhead and latency.