Reviewerly/exHarmony huggingface dataset:
How many models/atlases are needed as priors for capturing anatomic population variations?

Many medical image processing and analysis operations can benefit a great deal from prior information encoded in the form of models/atlases to capture variations over a population in form, shape, anatomic layout, and image appearance of objects. However, two fundamental questions have not been addressed in the literature: "How many models/atlases are needed for optimally encoding prior information to address the differing body habitus factor in that population?" and "Images of how many subjects in the given population are needed to optimally harness prior information?" We propose a method to seek answers to these questions. We assume that there is a well-defined body region of interest and a subject population under consideration, and that we are given a set of representative images of the body region for the population. After images are trimmed to the exact body region, a hierarchical agglomerative clustering algorithm partitions the set of images into a specified number of groups by using pairwise image (dis)similarity as a cost function. Optionally the images may be pre-registered among themselves prior to this partitioning operation. We define a measure called Residual Dissimilarity (RD) to determine the goodness of each partition. We then ascertain how RD varies as a function of the number of elements in the partition for finding the optimum number(s) of groups. Breakpoints in this function are taken as the recommended number of groups/models/atlases. Our results from analysis of sizeable CT data sets of adult patients from two body regions - thorax (346) and head and neck (298) - can be summarized as follows. (1) A minimum of 5 to 8 groups (or models/atlases) seems essential to properly capture information about differing anatomic forms and body habitus. (2) A minimum of 150 images from different subjects in a population seems essential to cover the anatomical variations for a given body region. (3) In grouping, body habitus variations seem to override differences due to other factors such as gender, with/without contrast enhancement in image acquisition, and presence of moderate pathology. This method may be helpful for constructing high quality models/atlases from a sufficiently large population of images and in optimally selecting the training image sets needed in deep learning strategies.
Proposed similarity measure using Bhattacharyya coefficient for context aware recommender system

Context Aware Recommender Systems exploit specific situation of users for recommendations, hence are more accurate and satisfactory. Neighborhood based collaborative filtering is the most successful approach in this area owing of its simplicity, intuitiveness, efficiency and domain independence. Th e key of this approach is to find similarity between users or items using user–item–context rating matrix. Typically, context aware datasets are highly sparse since there are not enough or no preferences under most contextual conditions. Traditional similarity measures such as Pearson correlation coefficient, Cosine and Mean squared difference suffer from co-rated item problem and do not consider contextual conditions of the users. Therefore, these measures are not effective for sparse datasets. Therefore, the aim of this paper is to propose a new similarity measure and its variants based on Bhattacharyya Coefficient which are suitable for sparse datasets weighted by contextual similarity. Subsequently, we have applied them in neighborhood based algorithms where each component is contextually weighted. The experiments are performed on two contextually rich datasets which are especially designed to do personalization research instead traditional well known datasets. The results for Individual and Group recommendations indicate that the proposed similarity measure based algorithms have significantly increased the accuracy of predictions over traditional Pearson correlation coefficient measure based algorithms.
Saliency detection via background and foreground null space learning

In this paper, we present a novel bottom-up salient object detection approach by exploiting the relationship between the saliency detection and the null space learning. A key observation is that saliency of an image segment can be estimated by measuring the distance to the single point, which represents the background or foreground salient samples in the null spaces. We apply the null Foley–Sammon transformation to model the null spaces of the background samples or foreground salient samples, where the potentially large and complex intra-class variations of the samples are totally removed and the specific features of the respective classes are represented by a single point. Afterward, we formulate the separation of the saliency regions from the background as a distance measurement to this single point in the null space. An optimization algorithm is devised to fuse the background samples based saliency map and foreground samples based saliency map. Results on five benchmark datasets show that the proposed method achieves superior performance compared with the newest state-of-the-art methods in terms of different evaluation metrics, especially for complex natural images.
Queueing Network Based Driver Model for Varying Levels of Information Processing

With growing interest in the topic of smart computing and context-aware services, identifying driver's intention has become important in automotive industry. Among existing studies on driver assistance systems, few studies have used the concept of the levels of processing (LOP) to understand driver's information processing. This paper introduces experimental and computational studies that connect human behavior data to internal goal of a driver when an in-vehicle task involves visual search. In the case of consuming information displayed on a car instrument cluster, we considered two levels of information processing, perceptual and cognitive. Through an empirical study, we observed different human oculomotor behaviors that depend on the LOP by evaluating the reaction time (RT) and eye movement patterns. Further, we suggested different ways of information processing that can be represented as different routes in the underlying cognitive architecture of a queueing network. Simulation study demonstrated that trends in simulated oculomotor behavior were similar to those observed in the experiment, that is, RT at the cognitive LOP was shorter than that at the perceptual LOP, while eyes fixated for shorter duration and with lower frequency. In terms of application, this study reveals the possibility of using human-generated data for evaluating the innate purposes of drivers. Together with further development of sensors and invasive computing, the approach proposed in this paper could assist the realization of cognitive cars by understanding drivers' intent using eye tracking technology.
Meet cyrus

Being declarative, SQL stands a better chance at being the programming language for conceptual computing next to natural language programming. We examine the possibility of using SQL as a back-end for natural language database programming. Distinctly from keyword based SQL querying, keyword dependence and SQL's table structure constraints are significantly less pronounced in our approach. We present a mobile device voice query interface, called Cyrus, to arbitrary relational databases. Cyrus supports a large type of query classes, sufficient for an entry level database class. Cyrus is also application independent, allows test database adaptation, and not limited to specific sets of keywords or natural language sentence structures. It's cooperative error reporting is more intuitive, and iOS based mobile platform is also more accessible compared to most contemporary mobile and voice enabled systems.
Multiple Subspace Alignment Improves Domain Adaptation

We present a novel unsupervised domain adaptation (DA) method for cross-domain visual recognition. Though subspace methods have found success in DA, their performance is often limited due to the assumption of approximating an entire dataset using a single low-dimensional subspace. Instead, we develop a method to effectively represent the source and target datasets via a collection of low-dimensional subspaces, and subsequently align them by exploiting the natural geometry of the space of subspaces, on the Grassmann manifold. We demonstrate the effectiveness of this approach, using empirical studies on two widely used benchmarks,with performance on par or better than the performance of the state of the art domain adaptation methods.
Layer thickness estimation of 3D printed model for digital multimedia forensics

Layer thickness is the major parameter of the 3D printing process that should be investigated for digital forensic purposes. However, despite its importance, there have been no studies yet on this parameter of layer-manufactured models. This Letter focuses on an effective estimation of layer thickness parameters for layer-manufactured models. Based on the analysis results of the scanned 3D model, the proposed method provides a new solution for estimating the layer thickness. Regarding potential applications including multimedia forensics, the proposed estimator would be useful in a variety of fields.
SpeedReader: Reader Mode Made Fast and Private

Most popular web browsers include “reader modes” that improve the user experience by removing un-useful page elements. Reader modes reformat the page to hide elements that are not related to the page's main content. Such page elements include site navigation, advertising related videos and images, and most JavaScript. The intended end result is that users can enjoy the content they are interested in, without distraction.
Subclass-based semi-random data partitioning for improving sample representativeness

In machine learning tasks, it is essential for a data set to be partitioned into a training set and a test set in a specific ratio. In this context, the training set is used for learning a model for making predictions on new instances, whereas the test set is used for evaluating the prediction accuracy of a model on new instances. In the context of human learning, a training set can be viewed as learning material that covers knowledge, whereas a test set can be viewed as an exam paper that provides questions for students to answer. In practice, data partitioning has typically been done by randomly selecting 70% instances for training and the rest for testing. In this paper, we argue that random data partitioning is likely to result in the sample representativeness issue, i.e., training and test instances show very dissimilar characteristics leading to the case similar to testing students on material that was not taught. To address the above issue, we propose a subclass-based semi-random data partitioning approach. The experimental results show that the proposed data partitioning approach leads to significant advances in learning performance due to the improvement of sample representativeness.
A linear classifier based approach for identifying security requirements in open source software development

There are several security requirements identification methods proposed by researchers in up-front requirements engineering (RE). However, in open source software (OSS) projects, developers use lightweight representation and refine requirements frequently by writing comments. They also tend to discuss security aspect in comments by providing code snippets, attachments, and external resource links. Since most security requirements identification methods in up-front RE are based on textual information retrieval techniques, these methods are not suitable for OSS projects or just-in-time RE. In this study, we proposed a linear based approach to identify security requirements. It first uses logistic regression models (RMs) to calculate feature values for requirements in OSS project. Then it uses the linear combination of all feature values to classify security and non-security requirements Our results show that compares to single RMs, our approach can achieve higher recall and precision.
Localizing salient body motion in multi-person scenes using convolutional neural networks

With modern computer vision techniques being successfully developed for a variety of tasks, extracting meaningful knowledge from complex scenes with multiple people still poses problems. Consequently, experiments with application-specific motion, such as gesture recognition scenarios, are often constrained to single person scenes in the literature. Therefore, in this paper we address the challenging task of detecting salient body motion in scenes with more than one person. We propose a neural architecture that only reacts to a specific kind of motion in the scene: A limited set of body gestures. The model is trained end-to-end, thereby avoiding hand-crafted features and the strong reliance on pre-processing as it is prevalent in similar studies. The presented model implements a saliency mechanism that reacts to body motion cues which have not been included in previous computational saliency systems. Our architecture consists of a 3D Convolutional Neural Network that receives a frame sequence as its input and localizes active gesture movement. To train our network with a large data variety, we introduce an approach to combine Kinect recordings of one person into artificial scenes with multiple people, yielding a large diversity of scene configurations in our dataset. We performed experiments using these sequences and show that the proposed model is able to localize the salient body motion of our gesture set. We found that 3D convolutions and a baseline model with 2D convolutions perform surprisingly similar on our task. Our experiments revealed the influence of gesture characteristics on how well they can be learned by our model. Given a distinct gesture set and computational restrictions, we conclude that using 2D convolutions might often perform equally well.
Quantification of the impact of priors in Bayesian statistics via Stein’s Method

We compare two distinct non-uniform choices of prior distributions by quantifying the Wasserstein distance between the respective resulting posterior distributions at any fixed sample size by means of Stein’s Method. We illustrate this measure of the prior impact on the normal, Binomial and Poisson models.
Secure image compression approach based on fusion of 3D chaotic maps and arithmetic coding

Advances in digital image processing and communications have created a great demand for real-time secure image transmission over the networks. However, the development of effective, fast and secure dependent image compression-encryption systems are still a research problem as the intrinsic features of images. A new approach is suggested in this study for partial image encryption-compression that adopts chaotic three-dimensional (3D) cat map to de-correlate relations among pixels in conjunction with an adaptive thresholding technique that is utilised as a lossy compression technique instead of using complex quantisation techniques and also as a substitution technique to increase the security of the cipher image. The proposed scheme is based on employing both of lossless compression with encryption on the most significant part of the image after contourlet transform. However, the least significant parts are lossy compressed by employing a simple thresholding rule and arithmetic coding to render the image totally unrecognisable. Due to the weakness of 3D cat map to chosen plain text attack, the suggested scheme incorporates a mechanism to generate random key depending on the contents of the image (context key). Several experiments were done on benchmark images to insure the validity of the proposed technique.
Mineral prospectivity analysis for BIF iron deposits: A case study in the Anshan-Benxi area, Liaoning province, North-East China

Banded iron formation (BIF) deposits are the most important source of iron in China, and they are dominantly distributed in the North China Craton (NCC). The Anshan-Benxi area is situated in the northeastern part of the NCC and is considered to be the most fertile and productive iron metallogenic province in China. The recent successive discoveries of large BIF deposits in this area, especially the world-class Dataigou BIF deposit, indicates that there is great potential for iron resources in this area. The BIF deposits are well defined by magnetic and gravity anomalies and thereby extensive petrophysical and geophysical studies are critical for BIF prospective mapping. A data-driven weights-of-Evidence (WofE) analysis was used for BIF prospectivity modeling. The results indicated that prospective areas are generally confined to the Precambrian greenstone belt basin and high-prospectivity zones (Class A) are mainly distributed in the middle and northeastern parts of the study area. Moderate-prospectivity zones (Class B) are mainly distributed in southwestern and northeastern parts of the study area. Finally, we employed selective human intervention to redefine priority targets to be more reliable. The results suggest the method is successful as 82 target areas were narrowed to Class A 20 and 11 Class B targets. The total of Class A and Class B targets was just over one-third of the total number of targets, offering a much smaller but effective selection of targets for consideration by mineral explorers.
Audio-visual learning for body-worn cameras

Wearable cameras capture user-centered data that can be used to analyze a scene, to recognize interactions and to classify physical activities. A wearable camera is equipped with multiple sensors such as microphone(s) and inertial measurement units, in addition to the imager. However, despite this richness in sensing modalities, the analysis of data from a wearable camera is particularly challenging due to unconventional mounting and capturing conditions, rapid changes in camera pose, self-occlusions, background noise and motion blur. In this chapter we present the main challenges for audio-visual classification using signals from wearable cameras, we show how multi-modality can help address those challenges and we discuss person reidentification as a specific application example.
The Exact VC Dimension of the WiSARD n-Tuple Classifier

The Wilkie, Stonham, and Aleksander recognition device (WiSARD) n -tuple classifier is a multiclass weightless neural network capable of learning a given pattern in a single step. Its architecture is determined by the number of classes it should discriminate. A target class is represented by a structure called a discriminator, which is composed of N RAM nodes, each of them addressed by an n -tuple. Previous studies were carried out in order to mitigate an important problem of the WiSARD n -tuple classifier: having its RAM nodes saturated when trained by a large data set. Finding the VC dimension of the WiSARD n -tuple classifier was one of those studies. Although no exact value was found, tight bounds were discovered. Later, the bleaching technique was proposed as a means to avoid saturation. Recent empirical results with the bleaching extension showed that the WiSARD n -tuple classifier can achieve high accuracies with low variance in a great range of tasks. Theoretical studies had not been conducted with that extension previously. This work presents the exact VC dimension of the basic two-class WiSARD n -tuple classifier, which is linearly proportional to the number of RAM nodes belonging to a discriminator, and exponentially to their addressing tuple length, precisely N(2n-1)+1 . The exact VC dimension of the bleaching extension to the WiSARD n -tuple classifier, whose value is the same as that of the basic model, is also produced. Such a result confirms that the bleaching technique is indeed an enhancement to the basic WiSARD n -tuple classifier as it does no harm to the generalization capability of the original paradigm.
Efficient Construction of a Complete Index for Pan-Genomics Read Alignment

While short read aligners, which predominantly use the FM-index, are able to easily index one or a few human genomes, they do not scale well to indexing databases containing thousands of genomes. To understand why, it helps to examine the main components of the FM-index in more detail, which is a rank data structure over the Burrows-Wheeler Transform ( $${\mathsf{BWT}}$$ ) of the string that will allow us to find the interval in the string's suffix array ( $${\mathsf{SA}}$$ ) containing pointers to starting positions of occurrences of a given pattern; second, a sample of the $${\mathsf{SA}}$$ that—when used with the rank data structure—allows us access to the $${\mathsf{SA}}$$ . The rank data structure can be kept small even for large genomic databases, by run-length compressing the $${\mathsf{BWT}}$$ , but until recently there was no means known to keep the $${\mathsf{SA}}$$ sample small without greatly slowing down access to the $${\mathsf{SA}}$$ . Now that Gagie et al. (SODA 2018) have defined an $${\mathsf{SA}}$$ sample that takes about the same space as the run-length compressed $${\mathsf{BWT}}$$ —we have the design for efficient FM-indexes of genomic databases but are faced with the problem of building them. In 2018 we showed how to build the $${\mathsf{BWT}}$$ of large genomic databases efficiently (WABI 2018) but the problem of building Gagie et al.'s $${\mathsf{SA}}$$ sample efficiently was left open. We compare our approach to state-of-the-art methods for constructing the $${\mathsf{SA}}$$ sample, and demonstrate that it is the fastest and most space-efficient method on highly repetitive genomic databases. Lastly, we apply our method for indexing partial and whole human genomes and show that it improves over Bowtie with respect to both memory and time. Availability: The implementations of our methods can be found at https://gitlab.com/manzai/Big-BWT (BWT and SA sample construction) and at https://github.com/alshai/r-index (indexing).
Construction and investigation of Cram endgame databases

In this paper we investigate the game of Cram, which is the impartial version of Domineering. We have built Cram endgame databases for all board sizes &lt; 30 squares. We developed a program that fills the databases with their Combinatorial Game Theory (CGT) values. Since Cram is an impartial game, all CGT values for Cram positions are so-called nimbers, indicated by ∗ n. The nimber value of a position not only directly determines the game-theoretic value (first- or second-player win), but also provides an optimal playing strategy. When analyzing the resulting databases we observed the following facts. Firstly we confirmed that the CGT values of all investigated empty boards are in agreement with results published in the literature. Since the value of an empty board depends completely on the values of many partially filled positions in the database, this is a strong indication that our process of filling the database with CGT values is correct. Secondly, although the series of values for 2 × n boards is completely regular, namely a value ∗ 0 for n being even and ∗ 1 for n being odd, this was not proven formally so far. We were able to provide such a proof. We also investigated the databases for their contents. So far we encountered nimber values up to ∗ 11 among single-fragment Cram positions. It appears that for a ∗ n value to occur a board size with ≈ 3 n squares is needed, some more for very tall boards ( 1 × n), some less for wider boards ( 4 × n). So far no single-fragment Cram positions were encountered with nimber values ⩾ ∗ 12, although we show a construction of larger multi-fragment positions with nimber values from ∗ 12 up to ∗ 15. In a preliminary experiment we incorporated the CGT endgame databases constructed into a simple alpha-beta solver for the game. Results revealed a large improvement in solving power.