Reviewerly/exHarmony huggingface dataset:
Sexual dimorphism in the lip print pattern and size among Ugandan, Kenyan and Somali population

Abstract Background Lip prints are essential identification tools in forensics. Lip prints are individually unique and inheritable which makes them possible for personal identification. Aim We studied sexual differences in print patterns and sizes among Ugandans, Kenyans and Somalis. Method In this descriptive study of 180 adults (60 from each country) clear lip print impressions were made on clean pieces of white plain papers. Lip prints were divided into quadrants and we used Suzuki and Tsuchihashi (1974) classification to study the patterns. Lip length and width were measured in centimeters. Results Lip print Type I and I’ were dominant in a range of 60–100% in both males and females of all countries. Kenyans had more of Type II (males 26%; females 40%) while Somalis exhibited more of Type IV males 56%; females 46%. However, Ugandans had the least number of Type IV pattern 6.6% males and 13% females. There was no Type V in all the participants. Statistical analysis using a 2-way ANOVA showed statistical significance in the lip print patterns among the different population groups. In the male subjects, Ugandans had wider lips (5.48cm ± 0.50) while Kenyans had longer lips (5.14cm ± 0.85). In the females group; Ugandans had wider lips (4.99cm ± 0.59) while Kenyans had longer lips (4.36cm ± 0.71). Statistically significant difference was observed between the means of lip sizes. Conclusion This study shows that the lip print pattern and lip size varied significantly among the different population groups. This variation can help to narrow the search gap in criminal investigations and forensic purposes.
A Data Hiding Approach Based on Reference-Affected Matrix

Data security has got many remarkable achievements. However, the issues of the lower distortion and the higher embedding capacity in the embedded secret data in media have not much been considered by scholars. This paper proposes a new data hiding approach to the embedded secrets based on the guidance of the x-cross-shaped reference-affected matrix to solve these issues. Adjacent pixels would be found out large area with similar values which can utilize for manipulating data embedding and extracting on a difference–coordinate plan instead of the traditional pixel–coordinate plan. Three parts: petal matrix, calyx matrix, and stamen matrix are combined for data embedding by using the x-cross-shaped reference matrix. The experimental results compared with the previous methods in the literature shows that the proposed approach brings outstanding payload with the cover visual quality.
Interoperability Standards for Adaptive Instructional Systems: Vertical and Horizontal Integrations

Adaptive instructional systems (AISs) – tools and methods that tailor each student’s instructional experiences to their needs within a set of domain learning objectives – are becoming increasingly common. In an ideal configuration, AISs work in concert using open interoperability standards to provide a seamless experience for students and instructors, while leveraging high-frequency contextual data to inform the learning flow. With the large amount of learning interactions that can take place in AISs, however, existing industry standards are unable to support the interoperability and extensibility of components within an AIS and among different AISs. In this paper, we propose extensions on top of current industry standards to enable interoperability among components within an AIS. We also discuss the need of interoperability standards across different AISs on the learning ontology and data models, and the opportunity to leverage recent advances in federated machine learning to enable horizontal integration across separate AISs.
Preprocessing Ambiguous Imprecise Points

Let R = {R_1, R_2, ..., R_n} be a set of regions and let X = {x_1, x_2, ..., x_n} be an (unknown) point set with x_i in R_i. Region R_i represents the uncertainty region of x_i. We consider the following question: how fast can we establish order if we are allowed to preprocess the regions in R? The preprocessing model of uncertainty uses two consecutive phases: a preprocessing phase which has access only to R followed by a reconstruction phase during which a desired structure on X is computed. Recent results in this model parametrize the reconstruction time by the ply of R, which is the maximum overlap between the regions in R. We introduce the ambiguity A(R) as a more fine-grained measure of the degree of overlap in R. We show how to preprocess a set of d-dimensional disks in O(n log n) time such that we can sort X (if d=1) and reconstruct a quadtree on X (if d >= 1 but constant) in O(A(R)) time. If A(R) is sub-linear, then reporting the result dominates the running time of the reconstruction phase. However, we can still return a suitable data structure representing the result in O(A(R)) time. In one dimension, {R} is a set of intervals and the ambiguity is linked to interval entropy, which in turn relates to the well-studied problem of sorting under partial information. The number of comparisons necessary to find the linear order underlying a poset P is lower-bounded by the graph entropy of P. We show that if P is an interval order, then the ambiguity provides a constant-factor approximation of the graph entropy. This gives a lower bound of Omega(A(R)) in all dimensions for the reconstruction phase (sorting or any proximity structure), independent of any preprocessing; hence our result is tight. Finally, our results imply that one can approximate the entropy of interval graphs in O(n log n) time, improving the O(n^{2.5}) bound by Cardinal et al.
Historical Theme Game Using Finite State Machine for Actor Behaviour

North Sumatra has a lot of stories about the history of the struggle. For instancing on the Dutch military aggression in Indonesia after independence. Many insurgencies do each region in northern Sumatra to maintain its independence. This application was created to show the game in the form of 3D for entertainment and education. This game genre of First-Person Shooter in which players seek to kill the enemy and shot her way. This game application design based on the folklore of North Sumatra on fighting Stabat from Dutch hands. Game Engine used is Unity 5. Unity can be used to produce the game in 3D to realize the real world in virtual form. The design of this game is the result of the analysis, design, and implementation of methods Finite State Machine is used for NPC behaviour. Results from the design of this game is a game in 3D based on the story of history and to NPC using Finite State Machine.
Fusion of Global and Local Gaussian-Hermite Moments for Face Recognition

In automatically recognizing human faces, it is an important problem how to extract the effective features from the corrupted face. This paper propose a new face recognition algorithm based on fusion of global and local Gaussian-Hermite moments (GHMs). Firstly, in order to solve the interference of noise on features, we use the GHMs of face image as facial feature. Second, we construct the face image spatial pyramid to extract the global and local features of the face, and then we compute scatter-ratio to seclect highly discriminative feature. Lastly we use sparse representation classifier to improve the robust of algorithm. Experiments on ORL, FERET and Yale A face databases reveal that the accuracy of proposed algorithm is better than traditional algorithm, especially when the face images are corrupted by salt&pepper noise.
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data

Empirical risk minimization is the main tool for prediction problems, but its extension to relational data remains unsolved. We solve this problem using recent ideas from graph sampling theory to (i) define an empirical risk for relational data and (ii) obtain stochastic gradients for this empirical risk that are automatically unbiased. This is achieved by considering the method by which data is sampled from a graph as an explicit component of model design. By integrating fast implementations of graph sampling schemes with standard automatic differentiation tools, we provide an efficient turnkey solver for the risk minimization problem. We establish basic theoretical properties of the procedure. Finally, we demonstrate relational ERM with application to two non-standard problems: one-stage training for semi-supervised node classification, and learning embedding vectors for vertex attributes. Experiments confirm that the turnkey inference procedure is effective in practice, and that the sampling scheme used for model specification has a strong effect on model performance.
Learning one-hidden-layer neural networks under general input distributions

Significant advances have been made recently on training neural networks, where the main challenge is in solving an optimization problem with abundant critical points. However, existing approaches to address this issue crucially rely on a restrictive assumption: the training data is drawn from a Gaussian distribution. In this paper, we provide a novel unified framework to design loss functions with desirable landscape properties for a wide range of general input distributions. On these loss functions, remarkably, stochastic gradient descent theoretically recovers the true parameters with \emph{global} initializations and empirically outperforms the existing approaches. Our loss function design bridges the notion of score functions with the topic of neural network optimization. Central to our approach is the task of estimating the score function from samples, which is of basic and independent interest to theoretical statistics. Traditional estimation methods (example: kernel based) fail right at the outset; we bring statistical methods of local likelihood to design a novel estimator of score functions, that provably adapts to the local geometry of the unknown density.
Wigner function for squeezed negative binomial state and evolution of density operator for amplitude decay*

Using the thermal-entangled state representation and the operator-ordering method, we investigate Wigner function (WF) for the squeezed negative binomial state (SNBS) and the analytical evolution law of density operator in the amplitude decay channel. The results show that the analytical WF is related to the square of the module of single-variable Hermite polynomials, which leads to a new two-variable special function and its generating function, and the parameters s and γ play opposite roles in the WF distributions. Besides, after undergoing this channel, the initial pure SNBS evolves into a new mixed state related to two operator Hermite polynomials within normal ordering, and fully loses its nonclassicality and decays to vacuum at long decay time.
Urine Sediment Detection Based on Deep Learning

Particle urinary sediment analysis in microscopic images can help doctors assess patients with kidney and urinary tract disease. Manual urine sediment inspection is labor intensive, subjective and time consuming, and traditional automated algorithms often extract handcrafted identification features. In this paper, instead of using manual extraction of features, we use CNN to learn features in an end-to-end manner to identify urine particles. In this paper, urine particle recognition is used as object detection processing, and other advanced target detection algorithms, Faster R-cnn, SSD, and deep learning frameworks such as RestNet and DenseNet, are used to study a new target detection method for urine particle recognition.
An Adaptive Framework for Conversational Question Answering

In Conversational Question Answering (CoQA), humans propose a series of questions to satisfy their information needs. Based on our preliminary analysis, there are two major types of questions, namely verification questions and knowledgeseeking questions. The first one is to verify some existing facts, while the latter is to obtain new knowledge about some specific object. These two types of questions differ significantly in their answering ways. However, existing methods usually treat them uniformly, which may easily be biased by the dominant type of questions and obtain inferior overall performance. In this work, we propose an adaptive framework to handle these two types of questions in different ways based on their own characteristics. We conduct experiments on the recently released CoQA benchmark dataset, and the results demonstrate that our method outperforms the state-of-the-art baseline methods.
Learning analysis of mobile JavaScript frameworks

JavaScript is currently a popular language mainly in the creation of client-side code for both web development and, more and more, for the mobile applications development. In this paper, we analyze learning for programmers in frameworks for the development of mobile JavaScript applications, namely React Native, NativeScript and Ionic. This analysis was carried out based on a taxonomy adapted from Bloom, created in this work for the evaluation of programmers, with criteria initially selected for evaluation and with the application of questions based on the taxonomy in order to obtain the most relevant analysis criteria. The results show that NativeScript was the most difficult to learn framework with the most negative feedback, unlike Ionic and React Native, which presented identical global results, but with a slight upward trend in global mean results for React Native in relation to Ionic.
Joint Learning of Brain Lesion and Anatomy Segmentation from Heterogeneous Datasets

Brain lesion and anatomy segmentation in magnetic resonance images are fundamental tasks in neuroimaging research and clinical practice. Given enough training data, convolutional neuronal networks (CNN) proved to outperform all existent techniques in both tasks independently. However, to date, little work has been done regarding simultaneous learning of brain lesion and anatomy segmentation from disjoint datasets. In this work we focus on training a single CNN model to predict brain tissue and lesion segmentations using heterogeneous datasets labeled independently, according to only one of these tasks (a common scenario when using publicly available datasets). We show that label contradiction issues can arise in this case, and propose a novel adaptive cross entropy (ACE) loss function that makes such training possible. We provide quantitative evaluation in two different scenarios, benchmarking the proposed method in comparison with a multi-network approach. Our experiments suggest that ACE loss enables training of single models when standard cross entropy and Dice loss functions tend to fail. Moreover, we show that it is possible to achieve competitive results when comparing with multiple networks trained for independent tasks.
Runtime type safety for Erlang/otp behaviours

Callback-oriented Erlang/OTP behaviours such as the gen_server library are susceptible to malformed requests and ill-typed messages, causing server processes to crash unless a defensive programming style is used. We contribute an alternative approach in the form of a fully automatic hybrid analysis of callback modules using a notion of type safety based upon a sub-typing relation for Erlang. A combination of compile-time type inference, automatic code injection, and modifications to the request dispatch code of gen_server are used to demonstrate how generic server processes can be protected from client-side type errors.
From XML Retrieval to Semantic Search and Beyond

INEX ran as an independent evaluation forum for 10 years before it teamed up with CLEF in 2012. Even before 2012 there was considerable collaboration between INEX and CLEF, and these collaborations increased in intensity when CLEF moved beyond its traditional cross-lingual focus in 2009/2010 shifting to include all experimental IR. This led to the merger of CLEF and INEX, and effectively to the inclusion of INEX as a large track or lab into CLEF in 2012. This chapter details the efforts of the INEX lab in CLEF (2012–2014), as well as the ongoing activities as separate labs, under the labels Social Book Search (2015–2016), and Microblog Contextualization (2016–2018).
Security for Distributed Deep Neural Networks: Towards Data Confidentiality &amp; Intellectual Property Protection

Current developments in Enterprise Systems observe a paradigm shift, moving the needle from the backend to the edge sectors of those; by distributing data, decentralizing applications and integrating novel components seamlessly to the central systems. Distributively deployed AI capabilities will thrust this transition. Several non-functional requirements arise along with these developments, security being at the center of the discussions. Bearing those requirements in mind, hereby we propose an approach to holistically protect distributed Deep Neural Network (DNN) based/enhanced software assets, i.e. confidentiality of their input & output data streams as well as safeguarding their Intellectual Property. Making use of Fully Homomorphic Encryption (FHE), our approach enables the protection of Distributed Neural Networks, while processing encrypted data. On that respect we evaluate the feasibility of this solution on a Convolutional Neuronal Network (CNN) for image classification deployed on distributed infrastructures.
Real-time single-view face detection and face recognition based on aggregate channel feature

A single-view face detector and a novel face recognition method based on the aggregate channel feature (ACF) that work at real-time speeds, suitable in a computing resource-constrained setting are presented in this work. The four stage tree-based face detector is trained on a subset of the AFLW dataset. The face detection performance is analysed using the AFW dataset. The face recogniser uses ACF features along with classification algorithms, either SVM or MLP. The face recogniser is trained and tested on the GATech Face dataset. Our face detector displays comparable performance against the state of the art while working at 29.8 fps. The face recogniser achieves a level of performance that is competitive with other state of the art works. The effect of PCA-based dimension reduction of ACF features on face recognition performance is also studied in this work.
A Novel Objective Quality Assessment Method for Transcoded Videos From H.264/AVC to H.265/HEVC Utilizing Probability Theory

The latest video coding standard H.265/HEVC is developed to succeed the previous coding standard H.264/AVC. However, a large amount of legacy content was coded with H.246/AVC. Therefore, transcoding from H.264/AVC to H.265/HEVC format is required. During the process of transcoding, we can easily calculate the distortion of the transcoded video with respect to the H.264/AVC-decoded video. However, since the original video is usually unavailable, the distortion between the original video and the transcoded video is unknown, which makes it difficult to control the coding quality of the transcoded video compared to the original video. In this paper, we propose a novel and accurate quality estimation method for transcoded videos utilizing probability theory. Experimental results demonstrate that the predicted quality of transcoded videos approximate the true value, with an average error of 0.28 dB, 0.41 dB, and 0.46 dB for Y, Cb, and Cr components, respectively.
MetricsVis: A Visual Analytics System for Evaluating Employee Performance in Public Safety Agencies

Evaluating employee performance in organizations with varying workloads and tasks is challenging. Specifically, it is important to understand how quantitative measurements of employee achievements relate to supervisor expectations, what the main drivers of good performance are, and how to combine these complex and flexible performance evaluation metrics into an accurate portrayal of organizational performance in order to identify shortcomings and improve overall productivity. To facilitate this process, we summarize common organizational performance analyses into four visual exploration task categories. Additionally, we develop MetricsVis, a visual analytics system composed of multiple coordinated views to support the dynamic evaluation and comparison of individual, team, and organizational performance in public safety organizations. MetricsVis provides four primary visual components to expedite performance evaluation: (1) a priority adjustment view to support direct manipulation on evaluation metrics; (2) a reorderable performance matrix to demonstrate the details of individual employees; (3) a group performance view that highlights aggregate performance and individual contributions for each group; and (4) a projection view illustrating employees with similar specialties to facilitate shift assignments and training. We demonstrate the usability of our framework with two case studies from medium-sized law enforcement agencies and highlight its broader applicability to other domains.
Reflection Separation using a Pair of Unpolarized and Polarized Images

When we take photos through glass windows or doors, the transmitted background scene is often blended with undesirable reflection. Separating two layers apart to enhance the image quality is of vital importance for both human and machine perception. In this paper, we propose to exploit physical constraints from a pair of unpolarized and polarized images to separate reflection and transmission layers. Due to the simplified capturing setup, the system becomes more underdetermined compared with existing polarization based solutions that take three or more images as input. We propose to solve semireflector orientation estimation first to make the physical image formation well-posed and then learn to reliably separate two layers using a refinement network with gradient loss. Quantitative and qualitative experimental results show our approach performs favorably over existing polarization and single image based solutions.
Online influence, offline violence: Linguistic responses to the 'Unite the Right' rally

The media frequently describes the 2017 Charlottesville 'Unite the Right' rally as a turning point for the alt-right and white supremacist movements. Social movement theory suggests that the media attention and public discourse concerning the rally may have influenced the alt-right, but this has yet to be empirically tested. The current study investigates whether there are differences in language use between 7,142 alt-right and progressive YouTube channels, in addition to measuring possible changes as a result of the rally. To do so, we create structural topic models and measure bigram proportions in video transcripts, spanning eight weeks before to eight weeks after the rally. We observe differences in topics between the two groups, with the 'alternative influencers' for example discussing topics related to race and free speech to an increasing and larger extent than progressive channels. We also observe structural breakpoints in the use of bigrams at the time of the rally, suggesting there are changes in language use within the two groups as a result of the rally. While most changes relate to mentions of the rally itself, the alternative group also shows an increase in promotion of their YouTube channels. Results are discussed in light of social movement theory, followed by a discussion of potential implications for understanding the alt-right and their language use on YouTube.