Miracle-dz/UniFAR-FTUs huggingface dataset:
Lifted First-Order Belief Propagation

Unifying first-order logic and probability is a long-standing goal of AI, and in recent years many representations combining aspects of the two have been proposed. However, inference in them is generally still at the level of propositional logic, creating all ground atoms and formulas and applying standard probabilistic inference methods to the resulting network. Ideally, inference should be lifted as in first-order logic, handling whole sets of indistinguishable objects together, in time independent of their cardinality. Poole (2003) and Braz et al. (2005, 2006) developed a lifted version of the variable elimination algorithm, but it is extremely complex, generally does not scale to realistic domains, and has only been applied to very small artificial problems. In this paper we propose the first lifted version of a scalable probabilistic inference algorithm, belief propagation (loopy or not). Our approach is based on first constructing a lifted network, where each node represents a set of ground atoms that all pass the same messages during belief propagation. We then run belief propagation on this network. We prove the correctness and optimality of our algorithm. Experiments show that it can greatly reduce the cost of inference.
Discrimination of bipeds from quadrupeds using seismic footstep signatures

Seismic sensors are widely used to detect moving targets in the ground sensor network, and can be easily employed to discriminate human and quadruped based on their footstep signatures. Because of the complex environmental conditions and the non-stationary nature of the seismic signals, footstep detection and classification is a very challenging problem. The solution to this problem has various applications such as border security, surveillance, perimeter protection and intruder detection. Previous works in the domain of seismic detection of human vs. quadruped have relied on the cadence frequency-based models. However, cadence-based detection alone results in high false alarms. In this paper, we describe a seismic footstep database and present classification results based on support vector machine (SVM). We demonstrate that in addition to applying a good classification algorithm, finding robust features are very important for seismic discrimination.
Assessing level of affective learning of undergraduate nursing students at a university in the Western Cape regarding rendering prevention of mother-to-child transmission services

Nursing assessment and teaching strategies focus mainly on the cognitive and psychomotor areas of learning. This is eminent in research studies that state that the cognitive approach is taken because of challenges in investigating the affective domain. This study assesses undergraduate nursing students’ level of affective learning with regard to rendering prevention of mother-to-child transmission (PMTCT) services, which is key to working with at-risk populations. A qualitative exploratory descriptive and contextual design was utilised to assess the level of affective learning based on the students’ reflective journaling. Ninety reflective journals were analysed by means of content analysis using Atlas.ti 7. Students were able to reflect at all the levels of the affective domain, and it was found that the use of reflective journaling is an appropriate teaching and assessment tool to enhance this learning domain. However, further research is needed on the interrelationship between the affective domain and the cognitive and psychomotor domains.
PE followed me home from school today: Effects of the Activity Challenge on youth summer physical activity

PE Followed Me Home From School Today: Effects of the Activity Challenge on Youth Summer Physical Activity Kacey DiGiacinto The prevalence of overweight and obese youth in the nation is growing at an alarming pace (Centers for Disease Control and Prevention, 2008; West Virginia Department of Health and Human Resources, 2007). Currently, 3.8% of elementary schools, 7.9% of middle schools, and 2.1% of high schools provide daily physical education (Jones, Axelrad, & Wattigney, 2007). That means that a majority of America’s youth are not provided with the opportunity to accumulate the recommended daily amounts of physical activity. With formal physical education in mind schools should be focusing on increasing youth out-of-school physical activity. Teacher assigned, out-of-school, physical activity, self-management programs serve to provide the nation’s youth with the best opportunity for increasing physical activity, without taking away from the existing in-school curriculum. The Activity Challenge (AC) is a physical activity program, modeled after the Summer Shape-Up Challenge, designed to increase youth physical activity during the summer and after school (National Association for Sport and Physical Education, 2005). To evaluate the effectiveness of the AC the researcher used a single-case combined ABABC reversal design and an ABC delayed multiple baseline design. The dependent variable was daily step count as measured by the New Lifestyles 800 brand of the Yamax Digiwalker pedometer. Steps counts indicated that the AC was not effective at increasing or maintaining participant out-of-school physical activity.
Prostate Needle Biopsy Reporting: How Are the Surgical Members of the Society of Urologic Oncology Using Pathology Reports to Guide Treatment of Prostate Cancer Patients?

Recent trends in prostate needle biopsy reporting have resulted in the inclusion of more information and new diagnostic categories. The goal of the current study was to survey surgical Members of the Society of Urologic Oncology to determine what information academic urologists consider important in the management of their prostate cancer (PCa) patients. A questionnaire was developed to investigate several areas of PCa biopsy reporting, which vary from institution to institution. Urologists were sent questionnaires and asked to return anonymous responses; 42 questionnaires were completely evaluated with a response rate of 76% (42 of 55). The urologists targeted for this survey were highly experienced with an average of 22 years in clinical practice (range, 6–35 years). On average, they performed 92 radical prostatectomies per year and 449 over the past 5 years (range, 60–1500) for a group total of 18,840 radical prostatectomies; 94% have their patient’s biopsy reviewed prior to surgery. The primary and secondary Gleason pattern was required by 60% (25 of 42) of the respondents. In prostate needle biopsies containing only a single minute focus of PCa, only 41% (17 of 42) of respondents would request a Gleason score if not provided in the initial report. Interestingly, in biopsies with multiple positive cores from separate locations, 81% (34 of 42) use the highest Gleason score, regardless of the overall percentage involvement, to determine their treatment plan. Other pathology parameters requested by the respondents in descending order included: % involvement of the core by PCa (67%), the presence or absence of perineural invasion (38%), the number of cores with PCa (33%), and the length of core involvement (29%). Only 24% (10 of 42) of respondents use perineural invasion status to guide nerve-sparing surgery. The more radical prostatectomies performed by a surgeon, the greater the likelihood that they considered perineural invasion clinically important (Mann-Whitney, two-tailed, P = 0.015). The term atypical small acinar proliferation was uniformly considered sufficient to re-biopsy by 98% (41 of 42) of the urologists. This is the first study to survey urologists as to what information they require from prostate needle biopsy reports in their treatment planning of men with clinically localized PCa. With the exception of Gleason score, the use of detailed pathology information was variably used to guide treatment. PNI was not considered important by the majority of respondents. In contrast, atypical small acinar proliferation, a more recent diagnostic category, was recognized as important by nearly all respondents. Knowledge of how pathology biopsy reports are being used should help evaluate what data should be uniformly part of standard biopsy pathology report and help improve communication between pathologists and urologists.
A case study in specification defects detection using statecharts

There are two types of software defects: (1)implementation defects(i.e., discrepancies between a implementation and its specification), and (2)specification defects(Systems implemented correctly may also fail due to its defective specification design). This paper presents a specification defects detection method called Specification Defects Detection Using Statecharts (SDDS), and applies it to 5 classes from open source software. SDDS converts source codes to statecharts and then find intrinsic design defects or risks of specifications from their statecharts. We applied this method in our case study and proved that at least 3 types of specification defects exist and can be detected by SDDS.
TANGENT: a novel, 'Surprise me', recommendation algorithm

Most of recommender systems try to find items that are most relevant to the older choices of a given user. Here we focus on the "surprise me" query: A user may be bored with his/her usual genre of items (e.g., books, movies, hobbies), and may want a recommendation that is related, but off the beaten path, possibly leading to a new genre of books/movies/hobbies. How would we define, as well as automate, this seemingly selfcontradicting request? We introduce TANGENT, a novel recommendation algorithm to solve this problem. The main idea behind TANGENT is to envision the problem as node selection on a graph, giving high scores to nodes that are well connected to the older choices, and at the same time well connected to unrelated choices. The method is carefully designed to be (a) parameter-free (b) effective and (c) fast. We illustrate the benefits of TANGENT with experiments on both synthetic and real data sets. We show that TANGENT makes reasonable, yet surprising, horizon-broadening recommendations. Moreover, it is fast and scalable, since it can easily use existing fast algorithms on graph node proximity.
Brain tumor segmentation using cascaded deep convolutional neural network

Gliomas are the most common and threatening brain tumors with little to no survival rate. Accurate detection of such tumors is crucial for survival of the subject. Naturally, tumors have irregular shape and can be spatially located anywhere in the brain, which makes it a challenging task to segment them accurately enough for clinical purposes. In this paper, an automated segmentation algorithm for brain tumor using deep convolutional neural networks (DCNN) is proposed. Deep networks tend to have a lot of parameters thus over-fitting is almost always an issue especially when data are sparse. Max-out and drop-out layers are used to reduce the chances of over-fitting since data are scant. Patch based training method is used for the model where two types of patches sized 37×37 and 19×19 with same center pixel are selected. The proposed algorithm includes preprocessing in which images are normalized and bias field corrected, and post processing where small false positives are removed using morphological operators. BRATS 2013 dataset is used for evaluation of the proposed method, where it outperforms state-of-the-art methods with similar settings in key performance indicators.
Susceptibility in Inhomogeneous Random Graphs

We study the susceptibility, i.e., the mean size of the component containing a random vertex, in a general model of inhomogeneous random graphs. This is one of the fundamental quantities associated to (percolation) phase transitions; in practice one of its main uses is that it often gives a way of determining the critical point by solving certain linear equations. Here we relate the susceptibility of suitable random graphs to a quantity associated to the corresponding branching process, and study both quantities in various natural examples.
3D Triangulations for Industrial Applications

This paper presents a methodology for creating 3D non-structured meshes for industrial applications, which honor all the boundaries of the domain. The input for the system is a multi-region 3D non-manifold model, possibly containing dangling faces, which poses a number of difficulties for standard 3D triangulation frameworks. The main contribution of this work consists of a detailed description of how these difficulties were overcome in a real implementation.
DEVELOPMENT OF A VARIABLE CAPACITY ROTARY COMPRESSOR PART II : DESIGN OF RELIABLE CLUTCHING MECHANISM

A novel rotary compressor that controls capacity mechanically without any electronic frequency modulation has been developed. Its major difference from the typical variable capacity compressors that have two cylinders is the variable displacements alternatively operate at each stage according to the external loading condition. Due to its structural characteristics having an idle cylinder and controlling the eccentricity by rotation direction of shaft, several problems against the stability of the system may occur. To get rid of such reliability problems, a latching mechanism is developed with the aid of TRIZ, which was developed by Altshuller. In this paper, we will present brief explanations on the new mechanism and test results from a full-scale prototype.
Collaborative Filtering with Aspect-Based Opinion Mining: A Tensor Factorization Approach

Collaborative filtering (CF) aims to produce user specific recommendations based on other users' ratings of items. Most existing CF methods rely only on users' overall ratings of items, ignoring the variety of opinions users may have towards different aspects of the items. Using the movie domain as a case study, we propose a framework that is able to capture users' opinions on different aspects from the textual reviews, and use that information to improve the effectiveness of CF. This framework has two components, an opinion mining component and a rating inference component. The former extracts and summarizes the opinions on multiple aspects from the reviews, generating ratings on the various aspects. The latter component, on the other hand, infers the overall ratings of items based on the aspect ratings, which forms the basis for item recommendation. Our core contribution is in the proposal of a tensor factorization approach for the rating inference. Operating on the tensor composed of the overall and aspect ratings, this approach is able to capture the intrinsic relationships between users, items, and aspects, and provide accurate predictions on unknown ratings. Experiments on a movie dataset show that our proposal significantly improves the prediction accuracy compared with two baseline methods.
Bayesian estimation via sequential Monte Carlo sampling: Unconstrained nonlinear dynamic systems

Precise estimation of state variables and model parameters is essential for efficient process operation. Bayesian formulation of the estimation problem suggests a general solution for all types of systems. Even though the theory of Bayesian estimation of nonlinear dynamic systems has been available for 4 decades, practical implementation has not been feasible because of computational and methodological challenges. Consequently, most existing methods rely on simplifying assumptions to obtain a tractable but approximate solution. For example, extended Kalman filtering linearizes the process model and assumes Gaussian prior and noise. Moving-horizon-based least-squares estimation also assumes Gaussian or other fixed-shape prior and noise to obtain a least-squares optimization problem. This approach can impose constraints but is nonrecursive and requires computationally expensive nonlinear or quadratic programming. This paper introduces sequential Monte Carlo sampling for Bayesian estimation of chemical proce.
Xampling-enabled coexistence in spectrally crowded environments

We present a composite suite of technologies for spectral coexistence of existing communication and radar systems using the Xampling framework. For a stand-alone communication system, we consider a cognitive radio (CRo) that receives multiband signals with unknown carrier frequencies and directions of arrival, and demonstrate joint spectrum sensing via CompreSsed CArrier and Direction-ofarrival Estimation (CaSCADE) with an L-shaped configuration of two uniform linear arrays. For radars operating in bands with widespread spectral interference, we present an X-band prototype of cognitive sub-Nyquist multiple input multiple output (MIMO) radar (SUMMeR). The prototype allows sampling in both spatial and spectral domains at sub-Nyquist rates and cognitively transmits over multiple narrow subbands. Finally, we demonstrate Spectral Coexistence via Xampling (SpeCX) technology that shows joint operation of both - cognitive radio and cognitive monostatic radar - over a common spectrum. Our solutions to individual and joint operation of communication and radar systems supersede existing spectrum sharing technologies that require a compromise over performance of one of the systems.
C ORRECTION OF S ATURATED R EGIONS IN RGB C OLOR S PACE

In a digital image, color representation of a digital image sensor is limited to a narrow dynamic range. Especially, when extremely bright light is captured, the original color of a scene is saturated to the maximum value, up to which a digital image sensor can represent the color. This paper proposes an algorithm that corrects the color in a saturated region, where the original color is distorted and lost. For natural correction, i.e., to minimize the artifacts near the boundary of a saturated region, the proposed method uses the weighted sum of color value(s) in the saturated color channel(s) of neighborhood of saturated regions. In determining the weight of each pixel, saturation, hue, and color values are used with the certainty map. Using the certainty map, the proposed method can reliably distinguish the unsaturated and already desaturated neighboring pixels from the remaining pixels. Then, the proposed correction method computes the weight function using saturation, hue, and color values. Therefore, the proposed algorithm can get reliable corrected colors. Comparison of experimental results of the proposed and existing correction methods shows the effectiveness of the proposed saturated region correction method in the view of natural color restoration.
Online error detection and recovery in dataflow execution

The processor industry is well on its way towards manycore processors that comprise of large number of simple cores. The shift towards multi and manycores calls for new programming paradigms suitable for exploiting the inherent parallelism in applications. Dataflow execution was shown to be a good option for programming in such environments. It is well-known that as CMOS technology continues to scale, it becomes more prone to transient and permanent hardware faults. In this paper we present a novel mechanism for error detection and recovery that focuses on transient errors in dataflow execution. Due to the inherently parallel nature of dataflow, our solution is completely distributed and synchronizes only cores that have data dependencies between them, as opposed to prior work on error recovery that in general rely on global synchronization of the system. We evaluate the proposed solution via a software implementation on top of a dataflow runtime. Experimental results show that error detection overhead is highly related to the pressure on the memory bus. In memory bound applications, performance is found to deteriorate, while for other benchmarks, the observed overhead is less than 23%. We find no comparable previous work to contrast these results.
Communautés Créatives et Langage de Codification

La communication propose une analyse typologique des identites des communautes creatives. Plus particulierement, elle remet en cause l'apparente homogeneite des communautes epistemiques et des communautes de connaissance en procedant, d'une part, au moyen de comparaison entre differents programmes de recherche developpes en sociologie des sciences et du politique, d'autre part, en conduisant une analyse sociolinguistique des enonces lexicaux issus des auto-presentation des communautes. Cette analyse critique aboutit a segmenter l'ensemble des groupes creatifs en deux familles : les communautes au profil stable et regulier et a visee scientifique ou technique, et les collectifs au profil foisonnant et instable et a visee de transformation sociale.
Experiments on Sense Annotations and Sense Disambiguation of Discourse Connectives

Discourse connectives can be analyzed as discourse level predicates which projectpredicate-argument structure on a par with verbs at the sentence level. The PennDiscourse Treebank (PDTB) reﬂects this view in its design providing annotation ofthe discourse connectives and their arguments. Like verbs, discourse connectiveshave multiple senses. We present a set of manual sense annotation studies for threeconnectives whose arguments have been annotated in the PDTB. Using syntacticfeatures computed from the Penn Treebank and a simple MaxEnt model, we haveachieved some success in automatically disambiguating among their senses.
A note on test oracles and semantics of algebraic specifications

Algebraic testing is an automated software testing method based on algebraic formal specifications. It tests if a program correctly implements an algebraic specification by checking if the equations of the specification are satisfied. One of the key techniques of algebraic testing is the use of observation contexts as a test oracle so that the comparison of values of structured data can be realised by comparing values of simple data types. This leads to a behavioural semantics of algebraic specifications and divides sorts into observable and non-observable. To represent the structure of object-oriented programs and to facilitate incremental integration testing, this paper extends the notion of observable sorts by introducing a partial ordering between sorts to represent the importation relation between classes. In this framework, the validity of test oracles is formally proved in final algebra semantics.
Characterization of viscoelastic behaviour of a molding compound with application to delamination analysis in IC packages

Over the glass transition temperature, molding compounds strongly exhibit viscoelastic behavior which causes their Young's moduli to be not only temperature-dependent but also time-dependent. In the present study, the stress relaxation test is used for the characterization of the viscoelasticity of an epoxy molding compound. The viscoelastic properties of the epoxy molding compound are determined in terms of Prony coefficients, relaxation time and time-temperature shift factors. Furthermore, the effect of viscoelasticity on the delamination in IC packaging is investigated
Tracking the time course of object categorization using event-related potentials.

Object categorization processes were investigated by measuring event-related potentials while subjects categorized objects at the superordinate (e.g. animal), basic (e.g. dog) and subordinate (e.g. beagle) levels of abstraction. An enhanced negative deflection (N1) was found at posterior recording sites for subordinate level categorizations compared with basic level categorizations and was interpreted as a marker of increased visual analysis. In contrast, superordinate level categorizations produced a larger frontal negativity relative to basic level categorizations and were interpreted as an indicator of increased semantic processing. These results suggest a neurophysiological basis for the separate cognitive processes responsible for subordinate and superordinate object categorizations.