Miracle-dz/UniFAR-FTUs huggingface dataset:
MURGROOM: multi-site requirement reuse through GRaph and ontology matching

In the era of globalization, it is strongly recommended for a company when developing a new project to identify companies (at local and international levels) that have already developed similar projects to reuse their experience and reproduce their findings. It should be noticed that the reuse is an eternal grail to increase projects productivity. It may concern all aspects of the life cycle of project development that ranges from user requirements to codes. In this study, we focus on user requirements. In such a context, each company (site) has already expressed its own requirements using different modeling formalisms, conceptualizations and vocabularies and scheduling of tasks. This situation makes the reuse and exploitation of the requirements harder, since the heterogeneity is everywhere. To reduce this heterogeneity, the most important studies assume the existence of pivot models and shared ontologies that allow reducing heterogeneities. Unfortunately, these hypotheses are out of phase with the reality, in which the sites are autonomous and usually multilingual. In this paper, we propose to relax these hypotheses. Firstly, we propose a framework for requirements integration using an ontology-based matching system that identifies and unifies different heterogeneous items. Secondly, a reasoning mechanism is proposed to deduce relationships between integrated requirements. Finally, our proposed framework is evaluated w.r. t its feasibility, results quality and effectiveness. A tool, called MURGROOM implementing the framework is also proposed.
Computing Geometric Minimum Spanning Trees Using the Filter-Kruskal Method ★

Let P be a set of points in R. We propose GeoFilterKruskal, an algorithm that computes the minimum spanning tree of P using well separated pair decomposition in combination with a simple modification of Kruskal’s algorithm. When P is sampled from uniform random distribution, we show that our algorithm runs in O(n log n) time with probability at least 1−1/n for a given c > 1. Although this is theoretically worse compared to known O(n) [31] or O(n logn) [27, 11, 15] algorithms, experiments show that our algorithm works better in practice for most data distributions compared to the current state of the art [27]. Our algorithm is easy to parallelize and to our knowledge, is currently the best practical algorithm on multi-core machines for d > 2.
Evolving Ext 4 for Shingled Disks

Drive-Managed SMR (Shingled Magnetic Recording) disks offer a plug-compatible higher-capacity replacement for conventional disks. For non-sequential workloads, these disks show bimodal behavior: After a short period of high throughput they enter a continuous period of low throughput. We introduce ext4-lazy1, a small change to the Linux ext4 file system that significantly improves the throughput in both modes. We present benchmarks on four different drive-managed SMR disks from two vendors, showing that ext4-lazy achieves 1.7-5.4× improvement over ext4 on a metadata-light file server benchmark. On metadata-heavy benchmarks it achieves 2-13× improvement over ext4 on drive-managed SMR disks as well as on conventional disks.
Acoustic-phonetic information from excitation source for refining manner hypotheses of a phone recognizer

Reliable acoustic-phonetic (AP) information derived from the speech signal can be used to detect and correct errors in the output of a phone recognizer. In this paper, limited acoustic-phonetic information derived primarily by processing the excitation source information in the speech signal is used to improve the performance of detection of manner of articulation from a baseline phone recognition system. A context-independent HMM-based monophone system without any language information is used as the baseline system for this purpose. The performance of the phone recognizer in terms of its ability to detect the manners of articulation is studied. The errors in the hypothesis of the manner of articulation of phones are corrected using AP information such as voicing, voice bar and frication. It is shown that significant improvement can be achieved by using simple or limited AP information.
Machine learning for user modeling in a multilingual learning system

Towards the successful creation of user models that can be incorporated into foreign language learning systems, we have used algorithmic approaches residing in the field of machine learning. The creation of user models is even more demanding in the area of Computer Assisted Multilanguage Learning, since modeling is an even more complex process that concurrently handles information from multiple domains. These domains have important similarities but also basic differences. This paper describes the implementation of student modeling through machine learning techniques, which aims to ameliorate future multiple language learning systems. The incorporation of k-means clustering is used to address several barriers posed by the heterogeneous learning audience. The resulting system both generates and discovers user profiles, based on students' characteristics, performance and preferences. Through our system, we promote the adaptivity and individualization to each user that interacts with the application, by providing individualized help, error diagnosis and error proneness along with advice generator components.
Multiple Self-Organizing Maps for Intrusion Detection

The Kohonen self-organizing map is an extremely powerful mechanism for automatic mathematical characterization of acceptable system activity. Because it spontaneously develops a sophisticated characterization of the system whose behaviors it is trained to recognize, it could detect intrusions which it has never observed simply by noting the degree to which they differ from normal activity. After discussing the design of a network monitoring system which would maximize the potential of the self-organizing map, we describe briefly our experimental results in which a simpler system resoundingly detected two different exploits which we perpetrated against one of our servers.
Design and Implementation of an Intelligent System for Educational-Game Resource Discovery

On the basis of some research work on the educational game sites, we introduce an intelligent system for educational game resource discovery and define a concept of game-metadata structure. It will strive to organize educational games all over the internet and provide a new way of thinking in building game resource database. In this paper, both of the key technologies and the function modules are described in detail.
Qian Di Intel China Research Beijing

DBNs (Dynamic Bayesian Networks) [1] are powerful tool in modeling time-series data, and have been used in speech recognition recently [2,3,4]. The “decoding” task in speech recognition means to find the viterbi path [5](in graphical model community, “viterbi path” has the same meaning as MPE “Most Probable Explanation”) for a given acoustic observations. In this paper we describe a new algorithm utilizes a new data structure “backpointer”, which is produced in the “marginalization” procedure in probability inference. With these backpointers, the viterbi path can be found in a simple backtracking. We first introduce the concept of backpointer and backtracking; then give the algorithm to compute the viterbi path for DBNs based on backpointer and backtracking. We prove that the new algorithm is correct, faster and more memory saving comparison with old algorithm. Several experiments are conducted to demonstrate the effectiveness of the algorithm on several well known DBNs. We also test the algorithm on a real world DBN model that can recognize continuous digit numbers.
A Middleware Architecture for Dynamic Adaptation in Ubiquitous Computing

The development of applications that adapt to the environment and remain running even when the user is moving or switching device, remains an open research challenge. In this article we present a view of the EXEHDA middleware and a new service created for dynamic adaptation. EXEHDA is service-oriented, adaptive and was conceived to support the execution of ubiquitous applications. The main concept in the proposed design for the middleware and for the application is context awareness expressed in an adaptive behavior. The middleware manages and implements the follow-me semantics for ubiquitous applications. This is also a key to provide function- ality adapted to the constraints and unpredictability of the large-scale environment. To achieve this objective, EXEHDA employs various strategies in its services to allow the adaptation to the current context, such as on-demand adaptive service loading, and dynamic discovery and configuration. In that sense, EXEHDA provides services for distributed adaptive execution, context recognition, ubiquitous storage and access, and anonymous and asynchronous communications. To evaluate the new service proposed for dynamic adaptation we developed a case study, implementing an application in medical area. Analyzing the results we can see that the users found the application easy to use and usefulness for health workers at a hospital. This work is sponsored by RNP, FINEP and CNPq - Brazilian Foundations.
Security, Privacy, Confidentiality and Integrity of Emerging Healthcare Technologies: A Framework for Quality of Life Technologies to be HIPAA/HITECH Compliant, with Emphasis on Health Kiosk Design

This dissertation research focused on the following: 1. Determined possible vulnerabilities that exist in multi-user kiosks and the computer systems that make up multi-user kiosk systems. 2. Developed an evaluation system and audit checklist for multi-user kiosk systems adapted from the Office for Civil Rights (OCR) audit protocols to address the vulnerabilities identified from our research. 3. Improved the design of a multi-user health kiosk to meet the HIPAA/HITECH standards by incorporating P&S policies. 4. Explored the feasibility and preliminary efficacy of an intervention to explore the magnitude of differences in users’ perceived risk of privacy and security (P&S) breaches as well as correlation between perceived risk and their intention to use a multi-user health kiosk. A gap analysis demonstrated that we successfully incorporated 81% of our P&S polices into the current design of our kiosk that is undergoing pilot testing. This is higher than our initial target of 50%. Repeated measures ANOVA was performed to analyze baseline and six-month follow-up of 36 study participants to measure the magnitude of the change in their “perceived risk”. Results from the ANOVA found significant group-by-time interaction (Time*Group) F (2, 33) = . 27, P=. 77, ηp2=. 02, significant time interaction F (1, 33) = 4.73, P=. 04, ηp2=. 13, and no significant group interaction F (2, 33) =1.27, P=. 30 ηp2=. 07. The study intervention was able to significantly reduce users’ “perceived risk with time (baseline and six-month follow-up), even though the magnitude of the change was small. We were however, unable to perform the correlation analysis as intended since all the kiosk participants used in the analysis intended to use the kiosk both at baseline and at six-month follow-up. These findings will help in direct research into methods to reduce “perceived risk” as well as using education and communication to affect human behavior to reduce risky behavior on both internal and external use of new health IT applications and technologies. It could then serve as framework to drive policy in P&S of health applications, technologies and health IT systems.
New Battery Model and State-of-Health Determination Through Subspace Parameter Estimation and State-Observer Techniques

This paper describes a novel adaptive battery model based on a remapped variant of the well-known Randles' lead-acid model. Remapping of the model is shown to allow improved modeling capabilities and accurate estimates of dynamic circuit parameters when used with subspace parameter-estimation techniques. The performance of the proposed methodology is demonstrated by application to batteries for an all-electric personal rapid transit vehicle from the urban light transport (ULTRA) program, which is designated for use at Heathrow Airport, U.K. The advantages of the proposed model over the Randles' circuit are demonstrated by comparisons with alternative observer/estimator techniques, such as the basic Utkin observer and the Kalman estimator. These techniques correctly identify and converge on voltages associated with the battery state-of-charge (SoC), despite erroneous initial conditions, thereby overcoming problems attributed to SoC drift (incurred by Coulomb-counting methods due to overcharging or ambient temperature fluctuations). Observation of these voltages, as well as online monitoring of the degradation of the estimated dynamic model parameters, allows battery aging (state-of-health) to also be assessed and, thereby, cell failure to be predicted. Due to the adaptive nature of the proposed algorithms, the techniques are suitable for applications over a wide range of operating environments, including large ambient temperature variations. Moreover, alternative battery topologies may also be accommodated by the automatic adjustment of the underlying state-space models used in both the parameter-estimation and observer/estimator stages.
Cartesian genetic programming encoded artificial neural networks: a comparison using three benchmarks

Neuroevolution, the application of evolutionary algorithms to artificial neural networks (ANNs), is well-established in machine learning. Cartesian Genetic Programming (CGP) is a graph-based form of Genetic Programming which can easily represent ANNs. Cartesian Genetic Programming encoded ANNs (CGPANNs) can evolve every aspect of an ANN: weights, topology, arity and node transfer functions. This makes CGPANNs very suited to situations where appropriate configurations are not known in advance. The effectiveness of CGPANNs is compared with a large number of previous methods on three benchmark problems. The results show that CGPANNs perform as well as or better than many other approaches. We also discuss the strength and weaknesses of each of the three benchmarks.
Significant grey matter changes in a region of the orbitofrontal cortex in healthy participants predicts emotional dysregulation.

The traditional concept of 'categorical' psychiatric disorders has been challenged as many of the symptoms display a continuous distribution in the general population. We suggest that this is the case for emotional dysregulation, a key component in several categorical psychiatric disorder constructs. We used voxel-based magnetic resonance imaging morphometry in healthy human subjects (n = 87) to study how self-reported subclinical symptoms associated with emotional dysregulation relate to brain regions assumed to be critical for emotion regulation. To measure a pure emotional dysregulation, we also corrected for subclinical symptoms of non-emotional attentional dysregulation. We show that such subclinical emotional symptoms correlate negatively with the grey matter volume of lateral orbitofrontal cortex bilaterally-a region assumed to be critical for emotion regulation and dysfunctional in psychiatric disorders involving emotional dysregulation. Importantly, this effect is mediated both by a decrease in volume associated with emotional dysregulation and an increase in volume due to non-emotional attentional dysregulation. Exploratory analysis suggests that other regions involved in emotional processing such as insula and ventral striatum also show a similar reduction in grey matter volume mirroring clinical disorders associated with emotional dysregulation. Our findings support the concept of continuous properties in psychiatric symptomatology.
C-requirements specification teaching

Requirements elicitation, so directly related to the final success of a software project, is also a complex task. Requirement certification and validation need the customer and/or user's participation, for which reason software engineers must document the elicited requirements in a user-friendly format. The information, which clients find easier to understand, is gathered in a requirements catalog, usually written in natural language. This kind of requirements representation is often called customer oriented requirements or C-requirements. Usually, the teaching of the requirement-related topics is more concerned with requirement analysis, especially in modeling issues, than with the elicitation and C-requirements documentation process. This situation can produce misunderstandings and mistakes in students who try to carry out the requirements elicitation and documentation processes when they have to face a real or proposed software project. In this paper, we will describe a concrete requirements elicitation and documentation method that has been introduced to and accepted by students who take the software engineering subject as taught in the context of the computer science studies at the University of Salamanca (Spain).
Implementing 3D visualizations of EEG signals in artistic applications

The increasing availability of low-cost brain-computer interfaces (BCIs) is extending the possibilities of visual or auditory representations of cognitive activity. These representations are valuable to artists who seek to develop neural interactivity in their work; to scientists who seek new ways to visualize and develop new perspectives on neural functioning; and to clinicians who can harness visual and auditory feedback for treatment of neurological disorders. We describe a method that transforms the neuroelectric activity measured with electroencephalography (EEG) into 3-dimensional geometric data. The method makes use of OpenGL in order to construct artificial spatial-spectral representations of EEG data. We provide several examples in which this method is implemented in OpenGL visualizations.
Comparative study of shape, intensity and texture features and support vector machine for white blood cell classification

The complete blood count (CBC) is widely used test for counting and categorizing various pe- ripheral particles in the blood. The main goal of the paper is to count and classify white blood cells (leukocytes) in microscopic images into five major categories using features such as shape, intensity and texture features. The first critical step of counting and classification procedure in- volves segmentation of individual cells in cytological images of thin blood smears. The quality of segmentation has significant impact on the cell type identification, but poor quality, noise, and/or low resolution images make segmentation less reliable. We analyze the performance of our system for three different sets of features and we determine that the best performance is achieved by wavelet features using the Dual-Tree Complex Wavelet Transform (DT-CWT) which is based on multi-resolution characteristics of the image. These features are combined with the Support Vector Machine (SVM) which classifies white blood cells into their five primary types. This approach was validated with experiments conducted on digital normal blood smear images with low resolution.
Using a Hash-Based Method with Transaction Trimming and Database Scan Reduction for Mining Associati

In this paper, we examine the issue of mining association rules among items in a large database of sales transactions. Mining association rules means that given a database of sales transactions, to discover all associations among items such that the presence of some items in a transaction will imply the presence of other items in the same transaction. The mining of association rules can be mapped into the problem of discovering large itemsets where a large itemset is a group of items which appear in a suucient number of transactions. The problem of discovering large itemsets can be solved by constructing a candidate set of itemsets rst and then, identifying, within this candidate set, those itemsets that meet the large itemset requirement. Generally this is done iteratively for each large k-itemset in increasing order of k where a large k-itemset is a large itemset with k items. To determine large itemsets from a huge number of candidate large itemsets in early iterations is usually the dominating factor for the overall data mining performance. To address this issue, we develop an eeective algorithm for the candidate set generation. It is a hash based algorithm and is especially eeective for the generation of candidate set for large 2-itemsets. Explicitly, the number of candidate 2-itemsets generated by the proposed algorithm is, in orders of magnitude, smaller than that by previous methods, thus resolving the performance bottleneck. Note that the generation of smaller candidate sets enables us to eeectively trim the transaction database size at a much earlier stage of the iterations, thereby reducing the computational cost for later iterations signiicantly. The advantage of the proposed algorithm also provides us an opportunity of reducing the amount of disk I/O required. Extensive simulation study is conducted to evaluate performance of the proposed algorithm.
Object and native code thread mobility among heterogeneous computers

We present a technique for moving objects and threads among heterogeneous computers at the native code level. To enable mobility of threads running native code. we convert thread states among machine-dependent and machine-independent formats. We introduce the concept of bus stops, which are machine-independent representations of program points as represented by program counter values. The concept of bus stops can be used also for other purposes. e.g. to aid inspecting and debugging optimized code, garbage collection etc. We also discuss techniques for thread mobility among processors executing differently optimized codes. We demonstrate the viability of our ideas by providing a prototype implementation of object and thread mobility among heterogeneous computers. The prototype uses the Emerald distributed programming language without modification ; we have merely extended the Emerald runtime system and the code generator of the Emerald compiler. Our extensions allow object and thread mobility among VAX, Sun-3, HP9000/300, and Sun SPARC workstations. The excellent intra-node performance of the original homogeneous Emerald is retained : migrated threads run at native code speed before and after migration ; the same speed as on homogeneous Emerald and close to C code performance. Our implementation of mobility has not been optimized : thread mobility and trans-architecture invocations take about 60% longer than in the homogeneous implementation. We believe this is the first implementation of full object and thread mobility among heterogeneous computers with threads executing native code.
Essential Quality Analysis Criteria in ForensicGenetics Identification: A Position Statementof Italian Society of Human Genetics

The entry into force of DPR n. 87 of 7th April 2016 relating to the DNA Data Bank requires an implementation upgrade of the Forensic Genetics analysis to ensure the quality of results by the control of the pre-analytical and analytical processes. The decree provides the opportunity to define the operational guidance elements, shared by Italian laboratories in the Forensic Genetics field, aimed at ensuring the quality of data and at producing useful genetic profiles: for this reason, the laboratories who want to contribute to the Bank of DNA data need accreditation in accordance with ISO/ IEC 17025. This document represents a brief technical document of the Italian Society of Human Genetics in the aim of supporting laboratories seeking accreditation as well as the Italian National Accreditation Body appointed by the State to perform accreditation activity (ACCREDIA).