Miracle-dz/UniFAR-FTUs huggingface dataset:
Software visualization today: systematic literature review

Software visualization means visualizing various aspects and artifacts related to software. By this definition a wide range of different software engineering aspects from program comprehension to understanding software process and usage are covered. This paper presents the results of systematic literature review spanning six years of software visualization literature. The main result shows that the most studied topics in the past six years are related to software structure, behavior and evolution. Software process and usage are addressed only in few studies. In the future studying the adoption of software visualization tools in industry context would be beneficial.
Extracurricular Activity Participation in High School

Extracurricular activity participation (EAP) has been positively linked with increased academic achievement and college attendance. However, the mechanisms linking EAP to educational outcomes are poorly understood. Using the Education Longitudinal Study of 2002 (ELS), this study contributes to our understanding of the relationship between EAP and educational success by (1) examining the ability of nine educational, social, and developmental mechanisms to explain the link between EAP and high school math achievement gains and the chances of 4-year college attendance and (2) examining the ability of family income to moderate the influence of these mechanisms. Results suggest that educational expectations, noncognitive skills, and social capital in the form of communication among parental groups are meaningful mediators, regardless of family income. These findings extend the literature concerned with understanding how EAP is related to academic outcomes, a connection that is not necessarily intuitive.
Electrical Brain Responses in Language-Impaired Children Reveal Grammar-Specific Deficits

Background Scientific and public fascination with human language have included intensive scrutiny of language disorders as a new window onto the biological foundations of language and its evolutionary origins. Specific language impairment (SLI), which affects over 7% of children, is one such disorder. SLI has received robust scientific attention, in part because of its recent linkage to a specific gene and loci on chromosomes and in part because of the prevailing question regarding the scope of its language impairment: Does the disorder impact the general ability to segment and process language or a specific ability to compute grammar? Here we provide novel electrophysiological data showing a domain-specific deficit within the grammar of language that has been hitherto undetectable through behavioural data alone. Methods and Findings We presented participants with Grammatical(G)-SLI, age-matched controls, and younger child and adult controls, with questions containing syntactic violations and sentences containing semantic violations. Electrophysiological brain responses revealed a selective impairment to only neural circuitry that is specific to grammatical processing in G-SLI. Furthermore, the participants with G-SLI appeared to be partially compensating for their syntactic deficit by using neural circuitry associated with semantic processing and all non-grammar-specific and low-level auditory neural responses were normal. Conclusions The findings indicate that grammatical neural circuitry underlying language is a developmentally unique system in the functional architecture of the brain, and this complex higher cognitive system can be selectively impaired. The findings advance fundamental understanding about how cognitive systems develop and all human language is represented and processed in the brain.
Charge transport in junctions between d-wave superconductors.

We develop a microscopic analysis of superconducting and dissipative currents in junctions between superconductors with {ital d}-wave symmetry of the order parameter. We study the proximity effect in such superconductors and show that for certain crystal orientations the superconducting order parameter can be essentially suppressed in the vicinity of a nontransparent specularly reflecting boundary. This effect strongly influences the value and the angular dependence of the dc Josephson current {ital j}{sub {ital S}}. At {ital T}{similar_to}{ital T}{sub {ital c}} it leads to a crossover between {ital j}{sub {ital S}}{proportional_to}{ital T}{sub {ital c}}{minus}{ital T} and {ital j}{sub {ital S}}{proportional_to}({ital T}{sub {ital c}}{minus}{ital T}){sup 2} respectively for homogeneous and nonhomogeneous distribution of the order parameter in the vicinity of a tunnel junction. We show that at low temperatures the current-phase relation {ital j}{sub {ital S}}({ital cphi}) for superconductor--normal-metal--superconductor junctions and short weak links between {ital d}-wave superconductors is essentially nonharmonic and contains a discontinuity at {ital cphi}=0. This leads to further interesting features of such systems which can be used for pairing symmetry tests in high-temperature superconductors (HTSC). We also investigated the low-temperature {ital I}-{ital V} curves of normal-metal--superconductor and superconductor-superconductor tunnel junctions and demonstrated that depending on the junction type andmore » crystal orientation these curves show zero-bias anomalies {ital I}{proportional_to}{ital V}{sup 2}, I{proportional_to}V{sup 2}ln(1/{ital V}), and {ital I}{proportional_to}{ital V}{sup 3} caused by the gapless behavior of the order parameter in {ital d}-wave superconductors. Many of our results agree well with recent experimental findings for HTSC compounds. « less
Fiscal policy consistency and implications for macroeconomic aggregates: the case of Uganda

The relationship between growth in monetary aggregates and price changes continues to be a subject of considerable debate both in the academic and policy circles. Whereas the more ‘conservative’ policy makers hold that growth in monetary aggregates bear proportionately on prices, ‘liberals’ on the other hand suggest a fairly weak relationship and instead mainly attribute sustained price changes to other innovations (including structural weaknesses and poor productive capacity). This study employed vector autoregression techniques (and its variants) to examine both short term as well as long term interactions between selected macroeconomic aggregates with particular focus on the relationship between money growth and price changes. Results from both the reduced form vector autoregression specification and the contemporaneous structural vector autoregression show a weak causation from growth in monetary aggregates to price changes, but the link between changes in monetary aggregates and prices becomes stronger in the long run. The results also point to a strong relationship between price changes on the one hand and exchange rate depreciation, and past inflation outcomes on the other. The results imply a potential for increased revenue from monetisation, at least up to some feasible as well as the need to focus on other possible sources of price variations. In general, whereas it is possible for the relationship between prices and money to weaken, budget deficits beyond ‘certain financeable limits’ will clearly negate the possibility of attaining other objectives of macroeconomic policy. A natural concern that arises in such a context is one of sustainability and compatibility of the budget deficit with other macroeconomic targets. We also employed the government budget accounting framework to analyse sustainability of Uganda’s current fiscal stance. The results show that the consolidated deficit is consistent with attainment of target outcomes for other macroeconomic variables, most notably the rates of inflation and GDP growth rates. The inflation target has however, been achieved at the cost of an unsustainable domestic debt. From a policy perspective, issuing domestic debt at such a high real interest rates will allow lower money growth but at the cost of future increases in debt service obligations and thus future budget deterioration.
Complex Shift Dynamics of Some Elementary Cellular Automaton Rules

This paper presents a discussion of complex shift dynamics of some elementary cellular automaton rules (rules 30, 41, and 110). Equations that show some degree of self-similarity are obtained. It is demonstrated that rules 30, 41, and 110 exhibit Bernoulli shifts and are topologically mixing on one of their own closed invariant subsystems. Furthermore, many complex Bernoulli shifts are explored for finite symbolic sequences with periodic boundary conditions.
Prediction of website response time based on support vector machine

Website response time is one of the most important performance parameter of website. It can be used to assess website performance to forecast the status of website. Large amounts of data are applied by a distributed monitoring system that monitoring a university website response time. Support vector machine with information granulation is studied to predict the response time. It can predict accurately the range of ultimate response time, the relative accuracy of the forecast average response time can reach 96.2%.
Exploring Eye-Tracking-Driven Sonification for the Visually Impaired

Most existing sonification approaches for the visually impaired restrict the user to the perception of static scenes by performing sequential scans and transformations of visual information to acoustic signals. This takes away the user's freedom to explore the environment and to decide which information is relevant at a given point in time. As a solution, we propose an eye tracking system to allow the user to choose which elements of the field of view should be sonified. More specifically, we enhance the sonification approaches for color, text and facial expressions with eye tracking mechanisms. To find out how visually impaired people might react to such a system we applied a user centered design approach. Finally, we explored the effectiveness of our concept in a user study with seven visually impaired persons. The results show that eye tracking is a very promising input method to control the sonification, but the large variety of visual impairment conditions restricts the applicability of the technology.
U-Finder : An Interactive Tool for Data Exploration and Decision-Making

Choosing a university or college to attend can be a daunting task. Each school has different qualities that must be taken into account and weighed against one another when making a decision. Location, cost of tuition, prestige, and size are just some of the variables that potential students take into account. The selection problem is two-fold. Firstly, information about colleges and universities is not found in a central location. In most cases students must seek this information for themselves, either by searching school web pages, reading brochures, speaking to current students, or visiting the schools. Secondly, students need to put all of this information together in a meaningful way that allows them to evaluate the pros and cons of each school and balance the trade-offs between them. Unfortunately, people are not very good at judging tradeoffs, particularly when the number of options to consider increases.
Deep Learning for Just-in-Time Defect Prediction

Defect prediction is a very meaningful topic, particularly at change-level. Change-level defect prediction, which is also referred as just-in-time defect prediction, could not only ensure software quality in the development process, but also make the developers check and fix the defects in time. Nowadays, deep learning is a hot topic in the machine learning literature. Whether deep learning can be used to improve the performance of just-in-time defect prediction is still uninvestigated. In this paper, to bridge this research gap, we propose an approach Deeper which leverages deep learning techniques to predict defect-prone changes. We first build a set of expressive features from a set of initial change features by leveraging a deep belief network algorithm. Next, a machine learning classifier is built on the selected features. To evaluate the performance of our approach, we use datasets from six large open source projects, i.e., Bugzilla, Columba, JDT, Platform, Mozilla, and PostgreSQL, containing a total of 137,417 changes. We compare our approach with the approach proposed by Kamei et al. The experimental results show that on average across the 6 projects, Deeper could discover 32.22% more bugs than Kamei et al's approach (51.04% versus 18.82% on average). In addition, Deeper can achieve F1-scores of 0.22-0.63, which are statistically significantly higher than those of Kamei et al.'s approach on 4 out of the 6 projects.
Performance of Main Memory Commit Protocols

Distributed transaction systems require an atomic commitment protocol to preserve ACID properties. The performance of such protocols must be properly evaluated to make system developers able to make an educated choice. In this report, simulation of throughput and response time of atomic commitment protocols for main memory systems are presented. The simulations are verified by statistical analysis. Also, various performance enhancers for main memory commit protocols are presented and evaluated. The simulation results show that the performance can be significantly boosted by using optimizations and protocols especially designed for main memory systems.
Robust Decision Making using a Risk-Averse Utility Set ∗

Eliciting the utility of a decision maker is difficult. In this paper, we develop a flexible decision making framework, which uses the concept of utility robustness to address the problem of ambiguity and inconsistency in utility assessments. The ideas are developed by giving a probabilistic interpretation to utility and marginal utility functions. Boundary and additional conditions are used to describe a utility set that characterizes a decision maker’s risk attitude. Reformulation and convergence results are given for the discrete and continuous specifications of the utility set. A portfolio investment decision problem is used to illustrate the basic ideas, and demonstrate the usefulness of the proposed decision making framework.
Commentary on Friedemann Pulvermüller's The Neuroscience of Language. On Brain Circuits of Words and Serial Order

This book offers a paradigmatic view of how it is possible to ground linguistic research on, and constrain linguistic theory by, neuroscientific evidence. The main aim of Pulvermuller’s book is to present the putative neurobiological basis of language, that is, how language is organized in the human brain: using the author’s own words, “to spell out language in the language of neurons” (Pulvermuller, 2005, p. 1). In his opinion, once a neuronal language is developed which connects linguistic structures and processes to brain structures and processes, “it would be possible to explore the space of possibilities that is restricted on the one side by current neuroscientific knowledge and on the other side by linguistic phenomena” (Pulvermuller, 2005, p. 272). Neuroscientific data could then constrain linguistic theory and vice versa. One of the fundamental reasons that leads Pulvermuller to set up his program is the belief that “The brain machinery is not just one arbitrary way of implementing the process it realizes, as, for example, any hardware computer configuration can realize almost any computer program or piece of software. The claim is that, instead, the hardware reveals aspects of the program. Neuronal structure is information” (Pulvermuller, 2005, p. 9). The structural and functional properties of the cortex must then be taken into account. Neuroanatomical and neurophysiological evidence indicates that the cerebral cortex is a network of neurons characterized by ordered input and output connections in modality-specific areas, by heavy information mixing through shortand long-distance connections, and by correlation learning. Indeed, the cortex exhibits the following properties:
Decoupled root-MUSIC algorithm for Multidimensional Harmonic retrieval

In this paper, we propose a decoupled root-MUSIC algorithm adapted to the multidimensional harmonic model, which is widely used in MIMO channel sounding. The optimization criterion of the proposed algorithm is based on multidimensional orthogonal condition testings between a tensor steering manifold parameterized by the parameters of interest and a set of orthogonal projectors associated with each dimension. This criterion can be viewed as a set of decoupled estimation subproblems and allows the use of fast polynomial rooting techniques. In consequence, the proposed algorithm is highly scalable, parallelizable and avoids costly enumerative-based search. However, decoupling property implies to correctly pair the estimated model parameters. So, we propose a fast automatic pairing procedure based on the exploitation of the Vandermonde-structure preserving property of the alternating least squares candecomp/parafac (ALS-CP) algorithm. In addition, we study in a first time the case of a single snapshot and we generalize our algorithm to the multiple snapshots scenario. Finally, by means of numerical simulations, we show that the proposed scheme is efficient for one order of magnitude less complex than other standard algorithms.
Autoinducer-2 Triggers the Oxidative Stress Response in Mycobacterium avium, Leading to Biofilm Formation

ABSTRACT Mycobacterium avium is an environmental organism and opportunistic pathogen with inherent resistance to drugs, environmental stresses, and the host immune response. To adapt to these disparate conditions, M. avium must control its transcriptional response to environmental cues. M. avium forms biofilms in various environmental settings, including drinking water pipes and potable water reservoirs. In this study, we investigated the role of the universal signaling molecule autoinducer-2 (AI-2) in biofilm formation by M. avium. The addition of the compound to planktonic M. avium cultures resulted in increased biofilm formation. Microarray and reverse transcriptase PCR studies revealed an upregulation of the oxidative stress response upon addition of AI-2. This suggests that the response to AI-2 might be related to oxidative stress, rather than quorum sensing. Consistent with this model, addition of hydrogen peroxide, a known stimulus of the oxidative stress response, to M. avium cultures resulted in elevated biofilm formation. These results suggest that AI-2 does not act as a quorum-sensing signal in M. avium. Instead, biofilm formation is triggered by environmental stresses of biotic and abiotic origins and AI-2 may exert effects on that level.
More Blogging Features for Author Identification

In this paper we present a novel improvement in the field of authorship identification in personal blogs. The improvement in authorship identification, in our work, is by utilizing a hybrid collection of linguistic features that best capture the style of users in diaries blogs. The features sets contain LIWC with its psychology background, a collection of syntactic features & part-of-speech (POS), and the misspelling errors features. Furthermore, we analyze the contribution of each feature set on the final result and compare the outcome of using different combination from the selected feature sets. Our new categorization of misspelling words which are mapped into numerical features, are noticeably enhancing the classification results. The paper also confirms the best ranges of several parameters that affect the final result of authorship identification such as the author numbers, words number in each post, and the number of documents/posts for each author/user. The results and evaluation show that the utilized features are compact, while their performance is highly comparable with other much larger feature sets. Author identification in blogs has various motivations and challenges. Identifying the author of anonymous blog posts could be useful in various applications. This includes online security where it is valuable to extract the patterns of authors who may participate in different blog sites with different identities. Authorship identification has been applied on different types of text like emails, books, web forums, articles, and a little bit in blogs, but until now, no specific standard features are confirmed or solidly recommended due to the differentiation in the properties of text in each context. Moreover, there are many factors that have important roles and affect the performance of identification process such as the text length, the number of authors, the number of posts per author, and the type of authors. In this paper, we address the above issues by applying authorship identification on an online diaries corpus using a different type of linguistic features with numerous combinations and analyze those factors that affect the identification results. The remainder of
Boosting Systematic Search by Weighting Constraints

In this paper, we present a dynamic and adaptive variable ordering heuristic which guides systematic search toward inconsistent or hard parts of a Constraint Satisfaction Problem (CSP). This generic heuristic is able to exploit information about previous states of the search process whereas traditional dynamic ones only exploit information about the current state. Intuitively, it avoids some trashing by first instantiating variables involved in the constraints that have frequently participated in dead-end situations. Such information is recorded by associating a weight with each constraint. This weight is increased whenever the associated constraint is violated during search. The extensive experiments that we have conducted prove that our conflict-directed approach is the most efficient current one with respect to significant and large classes of academic, random and real-world instances.
Getting Expert Quality from the Crowd for Machine Translation Evaluation

This paper addresses the manual evaluation of Machine Translation (MT) quality by means of crowdsourcing. To this purpose, we replicated the ranking evaluation of the ArabicEnglish BTEC task proposed at the IWSLT 2010 Workshop by hiring non-experts through the CrowdFlower interface to Amazon’s Mechanical Turk. In particular, we investigated the effectiveness of “gold units” offered by CrowdFlower as the main quality control mechanism. The analysis of the collected data shows that agreement rates for non-experts are comparable to those obtained for experts, and that the crowd-based system ranking has a very strong correlation with expert-based ranking. Our results confirm that crowdsourcing is an effective way to reduce the costs of MT evaluation without sacrificing quality, and demonstrate that just exploiting the CrowdFlower control mechanism is enough to approximate expert-level data quality.