Miracle-dz/UniFAR-FTUs huggingface dataset:
Latent-Data Privacy Preserving With Customized Data Utility for Social Network Data

Social network data can help with obtaining valuable insight into social behaviors and revealing the underlying benefits. New big data technologies are emerging to make it easier to discover meaningful social information from market analysis to counterterrorism. Unfortunately, both diverse social datasets and big data technologies raise stringent privacy concerns. Adversaries can launch inference attacks to predict sensitive latent information, which is unwilling to be published by social users. Therefore, there is a tradeoff between data benefits and privacy concerns. In this paper, we investigate how to optimize the tradeoff between latent-data privacy and customized data utility. We propose a data sanitization strategy that does not greatly reduce the benefits brought by social network data, while sensitive latent information can still be protected. Even considering powerful adversaries with optimal inference attacks, the proposed data sanitization strategy can still preserve both data benefits and social structure, while guaranteeing optimal latent-data privacy. To the best of our knowledge, this is the first work that preserves both data benefits and social structure simultaneously and combats against powerful adversaries.
An assessment of maturity from anthropometric measurements.

PURPOSE The range of variability between individuals of the same chronological age (CA) in somatic and biological maturity is large and especially accentuated around the adolescent growth spurt. Maturity assessment is an important consideration when dealing with adolescents, from both a research perspective and youth sports stratification. A noninvasive, practical method predicting years from peak height velocity (a maturity offset value) by using anthropometric variables is developed in one sample and cross-validated in two different samples. METHODS Gender specific multiple regression equations were calculated on a sample of 152 Canadian children aged 8-16 yr (79 boys; 73 girls) who were followed through adolescence from 1991 to 1997. The equations included three somatic dimensions (height, sitting height, and leg length), CA, and their interactions. The equations were cross-validated on a combined sample of Canadian (71 boys, 40 girls measured from 1964 through 1973) and Flemish children (50 boys, 48 girls measured from 1985 through 1999). RESULTS The coefficient of determination (R2) for the boys' model was 0.92 and for the girls' model 0.91; the SEEs were 0.49 and 0.50, respectively. Mean difference between actual and predicted maturity offset for the verification samples was 0.24 (SD 0.65) yr in boys and 0.001 (SD 0.68) yr in girls. CONCLUSION Although the cross-validation meets statistical standards for acceptance, caution is warranted with regard to implementation. It is recommended that maturity offset be considered as a categorical rather than a continuous assessment. Nevertheless, the equations presented are a reliable, noninvasive and a practical solution for the measure of biological maturity for matching adolescent athletes
Dynamic inference of static types for ruby

There have been several efforts to bring static type inference to object-oriented dynamic languages such as Ruby, Python, and Perl. In our experience, however, such type inference systems are extremely difficult to develop, because dynamic languages are typically complex, poorly specified, and include features, such as eval and reflection, that are hard to analyze. In this paper, we introduce constraint-based dynamic type inference, a technique that infers static types based on dynamic program executions. In our approach, we wrap each run-time value to associate it with a type variable, and the wrapper generates constraints on this type variable when the wrapped value is used. This technique avoids many of the often overly conservative approximations of static tools, as constraints are generated based on how values are used during actual program runs. Using wrappers is also easy to implement, since we need only write a constraint resolution algorithm and a transformation to introduce the wrappers. The best part is that we can eat our cake, too: our algorithm will infer sound types as long as it observes every path through each method body---note that the number of such paths may be dramatically smaller than the number of paths through the program as a whole. We have developed Rubydust, an implementation of our algorithm for Ruby. Rubydust takes advantage of Ruby's dynamic features to implement wrappers as a language library. We applied Rubydust to a number of small programs and found it to be both easy to use and useful: Rubydust discovered 1 real type error, and all other inferred types were correct and readable.
Rule based reasoning for network management

This paper focuses on improving network management by the adoption of artificial intelligence techniques. We propose a distributed multi-agent architecture for network management, where a logical reasoner acts as a managing entity capable of directing, coordinating, and triggering monitoring and management actions in the proposed architecture. The logical inference system has been devised to enable automated isolation, diagnosis, and to repair network anomalies, thus enhancing the reliability, performance, and security of the network. The measurements of network events are captured by programmable sensors deployed on the network devices and are collected by the network management entity where they are merged with general domain knowledge, with a view to identifying the root causes of anomalies, and to decide on reparative actions. The relevant results inferred by the logical reasoner and the significant events occurred on the network are stored both in a global DB and in local distributed DBs, in order to enable successive analyses of network events. In order to illustrate the advantages and potential benefits deriving from the reasoning capabilities of our management system, the results of preliminaries experiments are analyzed.
Reconstruction Algorithms Influence the Follow-Up Variability in the Longitudinal CT Emphysema Index Measurements

Objective We wanted to compare the variability in the longitudinal emphysema index (EI) measurements that were computed with standard and high resolution (HR) reconstruction algorithms (RAs). Materials and Methods We performed a retrospective review of 475 patients who underwent CT for surveillance of lung nodules. From this cohort, 50 patients (28 male) were included in the study. For these patients, the baseline and follow-up scans were acquired on the same multidetector CT scanner and using the same acquisition protocol. The CT scans were reconstructed with HR and standard RAs. We determined the difference in the EI between CT1 and CT2 for the HR and standard RAs, and we compared the variance of these differences. Results The mean of the variation of the total lung volume was 0.14 L (standard deviation [SD] = 0.13 L) for the standard RA and 0.16 L (SD = 0.15 L) for the HR RA. These differences were not significant. For the standard RA, the mean variation was 0.13% (SD = 0.44%) for EI -970 and 0.4% (SD = 0.88%) for EI -950; for the HR RA, the mean variation was 1.9% (SD = 2.2%) for EI -970 and 3.6% (SD = 3.7%) for EI -950. These differences were significant. Conclusion Using an HR RA appears to increase the variability of the CT measurements of the EI.
Causal evidence supporting functional dissociation of verbal and spatial working memory in the human dorsolateral prefrontal cortex

The human dorsolateral prefrontal cortex (dlPFC) is crucial for monitoring and manipulating information in working memory, but whether such contributions are domain‐specific remains unsettled. Neuroimaging studies have shown bilateral dlPFC activity associated with working memory independent of the stimulus domain, but the causality of this relationship cannot be inferred. Repetitive transcranial magnetic stimulation (rTMS) has the potential to test whether the left and right dlPFC contribute equally to verbal and spatial domains; however, this is the first study to investigate the interaction of task domain and hemisphere using offline rTMS to temporarily modulate dlPFC activity. In separate sessions, 20 healthy right‐handed adults received 1 Hz rTMS to the left dlPFC and right dlPFC, plus the vertex as a control site. The working memory performance was assessed pre‐rTMS and post‐rTMS using both verbal‐’letter’ and spatial‐’location’ versions of the 3‐back task. The response times were faster post‐rTMS, independent of the task domain or stimulation condition, indicating the influence of practice or other nonspecific effects. For accuracy, rTMS of the right dlPFC, but not the left dlPFC or vertex, led to a transient dissociation, reducing spatial, but increasing verbal accuracy. A post‐hoc correlation analysis found no relationship between these changes, indicating that the substrates underlying the verbal and spatial domains are functionally independent. Collapsing across time, there was a trend towards a double dissociation, suggesting a potential laterality in the functional organisation of verbal and spatial working memory. At a minimum, these findings provide human evidence for domain‐specific contributions of the dlPFC to working memory and reinforce the potential of rTMS to ameliorate cognition.
Self-adaptive composite control for flexible joint robot based on RBF neural network

Among numerous control schemes for flexible joint robots, the main problem is that the full state variable of acceleration and jerk must be known, which are difficult to measure, and the noise may be merged in the main signal. To solve this problem, a self adaptive composite control scheme is developed to control the flexible joint robots with modeling errors and subject to uncertain disturbances, which is based on considering the system as a low dimensional nominal rigid and a linear elastic subsystem. Using this approach, the controller consists of a slow and a fast term, the slow control is based on the well-known Computed Torque method and a RBF neural network based compensating controller. The neural network is trained on line based on Lyapunov theory to compensate for the modeling uncertainties, thus its convergence is guaranteed. The fast term is designed to provide stiffness and damping for eliminating elastic deformation. Simulations are presented for a planner manipulator with two flexible joints, the trajectory tracking results are provided to demonstrate performance of the scheme.
The Spectrum of Intermediate Syndrome Following Acute Organophosphate Poisoning: A Prospective Cohort Study from Sri Lanka

Background Intermediate syndrome (IMS) is a major cause of death from respiratory failure following acute organophosphate poisoning. The objective of this study was to determine repetitive nerve stimulation (RNS) predictors of IMS that would assist in patient management and clinical research. Methods and Findings Seventy-eight consenting symptomatic patients with organophosphate poisoning were assessed prospectively with daily physical examination and RNS. RNS was done on the right and left median and ulnar nerves at 1, 3, 10, 15, 20, and 30 Hz. The study was conducted as a prospective observational cohort study in the Central Province, Sri Lanka. IMS was diagnosed in ten out of 78 patients using a priori clinical diagnostic criteria, and five of them developed respiratory failure. All ten patients showed progressive RNS changes correlating with the severity of IMS. A decrement-increment was observed at intermediate and high frequencies preceding the onset of clinical signs of IMS. As the patient developed clinical signs of IMS, decrement-increment was progressively noted at low and intermediate frequencies and a combination of decrement-increment and repetitive fade or severe decrement was noted at high frequencies. Severe decrement preceded respiratory failure in four patients. Thirty patients developed forme fruste IMS with less severe weakness not progressing to respiratory failure whose RNS was characterized by decrement-increment or a combination of decrement-increment and repetitive fade but never severe decrements. Conclusions Characteristic changes in RNS, preceding the development of IMS, help to identify a subgroup of patients at high risk of developing respiratory failure. The forme fruste IMS with the characteristic early changes on RNS indicates that IMS is a spectrum disorder. RNS changes are objective and precede the diagnosis and complications of IMS. Thus they may be useful in clinical management and research.
SHIRI: buttocks humanoid that represents emotions with visual and tactual transformation of the muscles

In this paper, we propose a novel interface design for "human-robot" communication by focusing on visual and tactual transformation of the muscles. Since recent humanoids may appear as humanoid figures using human-like body gestures and behavior, it is hard to say that they have enough elements to cover the complex composition that is a human. The muscles that constitute the human body work by not only turning joints and generating limb and body movements, but also control skin surface shape and firmness, allowing the various levels of touch response. Therefore, we attempt to approach the creation of sensitive and subtle expression by a humanoid robot using organic constructs. In this project, we produce "SHIRI", which represents emotions with organic movements of the Gluteus Maximus Actuator (GMA). In addition, we also implement user interaction for SHIRI and consider how perceptions the user can obtain by communicating with SHIRI.
Testing techniques for a logic representation generator

Logic based representation can be used for expressing programs and models driven by a grammar. Thus, model analysis and transformation written as declarative paradigm rules can be more expressive. Usually, logic representations are obtained by translators which must be tested as any other software artifacts. We present several testing techniques in the context of logic based representation.
Associations of Maternal Dietary Patterns during Pregnancy with Offspring Adiposity from Birth Until 54 Months of Age

Most studies linking maternal diet with offspring adiposity have focused on single nutrients or foods, but a dietary pattern approach is more representative of the overall diet. We thus aimed to investigate the relations between maternal dietary patterns and offspring adiposity in a multi-ethnic Asian mother–offspring cohort in Singapore. We derived maternal dietary patterns using maternal dietary intake information at 26–28 weeks of gestation, of which associations with offspring body mass index (BMI), abdominal circumference (AC), subscapular skinfold (SS), and triceps skinfold (TS) were assessed using longitudinal data analysis (linear mixed effects (LME)) and multiple linear regression at ages 0, 3, 6, 9, 12, 15, 18, 24, 36, 48, and 54 months. Three dietary patterns were derived: (1) vegetables-fruit-and-white rice (VFR); (2) seafood-and-noodles (SfN); and (3) pasta-cheese-and-bread (PCB). In the LME model adjusting for potential confounders, each standard deviation (SD) increase in maternal VFR pattern score was associated with 0.09 mm lower offspring TS. Individual time-point analysis additionally revealed that higher VFR score was generally associated with lower postnatal offspring BMI z-score, TS, SS, and sum of skinfolds (SS + TS) at ages 18 months and older. Maternal adherence to a dietary pattern characterized by higher intakes of fruit and vegetables and lower intakes of fast food was associated with lower offspring adiposity.
Selling to the Newsvendor: An Analysis of Price-Only Contracts

We consider a simple supply-chain contract in which a manufacturer sells to a retailer facing a newsvendor problem and the lone contract parameter is a wholesale price. We develop a mild restriction satisfied by many common distributions that assures that the manufacturer's problem is readily amenable to analysis. The manufacturer's profit and sales quantity increase with market size, but the resulting wholesale price depends on how the market grows. For the cases we consider, we identify relative variability (i.e., the coefficient of variation) as key: As relative variability decreases, the retailer's price sensitivity decreases, the wholesale price increases, the decentralized system becomes more efficient (i.e., captures a greater share of potential profit), and the manufacturer's share of realized profit increases. Decreasing relative variability, however, may leave the retailer severely disadvantaged as the higher wholesale price reduces his profitability. We explore factors that may lead the manufacturer to set a wholesale price below that which would maximize her profit, concentrating on retailer participation in forecasting and retailer power. As these and other considerations can result in a wholesale price below what we initially suggest, our base model represents a worst-case analysis of supply-chain performance.
Insights into endoscopic imaging

Unarguably, the evolution of optical imaging technologies during the last decades has had a significant impact on the field of endoscopy. Conventional white-light colonoscopy has revealed a limited accuracy (59-84%) in differentiating neoplastic from non-neoplastic polyps [1-3]. Conversely, the optical diagnosis in vivo, at the time of endoscopy, is deemed to be an attractive approach for physicians, patients and health providers. With the advent of Narrow Band Imaging (NBI) and Chromo-Endoscopy (CE), new perspectives for the characterization of polyp features have emerged aiming at a more accurate diagnosis. Of importance, these modalities can be combined with high-resolution and/or magnification endoscopy (ME) to improve optical outcomes [3,4]. However, their wide routine use requires provision of appropriate evidence demonstrating their benefit and cost-efficacy and physician’s education, familiarity and competence.
A Rating System For Gaelic Football Teams: Factors That Influence Success

Abstract AIM: The current investigation aimed to create an objective rating of Gaelic football teams and to examine factors relating to a team's rating. METHOD: A modified version of the Elo Ratings formula (Elo, 1978) was used to rate Gaelic football teams. A total of 1101 competitive senior Inter County matches from 2010-2015 were incorporated into calculations. Factors examined between teams included population, registered player numbers, previous success at adult and underage levels, financial income from the GAA, team expenses and number of clubs in a county. RESULTS: The Elo Ratings formula for Gaelic football was found to have a strong predictive ability, correctly predicting the result in 72.90% of 642 matches over a 6 year period. Strong positive correlations were observed between previous success at senior level, Under 21 level, Under 18 level and current Elo points. Moderate correlations exist between population figures and current Elo points. Moderate correlations are also evident between the number of registered players in a county and the county’s Elo rating points. CONCLUSION: Gaelic football teams can be objectively rated using a modified Elo Ratings formula. In order to develop a successful senior team, counties should focus on the development of underage players, particularly up to U18 and U21 level.
Hierarchical clustering of large text datasets using Locality-Sensitive Hashing

In this paper, we present a hierarchical clustering algorithm of the large text datasets using Locality-Sensitive Hashing (LSH). The main idea of the LSH is to “hash” items several times, in such a way that similar items are more likely to be hashed to the same bucket than dissimilar are. The main drawback of the conventional hierarchical algorithms is a large time complexity (e.g. Single Linkage method has time complexity of ( )) Proposed algorithm reduces the time complexity to ( ). Here, represents the maximum number of items going to the single bucket. is a small constant as compared to n for the large number of buckets. Clustering results of the hierarchical clustering algorithm, that uses LSH, are similar to the clustering results of the classical single linkage method. The main advantage of the hierarchical clustering algorithm, that uses LSH, is a significant increase in speed for large datasets clustering in comparison with classical algorithms.
Activity theory and information seeking

One needs a fairly broad definition of “information science” in order to consider applications of activity theory in the field, and, fortunately, as evidenced by the range of topics ARIST has covered over the years, the editors have encouraged a wide definition. As we shall see, to restrict the definition in any way would result in a very short chapter indeed. Consequently, this review ranges widely, from aspects of psychology (where the theory was first formulated) through education, to information systems and human–computer interaction, digital library development, and information-seeking behavior. The paucity of contributions in what might be labeled “library and information science” is curious. It is common in this broad field to identify “activities” of numerous kinds in academic and practitioner writing. Reference service, information seeking, cataloging, children’s services, and online searching are all described as “activities” and yet “activity theory” has made very little impact. Why would activity theory be needed in library and information science? At present, research in this area is conducted within separate silos, sometimes defined by problem areas such as information retrieval (IR) and information-seeking behavior, sometimes defined by institutional type, such as research libraries, college and university libraries, school libraries, and so on. There is no overarching paradigm for research in these areas and the divisions are perpetuated in different sets of journals related to the different fields. The consequence is that researchers in one area may not understand the problem areas defined by another, may have no awareness of differences in research methodologies, and may have no common language within which to exchange ideas and results. Activity theory could provide that overarching paradigm. The aim of this chapter, therefore, is to review the development of the concept and its application in related fields and to suggest how those applications might find their way into library and information science. CHAPTER 4
Unsupervised Approach for Dialogue Act Classification

This paper presents an unsupervised approach for dialogue act (DA) classification. We used a latent variable model to compress the dimensions of the feature vector. We introduced a paraphraser to reduce the variety of expressions and to solve the pragmatic problem for DA classification. The paraphraser seemed to work well on some DA classifications in the unsupervised approach. The results obtained by the unsupervised approach were compared with the manually annotated labels. A preliminary experiment for semi-supervised tagging was also carried out, and we discuss these results.