Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Acceptability of Densification: How Positive Framing and Source Credibility Can Change Attitudes

Planners often divisively advocate densification in developed areas. This article investigates the possibility that framing densifying development in positive ways may reduce conflict around greater density in developed urban areas of the United States. We use an online survey that asks verified voters in local elections (n = 772) to describe their feelings toward a hypothetical densifying infill development in their neighborhoods before and after reading various framing statements. In particular, we consider the impact of these statements when they originate among planners versus when they originate among lay community experts. We use regression analyses to explore how reactions to the development relate to demographic characteristics and other aspects of respondents’ identities, and find that, while positive framing generally improves perceptions of the development regardless of its source, these effects are limited among respondents expressing concern about specific development impacts, among particular income groups, and in some neighborhood types.
Attachment Insecurity Mediates the Effects of Intimate Partner Violence and Childhood Maltreatment on Depressive Symptoms in Adult Women

Objective: Insecure attachment style may be one mechanism by which women who have experienced intimate partner violence (IPV) and childhood maltreatment (CM) are at increased risk for depressive symptoms. In fact, women who experienced high levels of both CM and IPV may be at the greatest risk for attachment insecurity and resulting depressive symptoms, as negative working models from CM may be exacerbated by IPV in adulthood. This effect is likely driven by emotional CM and IPV, rather than other types of abuse, as abusive insults may be internalized, resulting in greater attachment insecurity and depressive symptoms. Method: A community sample of pregnant women (N = 206) completed self-report measures of IPV, CM, attachment insecurity, and depressive symptoms. Results: Findings from structural equation modeling indicated that CM and IPV exhibited independent effects on depressive symptoms, and these associations were fully mediated by insecure attachment style. The interaction between CM and IPV did not predict attachment insecurity or depressive symptoms. Emotional and sexual CM as well as physical IPV predicted attachment insecurity, and emotional IPV had a direct effect on depression. Conclusions: Although emotional CM and IPV had indirect and direct effects on depressive symptoms, respectively, multiple types of abuse were associated with attachment insecurity and resulting depression. Insecurely attached women may interpret IPV in a way that is congruent with their negative working models, resulting in depressive symptoms. In addition, IPV may work to maintain, rather than exacerbate, attachment insecurity resulting from CM.
Building a Semantic-Primitive-Based Lexical Consultation System

The paper describes the design of semanticprimitive-based lexical consultation system and the possible processes which will be performed on a mahine-readable dictionary (MRD) and corpus to produce a machine-tractable dictionary (MTD) and tractable corpus automatically. Linguistic tools and reources are created during or after the processes.
Journal of Software Maintenance and Evolution: Research and Practice Software Visualization in Software Maintenance, Reverse Engineering, and Re-engineering: a Research Survey

SUMMARY Software visualization is concerned with the static visualization as well as the animation of software artifacts, such as source code, executable programs, and the data they manipulate, and their attributes, such as size, complexity, or dependencies. Software visualization techniques are widely used in the areas of software maintenance, reverse engineering, and re-engineering, where typically large amounts of complex data need to be understood and a high degree of interaction between software engineers and automatic analyses is required. This paper reports the results of a survey on the perspectives of 82 researchers in software maintenance, reverse engineering, and re-engineering on software visualization. It describes to which degree the researchers are involved in software visualization themselves, what is visualized and how, whether animation is frequently used, whether the researchers believe animation is useful at all, which automatic graph layouts are used if at all, whether the layout algorithms have deficiencies, and—last but not least—where the medium-term and long-term research in software visualization should be directed. The results of this survey help to ascertain the current role of software visualization in software engineering from the perspective of researchers in these domains and give hints on future research avenues.
Early Computation of Contour Curvature and Part Structure: Evidence from Holes

We used holes to study unilateral border ownership and in particular the information carried by the sign of the curvature along the contour (ie the difference between convex and concave regions). When people perceive a hole, its shape has a reversed curvature polarity (ie a changed sign of curvature) compared to the same region perceived as an object. Bertamini (2001 Perception 30 1295–1310), and Bertamini and Croucher (2003 Cognition 87 33–54) suggested and found evidence to support the hypothesis that, because convex regions are perceived as parts, positional information is more readily available for convex regions. Therefore a change is predicted when a given region is perceived as either a hole or a figure. We confirm that finding in this study, using holes defined by binocular disparity. We conclude that a change from figure to hole always reverses the encoding of curvature polarity. In turn, polarity obligatorily affects perceived part structure and the processing of position.
The XP of TAO: extreme programming of large, open-source frameworks

The Adaptive Communication Environment (ACE) and The ACE ORB (TAO) are cutting edge, real-time software products that by nature support and grow from change. Therefore, eXtreme Programming (XP) would seem to be appropriate for these projects. In fact, their developers have been following many XP practices all along. We describe these practices here, and identify some that we do not follow rigorously. However, three characteristics of ACE and TAO contraindicate the application of XP. These products are 1) large, 2) open-source, and 3) development frameworks, that is, not end-user applications. We explore the impediments to XP for such projects and how we have, or would like to, overcome them. In particular, we introduce remote pair programming as a potential augmentation to traditional pair programming.
A frequent keyword-set based algorithm for topic modeling and clustering of research papers

In this paper we introduce a novel and efficient approach to detect topics in a large corpus of research papers. With rapidly growing size of academic literature, the problem of topic detection has become a very challenging task. We present a unique approach that uses closed frequent keyword-set to form topics. Our approach also provides a natural method to cluster the research papers into hierarchical, overlapping clusters using topic as similarity measure. To rank the research papers in the topic cluster, we devise a modified PageRank algorithm that assigns an authoritative score to each research paper by considering the sub-graph in which the research paper appears. We test our algorithms on the DBLP dataset and experimentally show that our algorithms are fast, effective and scalable.
Abrupt Shot Detection in Video using Weighted Edge Information

Content Based Video Retrieval (CBVR) has been extensively utilized for automatic indexing, retrieval and management of video data. Segmentation of video is the prominent step in Content Based Video Retrieval. In this paper, we focus on automatic detection of abrupt shot cuts in video sequences. The proposed approach exploits the edge information of an image of a video frame for its characterization. A (2x2) mask of sliding window is used in both overlapping and non overlapping mode to assign binary weights to the edge information of an image. The binary weights evaluated for each mask is used to construct histogram for each image which forms the feature vector to represent an image. The Euclidean distance between the feature vectors of adjacent frames of a video are computed and these values are used for shot cut detection process using adaptive thresholding. To check the efficacy of the proposed shot boundary detection approach, experiments were carried out on a subset of standard video data set TRECVID 2001. The experimental results obtained by the proposed algorithm outperform some of the existing shot boundary detection algorithms in terms of precision, recall and F-measure rates.
Riemannian median and its estimation

In this paper, we define the geometric median of a probability measure on a Riemannian manifold, give its characterization and a natural condition to ensure its uniqueness. In order to calculate the median in practical cases, we also propose a subgradient algorithm and prove its convergence as well as estimating the error of approximation and the rate of convergence. The convergence property of this subgradient algorithm, which is a generalization of the classical Weiszfeld algorithm in Euclidean spaces to the context of Riemannian manifolds, also answers a recent question in P. T. Fletcher et al. [13]
Hybrid Process Neural Network based on Spatio-Temporal Similarities for Short-Term Traffic Flow Prediction

Spatio-temporal similarities, one of the characteristics to describe the relativity of traffic phenomenon, can be utilized to predict short-term traffic flow. These similarities not always appear at spatial adjacent road links because of complexity of road network. In this paper, we adopt Cross-Correlation Function to depict similarities between different traffic flow series according to the observed flow data. The process characteristic generalizes the evolvement rules of traffic flow which are essentials need to be tackled by a prediction model. After choosing the most correlative road links and their time delay instead of the upstream or downstream ones, a Hybrid Process Neural Network is constructed to predict shortterm traffic flow, which uses various scales to catch traffic features such as daily-periodicity, weekly-periodicity and spatiotemporal process, since a simple model is not good enough to depict all these rules. Application of the proposed method is demonstrated, and the experimental results show that our method outperforms other compared methods.
Who ‘likes’ populists? Characteristics of adolescents following right-wing populist actors on Facebook

ABSTRACT Past research has primarily focused on the positive potential of Social Network Sites (SNS) for democratic citizenship. However, SNS have also become key territories for right-wing populist actors (RWPA). Yet we almost completely lack research on adolescents’ use of SNS in the context of political populism. In this study, we draw a profile of adolescents who follow RWPA on Facebook incorporating three key predictors: political trust, personality traits as measured by the Big Five, as well as motivations to use SNS. A paper–pencil survey (N = 294) of 15–20-year-old school students was implemented. In line with hypotheses, we found that followers of RWPA scored low on political trust, informational motivations to use SNS, and the personality trait openness, but scored high on extraversion and participatory motivations to use SNS. In demographic terms, we found some evidence that lower educated and male students were more likely to follow RWPA. The important role of cognitive engagement with political information for the quality of participation on SNS is discussed.
Identi fi cation based on Di ff erence-inDi ff erences Approaches with Multiple Treatments

This paper discusses identification based on difference-in-differences (DiD) approaches with multiple treatments. It shows that an appropriate adaptation of the common trend assumption underlying the DiD strategy for the comparison of two treatments restricts the possibility of effect heterogeneity for at least one of the treatments. The required assumption of effect homogeneity is likely to be violated because of non-random assignment to treatment based on both observables and unobservables. However, this paper shows that, under certain conditions, the DiD estimate comparing two treatments identifies a lower bound in absolute values on the average treatment effect on the treated compared to the unobserved non-treatment state, even if effect homogeneity is violated. This is possible if, in expectation, the effects of both treatments compared to no treatment have the same sign, and one treatment has a stronger effect than the other treatment on the respective recipients. Such assumptions are plausible if treatments are ordered or vary in intensity.
Risk-Sensitive Planning*

Methods for planning in stochastic domains often aim for finding plans that minimize expected execution cost or maximize the probability of goal achievement. Researchers have largely ignored the question how to incorporate risk-sensitive attitudes into their planning mechanisms. Since utility theory shows that it can be rational to maximize expected utility, one might believe that by replacing all costs with their respective utilities (for an appropriate utility function) one could achieve risk-sensitive attitudes without having to change the existing probabilistic planning methods. Unfortunately, we show that this is usually not the case and, moreover, that the best action in a state can depend on the total cost that the agent has already accumulated. However, we demonstrate how one can transform risksensitive planning problems into equivalent ones for riskneutral agents provided that utility functions with the delta property are used. The transformation of a riskseeking planning problem can then be solved with any AI planning algorithm that either minimizes (or sarisrices) expected execution cost or, equivalently, one that maximizes (or satisrices) the probability of goal achievement. Thus, one can extend the functionality of these planners to risk-sensitive planning.
Does Flywheel Paradigm Training Improve Muscle Volume and Force? A Meta-Analysis.

Several studies have confirmed the efficacy of flywheel paradigm training for improving or benefiting muscle volume and force. A meta-analysis of 13 studies with a total of 18 effect sizes was performed to analyse the role of various factors on the effectiveness of flywheel paradigm training. The following inclusion criteria were employed for the analysis: (a) randomized studies; (b) high validity and reliability instruments; (c) published in a high quality peer-reviewed journal; (d) healthy participants; (e) studies where the eccentric programme were described; and (f) studies where increases in muscle volume and force were measured before and after training. Increases in muscle volume and force were noted through the use of flywheel systems during short periods of training. The increase in muscle mass appears was not influenced by the existence of eccentric overload during the exercise. The increase in force was significantly higher with the existence of eccentric overload during the exercise. The responses identified in this analysis are essential and should be considered by strength and conditioning professionals regarding the most appropriate dose response trends for flywheel paradigm systems to optimize the increase in muscle volume and force.
An Investigation of Ensemble Techniques for Detection of Spam Reviews

Whether purchasing a product or searching for a new doctor, consumers often turn to online reviews for recommendations. Determining whether reviews are truthful is imperative to the consumer, as to not get misled by false recommendations. Unfortunately, it is often difficult, or impossible, for humans to ascertain the validity of a review through reading the text, however, studies have shown machine learning methods perform well for detecting untruthful reviews. Previously, no studies have examined the effects of ensemble learners on the detection of untruthful reviews, despite these techniques being effective in related text classification domains. We seek to inform other researchers of the effects of ensemble techniques on the detection of spam reviews. To this aim, we evaluate four classifiers and three ensemble techniques using those four classifiers as base learners. We compare the results of Multinomial Naïve Bayes, C4.5, Logistic Regression, Support Vector Machine, Random Forest with 100, 250, and 500 trees, and Boosting and Bagging using the base learners. We found that none of the ensemble techniques tested were able to significantly improve review spam detection over standard Multinomial Naïve Bayes and thus, are not worth the computational expense they inflict.
A centrally controlled shuffle network for reconfigurable and fault-tolerant architecture

The paper describes a multistage shuffle interconnection network which is controlled by a central monitor. A control code broadcast by the monitor to all the basic switching elements of the network simultaneously, makes the network dynamically reconfigurable. The control code plays three vital roles. Firstly, it establishes conflict-free paths between several source-destination pairs. Thus the problem of collision, a major obstacle of a self-routing network, is completely eliminated. Secondly, the direct paths are established in one clock period irrespective of the number of stages. This makes the system faster. Thirdly, the control code also acts as a grouping code for executing a table of arbitrary data exchange requests between nodes in minimum number of passes. It is also shown that the network can be made fault-tolerant by the addition of an extra stage. Any single fault and some multiple faults in the intermediate stages can be tolerated by this scheme. Moreover, the switching from the faulty state to the fault-free state can be done in a single clock period, thus enabling fast fault-tolerant reconfiguration.
Cdk 1 phosphorylates SPAT-1 / Bora to trigger PLK1 activation and drive mitotic entry in C . elegans embryos

*N. Tavernier and A. Noatynska contributed equally to this paper. Correspondence to Monica Gotta: Monica.Gotta@unige.ch; or Lionel Pintard: pintard.lionel@ijm.univ-paris-diderot.fr Abbreviations used in this paper: C.e. , C. elegans; DIC, differential interference contrast; dsRNA, double-stranded RNA; H.s. , Homo sapiens; LC, liquid chromatography; MBP, Maltose-binding protein; MS, mass spectrometry; NEBD, nuclear envelope breakdown; PBD, Polo box domain; Sf9, Spodoptera frugiperda 9; SPAT-1, Suppressor of Par-Two 1; WT, wild type. Introduction
FN-Identify: Novel Restriction Enzymes-Based Method for Bacterial Identification in Absence of Genome Sequencing

Sequencing and restriction analysis of genes like 16S rRNA and HSP60 are intensively used for molecular identification in the microbial communities. With aid of the rapid progress in bioinformatics, genome sequencing became the method of choice for bacterial identification. However, the genome sequencing technology is still out of reach in the developing countries. In this paper, we propose FN-Identify, a sequencing-free method for bacterial identification. FN-Identify exploits the gene sequences data available in GenBank and other databases and the two algorithms that we developed, CreateScheme and GeneIdentify, to create a restriction enzyme-based identification scheme. FN-Identify was tested using three different and diverse bacterial populations (members of Lactobacillus, Pseudomonas, and Mycobacterium groups) in an in silico analysis using restriction enzymes and sequences of 16S rRNA gene. The analysis of the restriction maps of the members of three groups using the fragment numbers information only or along with fragments sizes successfully identified all of the members of the three groups using a minimum of four and maximum of eight restriction enzymes. Our results demonstrate the utility and accuracy of FN-Identify method and its two algorithms as an alternative method that uses the standard microbiology laboratories techniques when the genome sequencing is not available.
Rotation Invariant Localization of Duplicated Image Regions Based on Zernike Moments

This paper proposes a forensic technique to localize duplicated image regions based on Zernike moments of small image blocks. We exploit rotation invariance properties to reliably unveil duplicated regions after arbitrary rotations. We devise a novel block matching procedure based on locality sensitive hashing and reduce false positives by examining the moments' phase. A massive experimental test setup benchmarks our algorithm against state-of-the-art methods under various perspectives, examining both pixel-level localization and image-level detection performance. By taking signal characteristics into account and distinguishing between “textured” and “smooth” duplicated regions, we find that the proposed method outperforms prior art in particular when duplicated regions are smooth. Experiments indicate high robustness against JPEG compression, blurring, additive white Gaussian noise, and moderate scaling.
THEMIS: Towards Mutually Verifiable Billing Transactions in the Cloud Computing Environment

The ability to record and keep account of the usage of cloud resources in a credible and verifiable way is a precursor to widespread cloud deployment and availability because usage information is potentially sensitive and must be verifiably accurate. In an attempt to provide a mutually verifiable resource usage and billing mechanism, we found that the frequent asymmetric key operations of a digital signature lead to excessive computations and a bottleneck of billing transactions. As a remedy for these limitations, we propose a mutually verifiable billing system called THEMIS. The system, which introduces the concept of a cloud notary authority for the supervision of billing, makes billing more objective and acceptable to users and cloud service providers. THEMIS generates mutually verifiable binding information that can be used to resolve future disputes between a user and a cloud service provider. Because THEMIS does not require any asymmetric key operations of users and providers, it provides a level of security that is identical to that of a Public Key Infrastructure (PKI) and it minimizes the latency of billing transactions. This work has been undertaken on a real cloud computing service called iCube Cloud.
Energizing Software Engineering Education through Real-World Projects as Experimental Studies

Our experience shows that a typical industrial project can enhance software engineering research and bring theories to life. The University of Kentucky (UK) is in the initial phase of developing a software engineering curriculum. The first course, a graduate-level survey of software engineering, strongly emphasized quality engineering. assisted by the UK clinic, the students undertook a project to develop a phenylalanine milligram tracker. It helps phenylketonuria (PKU) sufferers to monitor their diet as well as assists PKU researchers to collect data. The project was also used as an informal experimental study. The applied project approach to teaching software engineering appears to be successful thus far. The approach taught many important software and quality engineering principles to inexperienced graduate students in an accurately simulated industrial development environment. It resulted in the development of a framework for describing and evaluating such a real-world project, including evaluation of the notion of a user advocate. It also resulted in interesting experimental trends, though based on a very small sample. Specifically, estimation skills seem to improve over time and function point estimation may be more accurate than LOC estimation.