Miracle-dz/UniFAR-FTUs huggingface dataset:
ETF: extended tensor factorization model for personalizing prediction of review helpfulness

Online reviews are valuable sources of information for a variety of decision-making processes such as purchasing products. As the number of online reviews is growing rapidly, it becomes increasingly difficult for users to identify those that are helpful. This has motivated research into the problem of identifying high quality and helpful reviews automatically. The current methods assume that the helpfulness of a review is independent from the readers of that review. However, we argue that the quality of a review may not be the same for different users. For example, a professional and an amateur photographer may rate the helpfulness of a review very differently. In this paper, we introduce the problem of predicting a personalized review quality for recommendation of helpful reviews. To address this problem, we propose a series of increasingly sophisticated probabilistic graphical models, based on Matrix Factorization and Tensor Factorization. We evaluate the proposed models using a database of 1.5 million reviews and more than 13 million quality ratings obtained from Epinions.com. The experiments demonstrate that the proposed latent factor models outperform the state-of-the art approaches using textual and social features. Finally, our experiments confirm that the helpfulness of a review is indeed not the same for all users and that there are some latent factors that affect a user's evaluation of the review quality.
Multi-Objective Optimization in Gait Planning of Biped Robot Using Genetic Algorithm and Particle Swarm Optimization Algorithm

This paper deals with multi-objective optimization in gait planning of a 7-dof biped robot ascending and descending some staircases. Both its power consumption as well as dynamic balance margin depends on a few common design parameters. The biped robot should have a maximum dynamic balance margin but at the expense of minimum power. Thus, a conflicting relationship exists between these two objectives. The said gait planning problem has been modeled and solved using two modules of adaptive neuro-fuzzy inference system. The said multi- objective optimization problems have been solved using a genetic algorithm and particle swarm optimization algorithm, separately. Pareto-optimal fronts of solutions have been obtained, which may help a designer to select the most appropriate solution out of several possibilities. Particle swarm optimization algorithm is found to perform better than genetic algorithm, as the former performs both local and global searches simultaneously, whereas the latter is seen to be weak in terms of its local search capability. Therefore, the main contribution of this paper lies with the application of two optimization algorithms to tackle multi- objective optimization in gait planning of biped robot.
Tissue coverage of a hydrophilic polymer-coated zotarolimus-eluting stent vs. a fluoropolymer-coated everolimus-eluting stent at 13-month follow-up: an optical coherence tomography substudy from the RESOLUTE All Comers trial

Aims To compare the tissue coverage of a hydrophilic polymer-coated zotarolimus-eluting stent (ZES) vs. a fluoropolymer-coated everolimus-eluting stent (EES) at 13 months, using optical coherence tomography (OCT) in an ‘all-comers' population of patients, in order to clarify the mechanism of eventual differences in the biocompatibility and thrombogenicity of the devices. Methods and results Patients randomized to angiographic follow-up in the RESOLUTE All Comers trial (NCT00617084) at pre-specified OCT sites underwent OCT follow-up at 13 months. Tissue coverage and apposition were assessed strut by strut, and the results in both treatment groups were compared using multilevel logistic or linear regression, as appropriate, with clustering at three different levels: patient, lesion, and stent. Fifty-eight patients (30 ZES and 28 EES), 72 lesions, 107 stents, and 23 197 struts were analysed. Eight hundred and eighty-seven and 654 uncovered struts (7.4 and 5.8%, P= 0.378), and 216 and 161 malapposed struts (1.8 and 1.4%, P= 0.569) were found in the ZES and EES groups, respectively. The mean thickness of coverage was 116 ± 99 µm in ZES and 142 ± 113 µm in EES (P= 0.466). No differences in per cent neointimal volume obstruction (12.5 ± 7.9 vs. 15.0 ± 10.7%) or other areas–volumetric parameters were found between ZES and EES, respectively. Conclusion No significant differences in tissue coverage, malapposition, or lumen/stent areas and volumes were detected by OCT between the hydrophilic polymer-coated ZES and the fluoropolymer-coated EES at 13-month follow-up.
Is PP Opacity on the Path to FalseBelief

Children exhibit notorious difficulty understanding and using propositional attitude verbs, such as believe or think (cf. de Villiers 1995, Papafragou et al. 2007 and references therein). The delayed acquisition of such mental verbs has been shown to correlate with children’s delayed mastery of a theory of mind (ToM), i.e., the ability to carry out reasoning in attributing False Belief to others. Children fail ToM reasoning tasks at age 3 and succeed around age 4, crucially around the time when they master attitude verbs. Jill de Villiers and colleagues (e.g., de Villiers 1995, de Villiers and de Villiers 2000) have argued that this correlation is, in fact, a causal connection, with full mastery of ToM tasks being contingent on full mastery of sentence complementation: “The child needs the full syntax of mental verbs plus sentential complements in order to represent in his own mind the belief states of other people, not simply to encode them for reporting about them in speech” (de Villiers and Pyers 1997). However, when compared to other verbs, mental verbs have not only complex syntactic properties, but conceptually difficult semantic properties involved in the attribution of belief to others (i.e., modeling the belief state of the subject, e.g., as a set of possible worlds). Do children acquire both kinds of properties at the same time, or is the acquisition timeline more complex? Could it be that young children grasp the belief attribution component of a verb like think (i.e., are able to model a belief state) without fully mastering sentence complementation? This study aims at teasing apart two factors involved in the mastery of attitude verbs, namely, syntactic complexity (sentential complementation and recursion) on the one hand, and conceptual difficulty in attributing (different) beliefs to other minds, on the other. To do so, we contrast sentences involving attitude verbs and sentences modified by Prepositional Phrases such as ‘According to John’, which, we argue, are syntactically simpler (i.e., they lack the recursion property of attitude verbs). We show that children perform better on the latter category, suggesting that children may acquire the ability to attribute beliefs to other minds prior to fully mastering attitude verbs, and that the final stumbling block in the acquisition of attitude verbs is syntactic in essence. Semantically, attitude verbs relate a proposition (expressed by its complement clause) to the mental state of an individual (the subject). For instance, (1) attributes to John the belief that the apple is in the box, without committing the speaker to the truth or falsity of the proposition expressed by the complement clause. Thus, (1) can be uttered truthfully in contexts where the apple is not in the box, so long as John believes that it is:
The role of self-conscious emotions in child protection social work practice: a case study of a local authority safeguarding service

This thesis reports on the first study into the role of self-conscious emotions, namely pride, shame, guilt, humiliation, and embarrassment, in social work practice. Employing a qualitative case study research design, involving the safeguarding service of one local authority, ethnographic constructionist grounded theory methods were used to develop a conceptual understanding of these emotional experiences in the practice of the social workers involved. Integrating data from 246.5 hours of observations, 99 diary entries, 33 assessments written by the social workers, 19 interviews, and 329 pages of documents relating to the organisation, this study analyses the context for these emotional experiences within the case study site, how they were experienced, and their influence on the social workers’ practice. It argues that these emotional experiences are inherently part of practice, influencing what the social workers did and how they did it, which could be manipulated by others to regulate the social workers’ identities so that they acted in institutionally ‘appropriate’ ways. While some social workers felt proud to act in such a manner in some contexts, often resulting in a difficult experience for the parents, most social workers felt constrained, believing they were no longer doing social work, and in some contexts sought to resist the institutional expectations.
MONEY, MEANING AND MATERIALISM: A PAPUA NEW GUINEAN CASE HISTORY

Through the 1980s and 1990s Kubo people of the interior lowlands of Papua New Guinea progressively accommodated money to prevailing social forms. Simultaneously, however, the advent of money changed both people's understandings of the world and social relations that were based in those understandings. We argue that the logic which underlies money may ultimately have contributed more to social change than did money per se. Drawing from this case study we discuss the dual nature of processes entailed in social change. In this paper we discuss the influence of money upon Kubo people of the interior lowlands of Papua New Guinea. We draw upon research conducted between 1986 and 1999. We ask what Kubo people did with money and what money did to Kubo people. We will answer the first question by arguing that over more than a decade Kubo people progressively accommodated money to prevailing social forms. And we will answer the second question by arguing that the advent of money changed both their understandings of the world and the social relations based in those understandings. Taken together, these answers show that a satisfactory accommodation to money was not possible without a change in understandings and the outcome of the latter was that the world to which money was accommodated was not the world that it had initially entered. In short, money mediated a dialectic between material and imagined worlds. With reference to both questions, we take money to be a resource. But, on the one hand, we are dealing with the pragmatics of engagement with that resource and, on the other, with the creative potential arising from that engagement. Our answers to both questions are centrally concerned with change. Those answers, we assert, direct attention not merely to the surface expressions or outcomes of change but also, and more importantly, to the processes that underlie change. The Kubo case is of particular interest because, first, their initial contact with money was not much earlier than 1970; secondly, customary modes of exchange though diverse in form lacked any equivalent of a local indigenous currency; and, thirdly, by the late 1990s Kubo had had no opportunities to participate in regional markets either by cash cropping or as indentured labourers and were abandoning, rather than extolling, the sorts of customary practices that, elsewhere in Melanesia, are
Angle Estimation in Bistatic MIMO Radar System based on Unitary ESPRIT

Angle estimation for a bistatic MIMO radar system based on Unitary Esprit techniques is proposed in this study. The algorithm exploits the dual invariance in distinct directions and centrosymmetry of the bistatic MIMO radar system to provide real valued computations and automatically paired Directions of Departures (DOD) and Directions of Arrival (DOA) estimates. Simulation results show the effectiveness of this method.
Approaches to Speech Recognition based on Speaker Recognition Techniques

We have experimented with approaches to speech recognition that are inspired by work from the speaker recognition community, including an approach that was used in IBM’s best speech recognition submissions in its January 2009 evaluation. Here we explain in general terms the techniques used, without the full technical details. We initially used an approach based on Maximum a Posteriori (MAP) adaptation of Gaussian Mixture Models; this approach gave improvements in a Maximum Likelihood system but did not seem promising to combine with discriminative training. More recently we have used a different approach based on a subspace adaptation of a Gaussian Mixture Model; this gave improvements over a standard system even when combined with discriminative training. It gave very substantial improvements when trained with limited data, which may relate to the smaller number of parameters needed with this approach.
Modern Families

The aim of this study was to examine parenting desire, intention, and experience among a sample of lesbian, gay, and bisexual individuals (N = 568) from Portugal between the ages of 18 and 76 years. Approximately 7% (n = 38) reported having children, with the majority stating a biological kinship with their children primarily from previous heterosexual relationships. The central focus of the current study includes participants without children (n = 530), on average 12 years younger than those with children. Among this group, 65% stated a desire to have children and 56% stated the intention to have children in the future. A multiple regression revealed that young age, involvement in lesbian, gay, bisexual, and transgender events, and low religiosity significantly predicted parenting intention. To further explore the effects of age on parenting intention, an analysis of variance was conducted. Younger participants reported significantly stronger parenting intention, and these differences were detected between each age/developmental group.
A review of kernels on covariance matrices for BCI applications

Recently, covariance matrices have been shown to be interesting features for signal classification and object detection. In this paper, we review and compare the existing kernels on covariance matrices and explore their use for EEG classification in Brain-Computer Interfaces (BCI). This study addresses both experimental and theoretical aspects of the problem. Beside the apparent complexity of the kernels, we show that this approach simplifies the whole BCI system. Finally, we empirically demonstrate that this simpler approach obtains state-of-the-art results on the BCI competition IV dataset 2a.
An Organization Modelling Framework for Multi-Perspective Information System Design

The design of information systems have traditionally been carried out primarily from a technical perspective. Recently, there has been increasing recognition that building truly eeective systems requires taking into account a much broader set of perspectives, representing the viewpoints of various stakeholders, addressing their economic, political, institutional and other concerns. In order to support organizational analysis as an integral part of the overall information system development process, there is need for a practical and systematic framework to utilize insights from these broader perspectives to guide design. This paper outlines a conceptual modelling framework which attempts to provide a systematic way for organizing and using knowledge from multiple organizational analysis perspectives. The framework is made up of three types of models. The rst represents an organizational connguration as a network of interdependencies among actors. The second models the make-up of social actors in terms of roles, positions, and concrete agents. The third uses a network of arguments to express the concerns that actors have about the current and potential organizational conngurations from various perspectives. The framework is illustrated with empirical examples taken from the literature.
SUPERVISED MOTION SEGMENTATION BY SPATIAL-FREQUENTIAL ANALYSIS AND DYNAMIC SLICED INVERSE REGRESSION

In this paper, we propose a new method for supervised motion segmen- tation based on spatial-frequential analysis and dimension reduction techniques. A sequence of images could contain non-ridge motion in the region of interest and the segmentation of these moving objects with deformation is challenging. The aim is to extract feature vectors that capture the spatial-frequential information in the training set and then to monitor the variations of the feature vectors over time in the test set. Given successive images in the training set, we consider a dynamic model that extends the sliced inverse regression in Li (1991). It is designed to capture the intrinsic dimension of feature vectors that holds over a local time scale. These projected features are then used to classify training images and predict forthcoming images in the test set into distinct categories. Theoretic properties are addressed. Simulation studies and clinical studies of a sequence of magnetic resonance images are reported, which confirm the practical feasibility of this new approach.
A Synergistic Methodology for Soil Moisture Estimation in an Alpine Prairie Using Radar and Optical Satellite Data

This paper presents a microwave/optical synergistic methodology to retrieve soil moisture in an alpine prairie. The methodology adequately represents the scattering behavior of the vegetation-covered area by defining the scattering of the vegetation and the soil below. The Integral Equation Method (IEM) was employed to determine the backscattering of the underlying soil. The modified Water Cloud Model (WCM) was used to reduce the effect of vegetation. Vegetation coverage, which can be easily derived from optical data, was incorporated in this method to account for the vegetation gap information. Then, an inversion scheme of soil moisture was developed that made use of the dual polarizations (HH and VV) available from the quad polarization Radarsat-2 data. The method developed in this study was assessed by comparing the reproduction of the backscattering, which was calculated from an area with full vegetation cover to that with relatively sparse cover. The accuracy and sources of error in this soil moisture retrieval method were evaluated. The results showed a good correlation between the measured and estimated soil moisture (R2 = 0.71, RMSE = 3.32 vol.%, p < 0.01). Therefore, this method has operational potential for estimating soil moisture under the vegetated area of an alpine prairie.
SCAD algorithm and its application in analyse of bank loan↑

Feature weighting can be considered an extension of feature selection. Traditional methods of feature weighting assume that feature relevance is invariant over the task's domain. As a result, they learn a single set of weight for the entire data set. In this paper, a proposed algorithm has been used, which is called Simultaneous Clustering and Attribute Discrimination (SCAD) and performs clustering and feature weighting simultaneously. First, the algorithm is analyzed in detail, on this basis, through a series of compare experiments, confirms this algorithm to have the high clustering precision; Finally, the algorithm is applied in the analyzing of the bank loan repaid information, that can efficiently discover the weight association of the main factors in loan information and realize potential customer.
Does only “ The Oz ” himself know who wrote The Royal Book of Oz ? Who Wrote the 15 th Book of Oz ? An Application of Multivariate Analysis to Authorship Attribution

Lyman Frank Baum (1856–1919), according to Martin Gardner, was “America’s greatest writer of children’s fantasy. ” “His Wonderful Wizard of Oz has long been the nation’s best known, best loved native fairy tale” (Gardner and Nye 1957, p. 19). Although initially rejected by publishers in Chicago, it became a children’s best seller by Christmas of 1900. Such an instant success led biographer Russell MacFall to write a chapter about 1900, calling it Baum’s annus mirabilis (Baum and MacFall 1961). After writing the first Oz book, Baum could not separate himself from the fantasy land he created. In less than two decades, he produced a series conDoes only “The Oz” himself know who wrote The Royal Book of Oz?
CTR: An Efficient Cluster-based Top-k Query Routing in MANETs

Top-k queries, which retrieve the k data items with the highest scores, have been receiving considerable attention because of their utility for many real life applications. In this paper, we propose an efficient query routing scheme enabling top-k data retrieval by only necessary nodes, named CTR. CTR employs a new clustering framework for top-k query processing, and in this framework, nodes holding high rank data items become ClusterHeads (CHs), and top-k queries are transmitted between CHs via gateway nodes which belong to multiple clusters. Each CH maintains a record of hop counts between itself and high rank data items, enabling it to judge whether or not to transmit a query on the fly. As a result, CTR reduces overhead while keeping high accuracy of the query result. Extensive experiments have demonstrated that CTR functions well in terms of accuracy of the query result, overhead, and delay.
Symbol Grounding and its Implications for Artificial Intelligence

In response to Searle's well-known Chinese room argument against Strong AI (and more generally, computationalism), Harnad proposed that if the symbols manipulated by a robot were sufficiently grounded in the real world, then the robot could be said to literally understand. In this article, I expand on the notion of symbol groundedness in three ways. Firstly, I show how a robot might select the best set of categories describing the world, given that fundamentally continuous sensory data can be categorised in an almost infinite number of ways. Secondly, I discuss the notion of grounded abstract (as opposed to concrete) concepts. Thirdly, I give an objective criterion for deciding when a robot's symbols become sufficiently grounded for "understanding" to be attributed to it. This deeper analysis of what symbol groundedness actually is weakens Searle's position in significant ways; in particular, whilst Searle may be able to refute Strong AI in the specific context of present-day digital computers, he cannot refute computationalism in general.1
Planar Covariation of Hindlimb and Forelimb Elevation Angles during Terrestrial and Aquatic Locomotion of Dogs

The rich repertoire of locomotor behaviors in quadrupedal animals requires flexible inter-limb and inter-segmental coordination. Here we studied the kinematic coordination of different gaits (walk, trot, gallop, and swim) of six dogs (Canis lupus familiaris) and, in particular, the planar covariation of limb segment elevation angles. The results showed significant variations in the relative duration of rearward limb movement, amplitude of angular motion, and inter-limb coordination, with gait patterns ranging from a lateral sequence of footfalls during walking to a diagonal sequence in swimming. Despite these differences, the planar law of inter-segmental coordination was maintained across different gaits in both forelimbs and hindlimbs. Notably, phase relationships and orientation of the covariation plane were highly limb specific, consistent with the functional differences in their neural control. Factor analysis of published muscle activity data also demonstrated differences in the characteristic timing of basic activation patterns of the forelimbs and hindlimbs. Overall, the results demonstrate that the planar covariation of inter-segmental coordination has emerged for both fore- and hindlimbs and all gaits, although in a limb-specific manner.