Reviewerly/exHarmony huggingface dataset:
Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging

Alzheimer’s disease is a neurodegenerative condition that accelerates cognitive decline relative to normal aging. It is of critical scientific importance to gain a better understanding of early disease mechanisms in the brain to facilitate effective, targeted therapies. The volume of the hippocampus is often used in diagnosis and monitoring of the disease. Measuring this volume via neuroimaging is difficult since each hippocampus must either be manually identified or automatically delineated, a task referred to as segmentation. Automatic hippocampal segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each hippocampus is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms employ voting procedures with voting weights assigned directly or estimated via optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. Our results suggest that incorporating tissue classification (e.g., gray matter) into the label fusion procedure can greatly improve segmentation when relatively homogeneous, healthy brains are used as atlases for diseased brains. The fully Bayesian approach also produces meaningful uncertainty measures about hippocampal volumes, information which can be leveraged to detect significant, scientifically meaningful differences between healthy and diseased populations, improving the potential for early detection and tracking of the disease. Supplementary materials for this article are available online.
Towards recovery of conditional vectors from conditional generative adversarial networks

A conditional Generative Adversarial Network allows for generating samples conditioned on certain external information. Being able to recover latent and conditional vectors from a conditional GAN can be potentially valuable in various applications, ranging from image manipulation for entertaining purposes to diagnosis of the neural networks for security purposes. In this work, we show that it is possible to recover both latent and conditional vectors from generated images given the generator of a conditional generative adversarial network. Such a recovery is not trivial due to the often multi-layered non-linearity of deep neural networks. Furthermore, the effect of such recovery applied on real natural images are investigated. We discovered that there exists a gap between the recovery performance on generated and real images, which we believe comes from the difference between generated data distribution and real data distribution. Experiments are conducted to evaluate the recovered conditional vectors and the reconstructed images from these recovered vectors quantitatively and qualitatively, showing promising results.
Face Image Super-Resolution via Sparse Representation and Local Texture Constraints

In order to solve the problem brought by low-resolution images and to make better use of low-resolution images, this paper proposed a face super-resolution image reconstruction method based on sparse representation and local texture constraints. In the proposed method, sparse representation is used to reconstruct the super-resolution of face image. Moreover, the local texture of the reconstructed image is also used twice before and after reconstruction as constraints to obtain better details and make the reconstructed face image clearer. Experimental results show that this method can effectively reconstruct the corresponding high-resolution images from low-resolution images.
Application of Teager Energy Operator on Linear and Mel Scales for Whispered Speech Recognition

This paper presents experimental results on whispered speech recognition based on Teager Energy Operator for linear and mel cepstral coefficients including the Cepstral Mean Subtraction normalization technique.The feature vectors taken into consideration are Linear Frequency Cepstral Coefficients, Teager Energy based Linear Frequency Cepstral Coefficients, Mel Frequency Cepstral Coefficients and Teager Energy based Mel Frequency Cepstral Coefficients.A speaker dependent scenario is used.For the recognition process, Dynamic Time Warping and Hidden Markov Models methods are applied.Results show a respectable improvement in whispered speech recognition as achieved by using the Teager Energy Operator with Cepstral Mean Subtraction.
Developing English Materials for Efl Learners at Islamic Junior High School

The purpose of this research was to develop an EFL course book for the first semester learners at the Islamic junior high school in Indonesia. The material development includes some procedures of need analysis, material development, expert validation, material revision, try-out and final revision. Based on the results of questionnaires distributed to 106-second graders, the interviews done to the teachers and field notes taken from a document of the syllabus, this research revealed that most of the students and English teachers require English instructional materials embracing religious and general knowledge. After validated by the experts and tried-out to the students, this research finally produced English materials that were supplemented by a teacher‟s guide, student‟s worksheets and a CD containing listening materials.
BioVision: A Biomimetics Platform for Intrinsically Motivated Visual Saliency Learning

We present BioVision, a bio-mimetics platform based on the human visual system. BioVision relies on the foveal vision principle based on a set of cameras with wide and narrow fields of view. We present in this platform a mechanism for learning visual saliency in an intrinsically motivated fashion. This model of saliency, learned and improved on-the-fly during the robot's exploration provides an efficient tool for localizing relevant objects within their environment. The proposed approach includes two intertwined components. On the one hand, a method for learning and incrementally updating a model of visual saliency from foveal observations. On the other hand, we investigate an autonomous exploration technique to efficiently learn such a saliency model. The proposed exploration, based on the intelligent adaptive curiosity (IAC) algorithm is able to drive the robot's exploration so that samples selected by the robot are likely to improve the current model of saliency. We then demonstrate that such a saliency model learned directly on a robot outperforms several state-of-the-art saliency techniques, and that IAC can drastically decrease the required time for learning a reliable saliency model. We also investigate the behavior of IAC in a non static environment, and how well this algorithm can adapt to changes.
Natural data structure extracted from neighborhood-similarity graphs

‘Big’ high-dimensional data are commonly analyzed in low-dimensions, after performing a dimensionality reduction step that inherently distorts the data structure. For a similar analysis, clustering methods are also often used. These methods introduce a bias as well, either by starting from the assumption of a particular, often geometric, property of the clusters, or by using iterative schemes to enhance cluster contours, with consequences that are hard to control. The goal of data analysis should, however, be to encode and detect structural data features at all scales and densities simultaneously, without assuming a parametric form of data point distances, or modifying them. Here, we propose a novel approach that directly encodes data point neighborhood similarities as a sparse graph. Our non-iterative framework permits a transparent interpretation of data, without altering the original data dimension and metric. Several natural and synthetic data applications demonstrate the efficacy of our novel method.
Trident: Distributed Storage, Analysis, and Exploration of Multidimensional Phenomena

Rising storage and computational capacities have led to the accumulation of voluminous datasets. These datasets contain insights that describe natural phenomena, usage patterns, trends, and other aspects of complex, real-world systems. Statistical and machine learning models are often employed to identify these patterns or attributes of interest. However, a wide array of potentially relevant models and parameterizations exist, and may provide the best performance only after preprocessing steps have been carried out. Our distributed analytics platform, Trident, facilitates the modeling process by providing high-level data exploration functionality as well as guidance for creation of effective models. Trident handles (1) data partitioning and storage, (2) metadata extraction and indexing, and (3) selective retrievals or transformations to prepare and generate training data. In this study, we evaluate Trident in the context of a 1.1 petabyte epidemiology dataset generated by a disease spread simulation; such datasets are often used in planning for national-scale outbreaks in animal populations.
Raw Harmonies: Transmediation through Raw Data

In this paper, the authors present the initial findings from explorations on transformation patterns of data in raw format when crossing or transmediating directly (i.e. unaffected by any other form of codification) between audio and visual media. These patterns have allowed the authors to engage in the production of transmediatic artifacts with some degree of control and agency, facilitating purposeful applications of transmediation. The products of such practices will enable a form of literacy, an aesthetic means to identify in visual media artifacts those patterns that could transmediate into useful or appealing sonic artifacts and vice versa.
Photon–photon correlations with a V-type three-level system interacting with two quantized field modes

We carry out a model study on the interaction of a V-type three-level emitter with two quantized cavity modes which are weakly driven by two classical fields. The emitter may be an atom or a molecule with nondegenerate upper levels in general. The lifetimes of the two upper levels are assumed to be much longer than the lifetime of the cavity photons. We calculate the two-time second order coherence function, namely Hanbury Brown–Twiss function g(2)(τ) where τ is the time delay between the two modes. We analyze the photon–photon correlations between the two cavity modes in terms of g(2)(τ). The variation of g(2)(τ) as a function of τ exhibits collapse and revival type oscillations as well as quantum beats for relatively short τ while in the limit τ→∞, g(2)(τ)≃1. We further show that the two cavity field modes are entangled when g(2)(0)>2. We develop a dressed state picture with single photon in each mode to explain the results and use the negativity of the partial transpose of the reduced field density matrix to show the entanglement between field modes. The model presented in this paper may be useful for generating entangled photon pairs, and also manipulating photon–photon correlations with a cavity QED set up.
Handheld Computers In The Classroom And Laboratory

Penn State Abington has integrated the student use of personal digital assistant (PDA) technology into several Information Sciences and Technology and engineering courses in order to foster active and collaborative learning experiences in the classroom and laboratory.Activities supported by the use of these handheld computers include electronic team exams, distribution of notes, programming, collaborative database projects, and access to web-based materials.Student access to handheld, mobile computers provides opportunities for improvements in both learning effectiveness and in the efficient delivery of instruction.
Semantic Similarity Functions and Measures

Similarity is the process through which two or more objects are compared in order to identify to what extent they are alike. This is useful, for instance, when there is the need to identify common elements among a set of objects or when a new object has to be classified w.r.t. the others. Similarity is a very generic notion spanning among different disciplines from psychology to computer science. In this paper, the focus will be on computational methods that exploit ontologies or search engines as primary source of “background” knowledge. In particular, after surveying on the most popular similarity measures recently proposed, the Similarity Library will be presented, which has the aim to provide researchers and practitioners with a flexible tool encompassing several similarity measures both between words and sentences.
Causal reasoning and symbolic relationships in Medieval Illuminations

This work applies knowledge engineering’s techniques to medieval illuminations. Inside it, an illumination is considered as a knowledge graph which was used by some elites in the Middle Ages to represent themselves as a social group and exhibit the events in their lives, and their cultural values. That graph is based on combinations of symbolic elements linked each to others with semantic relations. Those combinations were used to encode visual metaphors and influential messages whose interpretations are sometimes tricky for not experts. Our work aims to describe the meaning of those elements through logical modelling using ontologies. To achieve that, we construct logical reasoning rules and simulate them using artificial intelligence mechanisms. The goal is to facilitate the interpretation of illuminations and provide, in a future evolution of current social media, logical formalisation of new encoding and information transmission services.
"Press Space to Fire": Automatic Video Game Tutorial Generation

We propose the problem of tutorial generation for games, i.e. to generate tutorials which can teach players to play games, as an AI problem. This problem can be approached in several ways, including generating natural language descriptions of game rules, generating instructive game levels, and generating demonstrations of how to play a game using agents that play in a human-like manner. We further argue that the General Video Game AI framework provides a useful testbed for addressing this problem.
Towards Contingent World Descriptions in Description Logics

The philosophical, logical, and terminological junctions between Description Logics (DLs) and Modal Logic (ML) are important because they can support the formal analysis of modal notions of ‘possibility’ and ‘necessity’ through the lens of DLs. This paper introduces functional contingents in order to (i) structurally and terminologically analyse ‘functional possibility’ and ‘functional necessity’ in DL world descriptions and (ii) logically and terminologically annotate DL world descriptions based on functional contingents. The most significant contributions of this research are the logical characterisation and terminological analysis of functional contingents in DL world descriptions. The ultimate goal is to investigate how modal operators can – logically and terminologically – be expressed within DL world descriptions.
One-Shot SADI-EPE: A Visual Framework of Event Progress Estimation

In many practical engineering applications, the number of actions that have been finished should be known, particularly for an untrimmed video sequence that includes an event with a series of actions, it is important to know the number of actions that have been finished. In this paper, we termed this process as visual event progress estimation (EPE). However, the research related to this problem is few in the research community. To solve this problem, a visual human action analysis-based framework, namely one-shot simultaneously action detection and identification (SADI)-EPE, is presented in this paper. The visual EPE is modeled as an online one-shot learning-based problem; sliding window and attention-based bag of key poses formulate our framework. Unlike most of the action analysis methods relying on a number of training data of some predefined classes, our method can realize SADI for any event if one sample of the event is given, which makes it feasible for practical applications. At the same time, not only SADI but also the progress estimation of the event can be realized by our algorithm. In terms of methodology, the key pose is defined by an invariant pose descriptor from skeletal data and silhouette data. Moreover, in order to extract representative and discriminative poses from one training sample, we present a new bidirectional kNN-based attention weighted key pose selection method, which can filter the unrelated actions and model different importance of various key poses. In addition, an attention-based multi-modal fusion scheme, which addresses the difficulty of high-dimensional features and few training samples, is proposed to augment the performance of our algorithm. Finally, we propose an evaluation criterion for the estimation problem. Extensive results demonstrated the efficacy of our proposed framework.
A fuzzy approach to prioritize DEA ranked association rules

Association rule mining discovers interesting information from large databases. Frequency, reliability and domain knowledge form the multiple criteria for evaluation these association rules. Data envelopment analysis (DEA) is a popular technique used to rank association rules based on the previously mentioned multiple criteria. A decision maker might be interested to have a priority list of these ranked rules based on business and situational requirements. We present an approach to prioritise DEA ranked association rules based on the preference and desirability of the decision maker for different criteria. A modified generalised fuzzy evaluation method (MGFEM) obtains vector-valued fuzzy scores of a group of decision makers and aggregate them to form a preference. A fuzzy logic-based decision support mechanism prioritises these rules based on the decision maker's desirability using the membership function and preference obtained from MGFEM. An example of DEA ranked association rules is presented to explain this innovative approach for prioritisation.
The compared costs of domination, location-domination and identification

Let G = (V, E) be a finite graph and r ≥ 1 be an integer.and for any two distinct vertices x ∈ V , y ∈ V , we have B r (x) ∩ C = B r (y) ∩ C. We denote by γ r (G) (respectively, ld r (G) and id r (G)) the smallest possible cardinality of an r-dominating code (respectively, an r-locating-dominating code and an r-identifying code).We study how small and how large the three differences id r (G)-ld r (G), id r (G)-γ r (G) and ld r (G)γ r (G) can be.
Hybrid Diffusion: Spectral-Temporal Graph Filtering for Manifold Ranking

State of the art image retrieval performance is achieved with CNN features and manifold ranking using a k-NN similarity graph that is pre-computed off-line. The two most successful existing approaches are temporal filtering, where manifold ranking amounts to solving a sparse linear system online, and spectral filtering, where eigen-decomposition of the adjacency matrix is performed off-line and then manifold ranking amounts to dot-product search online. The former suffers from expensive queries and the latter from significant space overhead. Here we introduce a novel, theoretically well-founded hybrid filtering approach allowing full control of the space-time trade-off between these two extremes. Experimentally, we verify that our hybrid method delivers results on par with the state of the art, with lower memory demands compared to spectral filtering approaches and faster compared to temporal filtering.
Detection of Breast Cancer using Digital Image Processing Techniques

Breast cancer, a major public health contention and currently causes an increased rate of cancer death in women. The preeminent intent of this project in medical diagnostics is by using mammography, that is a unique imaging technique in medicine for examining the breasts. A higher quality mammographic images (for example electronic pictures) are stored, mammography method (i.e exam) is performed, which is a prior stage for detection and diagnosis of bosom’s malignant growth. In order to detect the tumor FCM algorithm for segmentation is used and the features are extracted by using multi-level wavelet transformation technique with PCA and then some features are added with GLCM features. Further, those segmented region features are extracted and the dataset is trained and tested completely. The images are classified by using SVM, KNN, Tree classifier, Neural Networks or Naive Bayes classifier. Finally, the images from Kaggle dataset are compared and categorized as normal, benign or malignant tumors.
Photo-realistic continuous digital zooming for an asymmetrical dual camera system

Asymmetrical dual camera systems are becoming more and more popular among many commercial products and they are expected to provide digital zooming images. Existing related algorithms such as super-solution and image fusion fail to reconstruct photo-realistic results because of the sampling rate inconsistency, especially when the focal lengths of cameras vary largely. Nevertheless, magnification factor of super-resolution algorithms are fixed and inflexible as well. By looking into the inherent drawbacks of existing super-resolution algorithm, a new photo-realistic continuous digital zooming algorithm is then proposed in this paper on the basis of a novel super-resolution framework and a band-inpainting algorithm. Experiment result illustrates that proposed algorithm combines the advantages of both super-resolution algorithm and image inpainting algorithm: it reconstructs band information with super-resolution methods, and introduce image inpainting algorithm to reconstruct poorly super-resolved texture areas to produce state-of-the-art photo-realistic digital zooming images.
bison Instantiating the Whitened Swap-Or-Not Construction

We give the first practical instance – bison – of the Whitened Swap-Or-Not construction. After clarifying inherent limitations of the construction, we point out that this way of building block ciphers allows easy and very strong arguments against differential attacks.
Semi-supervised soft margin consistency based multi-view maximum entropy discrimination

Multi-view maximum entropy discrimination (MVMED) and alternative MVMED (AMVMED) are proposed as extensions of maximum entropy discrimination (MED). In MVMED and AMVMED, they use hard margin consistency principle that the decision of margin parameter is related to classifier parameter directly. While the decision always be indirectly in practice, thus soft margin consistency based multi-view maximum entropy discrimination (SMVMED) has been proposed. But it is found that SMVMED is only adaptive to supervised problems. In this paper, we extend the model of SMVMED to the semi-supervised problems and develop a semi-supervised SMVMED (SSMVMED). Related experiments on multi-view data sets from different aspects have validated the effectiveness of SSMVMED theoretically and empirically. From the experiments, it is found that (1) compared with SMVMED, the average test accuracy of SSMVMED has a 2% enhancement; (2) SSMVMED costs more training time than SMVMED and the extra time is not more than 10%; (3) in terms of the generation of additional unlabeled instances, ‘mid’ strategy has a better test accuracy than ‘self’ and taking all instances to get the center brings a better test accuracy as well; (4) with SSMVMED, the applications to estimation problem and regression problem will be more feasible.