Reviewerly/exHarmony huggingface dataset:
Ethno-political favouritism in maternal health care service delivery: Micro-level evidence from sub-Saharan Africa, 1981–2014

It is commonly held that political leaders favour people of the same ethnic origin. We test this argument of ethno-political favouritism by studying variations in the usage of maternal health care services across groups in sub-Saharan Africa (SSA). More specifically, we link geo-referenced individual-level data from the Demographic and Health Surveys on 601,311 births by 399,908 mothers in 31 countries during the period 1981–2014 with data on the settlement of ethnic groups and their political status. Our results indicate that women benefit from the shift that brings co-ethnics into power, increasing the probability of receiving maternal health care services. The effect strengthens with increased competitiveness around elections. We advance the current literature in four important ways. Firstly, we undertake the first analysis that utilizes shifts in ethno-political status for the same individual, effectively eliminating competing time-invariant explanations to that of shifts in ethno-political status. Secondly, since SSA governments often incorporate multiple groups, we test the effect of patronage on being co-ethnic with cabinet members in general, and not only the president. Thirdly, health services constitute the public good most desired by citizens of SSA. Our measure captures a vital health service that is highly desired across groups. An increase in usage likely reflects genuine trickle-down effects of having co-ethnics in power, a crucial ingredient in building popular support for ethnic patrons. Fourthly, we show that electoral competition is an important conditioner of ethno-political favouritism.
Automatic Recognition and Translation of Polysemous Verbs Using the Platform NooJ

In this work we study the phenomenon of verbal polysemy which poses a big challenge in the domain of automatic treatment of languages. This verbal polysemy constitutes not only a constraint to the automatic linguistic recognition, but also a hurdle in the French-Arabic automatic translation, especially in detecting the exact and precise equivalent in the target language. We will try to find solutions in the process of automatic translation to solve the problem of verbal polysemy using the NooJ platform.
3DUITK: An Opensource Toolkit for Thirty Years of Three-Dimensional Interaction Research

We present the 3D User Interaction Toolkit (3DUITK), an open-source toolkit consisting of 25 Virtual Reality (VR) interaction techniques proposed from thirty years of prior research. Although interaction has been a topic heavily explored in previous papers, much of the research is yet to be re-implemented into an easy to use toolkit. Existing open-source VR interaction solutions provide basic functionality with few available techniques to choose from, and there has yet to be an open-source immersive user interaction toolkit to date. In this paper, we provide an overview of 3DUITK which includes the features, related works, design decisions, example applications, limitations, and future directions. While the focus of this work is on the VR context, 3DUITK has been employed in spatial augmented reality and mobile augmented reality applications.
An Enhanced K-Means MSOINN Based Clustering Over Neo4j with an Application to Weather Analysis

Drastic climate change is one of the environmental concerns faced by all the living beings on earth today. So an efficient weather clustering should be done in order to better consolidate the climatic variations because the climatic predictions are environmental specific. Neo4j, one of the popular NoSQL databases, is used to represent the weather dataset in a graphical manner. The flexibility, scalability, and simplicity of the neo4j graph database help to represent the area-wise weather report in a simplified, graphical way and makes it convenient for the weather analysis. As Neo4j can handle complex and connected data, clustering the dataset by the k-means MSOINN based on Self-Organizing Incremental Neural Network (SOINN) algorithm, which uses the squared root of numerical, and categorical data enhances the accuracy and distribution contrast to the traditional k-means methodology.
Multi-Label Active Learning Driven by Uncertainty and Inconsistency

Multi-label active learning (MLAL) can help learn a highperformance multi-label classifier based on a smaller training set by selecting and labeling high-quality samples iteratively. This paper proposes a new MLAL algorithm by introducing a new concept called inconsistency cardinality. During each learning iteration, the current classifier can produce temporal decision labels for the candidate samples. Then, supporting decision regarding each label can be derived from the conditional features based on the lower approximations in fuzzy rough set theory. The inconsistency cardinality is defined as the disagreement degree between these two sets of decisions, which will be combined with uncertainty to give a more comprehensive evaluation on unlabeled samples. Correspondingly, a new MLAL strategy is proposed. Experimental comparisons show the feasibility and effectiveness of the proposed algorithm.
Towards A Unified Programming Model for Blockchain Smart Contract dApp Systems

Developing smart contract decentralised application based systems typically involves writing code for various platforms, from the smart contract code residing on the underlying distributed ledger technology implementation to back end oracles and front end websites or mobile apps. In addition to the different technologies used for the different parts, the programmer is also burdened with implementing communication channels between the various parts. In this paper we propose a unified programming model allowing for developers to build such systems through a single code artifact, using a macroprogramming approach.
Two-Round and Non-Interactive Concurrent Non-Malleable Commitments from Time-Lock Puzzles

Non-malleable commitments are a fundamental cryptographic tool for preventing (concurrent) man-in-the-middle attacks. Since their invention by Dolev, Dwork, and Naor in 1991, their round-complexity has been extensively studied, leading up to constant-round protocols based on one-way functions (OWFs), and three-round protocols based on sub-exponential OWFs, and standard polynomial-time hardness assumptions such as decisional Diffie--Hellman (DDH) and ZAPs (i.e., two-round witness-indistinguishable proofs). But constructions of two-round, or non-interactive, non-malleable commitments have so far remained elusive; the only known construction relied on a strong and non-falsifiable assumption with a non-malleability flavor. Additionally, a recent result by Pass shows the impossibility of basing two-round non-malleable commitments on falsifiable assumptions using a polynomial-time black-box security reduction. In this work, we show how to overcome this impossibility using super-polynomial-time hardness assumptions. Our main result demonstrates the existence of two-round concurrent non-malleable commitments based on the following four primitives (all with sub-exponential security): (1) non-interactive commitments, (2) ZAPs (i.e., 2-round witness indistinguishable proofs), (3) collision-resistant hash functions, and (4) a “weak” time-lock puzzle. Primitives (1), (2), and (3) can be based on, e.g., the discrete log and the RSA assumption. Time-lock puzzles---puzzles that can be solved by “brute-force” in time $2^t$, but cannot be solved significantly faster even using parallel computers---were proposed by Rivest, Shamir, and Wagner in 1996 and have been extensively studied since. We additionally obtain a non-interactive (i.e., one-message) version of our protocol satisfying concurrent non-malleability w.r.t. uniform attackers and show that our non-malleable commitments satisfy an even stronger notion of chosen commitment attack security.
A Dynamic Convergent Replica Selection Strategy Based on Cloud Storage

Replica selection strategy have been used in Cloud Storage to reduce data access latency, eliminate bottlenecks of hot spots and achieve load balance. This paper studies the replica selection strategy and replication strategy. By concerning real-time bandwidth, performance of the peer, and replica's history request, following the principle that high valued replica has high proportionality to be select, a proportional matching based self-convergent replica selection strategy (PMC) based on HDFS (Hadoop Distributed File System) cloud storage environment is presented. The strategy has global optimization, effectively reduces access delay, balances load, eliminates hot bottlenecks. The simulation results show that the optimal selection strategy offers better performance by shortening the response time of replica selection.
Control of the Geometric Phase in Two Open Qubit–Cavity Systems Linked by a Waveguide

We explore the geometric phase in a system of two non-interacting qubits embedded in two separated open cavities linked via an optical fiber and leaking photons to the external environment. The dynamical behavior of the generated geometric phase is investigated under the physical parameter effects of the coupling constants of both the qubit–cavity and the fiber–cavity interactions, the resonance/off-resonance qubit–field interactions, and the cavity dissipations. It is found that these the physical parameters lead to generating, disappearing and controlling the number and the shape (instantaneous/rectangular) of the geometric phase oscillations.
Scalable Lossless Coding of Dynamic Medical CT Data Using Motion Compensated Wavelet Lifting with Denoised Prediction and Update

Professional applications like telemedicine often require scalable lossless coding of sensitive data. 3-D subband coding has turned out to offer good compression results for dynamic CT data and additionally provides a scalable representation in terms of low- and highpass subbands. To improve the visual quality of the lowpass subband, motion compensation can be incorporated into the lifting structure, but leads to inferior compression results at the same time. Prior work has shown that a denoising filter in the update step can improve the compression ratio. In this paper, we present a new processing order of motion compensation and denoising in the update step and additionally introduce a second denoising filter in the prediction step. This allows for reducing the overall file size by up to 4.4%, while the visual quality of the lowpass subband is kept nearly constant.
Travel routes recommendations via online social networks

On line social networks (e.g., Facebook, Twitter) allow users to tag their posts with geographical coordinates collected through the GPS interface of smart phones. The time- and geo-coordinates associated with a sequence of tweets manifest the spatial-temporal movements of people in real life. The paper presents an approach to recommend travel routes to social media users exploiting historic mobility data, social features of users and geographic characteristics of locations. Travel routes recommendation is formulated as a ranking problem aiming at minimg the top interesting locations and travel sequences among them, and exploit such information to recommend the most suitable travel routes to a target user.
Detailed Fault Model for Physical Quantum Circuits

Quantum circuits have recently been developed thanks to the global companies like IBM, Google, Microsoft and Intel. The physical realization of quantum circuits motivates to explore new areas of research. Testing of quantum circuits is one such area which needs significant attention in order to detect faulty gate operations in the circuits. To this end, first we need to identify the different types of faults that can result due to some unwanted physical failures during the implementation of the gate operations. This paper investigates those possibilities of physical failures in realizing the quantum operations and introduces a new family of fault models for quantum circuits. Experimental results include the actual number of newly proposed faults that can occur at the physical level of any quantum circuit.
Perceptual Font Manifold from Generative Model

Though in recent times, various fonts are available online for public usage, it is difficult and challenging to generate, explore, and edit the fonts to meet the preferences of all users. To address these issues, we propose in this paper, a font manifold interface to visualize the perceptual adjustment in the latent space of a generative model of fonts. In this paper, we adopt the variational autoencoder network for font generation. Then, we conducted a perceptual study on the generated fonts from the multi-dimensional latent space of the generative model. After we obtained the distribution data of specific preferences, we utilized a manifold learning approach to visualize the font distribution. As a case study of our proposed method, we developed a user interface for generated font exploration in the designated user preference using a heat map representation.
Local optimal oriented pattern for person independent facial expression recognition

Facial expressions play a key role in identifying the internal emotion state of human beings. Human beings have the tendency to recognize human emotions without any delay. But, a fully automated expression recognition by a computer is a problem that still persists. Towards solving this problem, a Local Optimal Oriented Pattern (LOOP) has been proposed in this paper. This descriptor is proposed to overcome some of the drawbacks in existing feature descriptors, Local Binary Pattern (LBP) and Local Directional Pattern (LDP) by combining the strengths of each of these two descriptors. The LOOP descriptor has been applied on JAFFE, MUG, WSEFEP and ADFES databases in person independent setup. The experiments are conducted for six, seven expressions in all the four databases. The experimental results proved that the proposed LOOP descriptor achieved a better recognition accuracy than existing methods by taking less computation time.
Bidirectionality in flow-sensitive demand-driven analysis

Demand-driven methods for program analysis have primarily been viewed as efficient algorithms for computing the same information as the corresponding exhaustive methods, but for a given set of demands. We explore demand-driven flow-sensitive alias analysis (which we call ADFSA ) and propose its improved version called PDFSA that computes both aliases and pointers for the demands raised by changing the notion of relevance for indirect assignment statements. We formally show that while ADFSA is as precise as the corresponding exhaustive flow-sensitive alias analysis (EFSA ), PDFSA can be more precise than both ADFSA and EFSA. This surprising result is based on the following insight: A demand-driven method computes less information than the corresponding exhaustive method. PDFSA exploits this to reduce the uncertainty caused by aliasing which in turn, reduces the conflation of memory locations thereby increasing precision. We formalize PDFSA using an inherent property of a demand-driven flow-sensitive alias analysis: demands are propagated against the control flow and aliases are propagated along the control flow. Traditionally, this has been seen as a collection of two separate analyses whose interaction is controlled by an algorithm that drives the two analyses. We formalize this algorithmic view as a bidirectional data flow analysis to define PDFSA declaratively. Further, we define Meet Over Paths (MoP) solution for bidirectional flows for reasoning about the soundness of PDFSA. Our definition generalizes the classical definition of MoP which is restricted to unidirectional flows. We have implemented PDFSA, ADFSA, and EFSA for static resolution of virtual function calls in C++ for constructing more precise call graphs. Our measurements show that the call graphs computed using PDFSA are indeed more precise than those that are computed using ADFSA or EFSA.
Workshop Design for Hands-on Exploration Using Soft Robotics and Onomatopoeia

Hands-on exploration with robotics has been developed as a tool for creativity, but there are limitations with regard to accessibility. As a new method of creative and pervasive exploration for children and students, we propose a workshop using inflatable soft robots with short video recipes and onomatopoeia as a prompt. This method does not require prior knowledge or high fabrication costs and includes open-ended designs. Based on the soft robots obtained from five practical workshops with 131 participants, about 88% showed new structures and 61% exhibited advanced trials, which require deep consideration of the structures. The participants discovered new mechanisms themselves through exploration and used the given onomatopoeia to imagine their own storytelling for exploration. The results indicate the potential of our method to encourage both structure discovery and diverse expressions. It can be used as a tool for creative learning and a guideline for designing other approaches.
Short-Term Prediction Method of HF Frequency Based on Deep Learning Network

To find an accurate and effective frequency prediction method is the key to improve HF communication. With analysis on ionosphere propagation features, and on the basis of ionospheric data in Sanya, Hainan, the Deep Learning method is applied to the ionospheric parameter prediction, then the method is compared with the Elman neural network prediction method. The prediction of the Deep Learning method is more close to the real value with less training cycles, and more accurate scientific basis is provided for the HF frequency prediction.
Convergence Analysis of Unscented Transform for Underwater Passive Target Tracking in Noisy Environment

Optimal passive target tracking in noisy ocean environments is an important research problem. This issue is frequently dealt with nonlinear Bayesian filtering techniques, in which passive measurements are nonlinear while the dynamic system model is considered linear. The key objective of underwater passive object tracking is to accurately find the motion parameters of the object over uncertain measurements extracted from array elements. In this paper, the rule of unscented transform is used to examine the convergence of unscented Kalman filter (UKF) and unscented Rauch Tung Striebel (URTS) type Kalman post processing method. This study delicately finds the real-time state of a long distance dynamic target in a noisy complex underwater scenario, in which variance of background noise is high. Underwater Bearings-Only Tracking (BOT) technology is used for tracking purposes by deploying eight array elements which are localized on a horizontal Uniform Linear Array (ULA). Simulations are done for finding least Root Mean Square Error (RMSE) among real and approximated position of the target. Independent numerical results demonstrate that URTS smoother offers a higher convergence rate from UKF in a noisy ocean environment.
Research on 3D Point Cloud Data Acquisition Technology Based on Linear Structured Light

This paper presents a new method of linear structured light scanning combining monocular and binocular. The red moving light stripe projected by miniature projector is used to replace the traditional laser, which not only reduces hardware costs but is also easier to use. In order to obtain the light plane equation with high precision, the cylindrical paper tube is scanned in the binocular system, then in the monocular system, the three-dimensional point coordinates of the object are calculated according to the principle of transmission projection geometry. Experimental results show that this method can effectively balance the requirements of accuracy and speed, and has certain practical value.
An Improved Meanshift Tracking Algorithm Using Adaptive Quantization Step in Color Space

The traditional meanshift based tracking algorithm uses a constant quantization step to carry out feature generation in the color space but it cannot dynamically alter the quantization step with the changes of the target geometry to improve computational efficiency in large depth-of-field scenarios. Based on the traditional meanshift algorithm, we proposed a tracking algorithm using adaptive quantization step which automatically adjusts the quantization step of the color histogram and uses the dynamic time warping algorithm to match the features with different dimensions when the target geometry changes, thereby, effectively reducing the average frame processing time. The comparative experiments under multiple scenarios demonstrated that the proposed algorithm can adaptively adjust the quantization step of color histogram in large depth of field scenarios and improve the operating efficiency of the algorithm.
Interactive Holograms for Better Construction Information Communication

Over the last few decades, the investments in more complex construction projects have increased the need for better communication means. Effective means of communications should be employed on construction projects to help aggregate dispersed information between project stakeholders. Advancements in information technology have resulted in new construction visualization techniques aiming at enhancing communication between project stakeholders. Some of these technologies include virtual reality, augmented reality, and holography. The aim of this paper is to further investigate the use of holography in the architecture, engineering, construction, and operations (AECO) industry. The holographic technology used in this research was previously developed by the authors through a game engine and an add-in for Revit. The add-in allows an easy transfer of information from a building information model in Revit to be used in the game engine. In addition, the developed hologram allows the user to interact with the hologram using voice commands and/or hand gestures. Both the game and the add-in were the subject of an experimental study conducted to evaluate their ease of use and effectiveness. The results proved that the developed add-in is easier to use and more effective than traditional methods used to export building models from modeling platforms into a gaming environment. Moreover, the interactive hologram was able to enhance the human-human interaction between multiple users. Future research will focus on further developing the software and on investigating the use of the holographic experience on job sites.
Fall Detection Algorithm based on Gradient Boosting Decision Tree

This paper proposes a fall detection algorithm for wearable devices or mobile terminals. Since there is currently no complete data set for the fall of the elderly, most of the research uses young experimenters to collect data, so the data set and the difference in the terminal leads to the low accuracy of the fall detection algorithm. In order to improve the accuracy of the algorithm, this paper uses a two-level detection that combines the offline threshold method with the online GBDT (Gradient Boosting Decision Tree) to detect the fall action. According to the analysis of the final result, the sensitivity of the GBDT algorithm is 96%, the false positive rate is 2.8%, and the accuracy rate is 96%. The improved decision tree algorithm can detect the fall behavior more accurately than the decision tree.
Applications of tf-idf concept to improve monolingual and cross-language information retrieval based on word embeddings

This work applied word embeddings for English monolingual information retrieval and Dutch-English cross-language information retrieval. Besides word embeddings, this work also applied tf-idf concept to increase result of relevant documents. We present experiments using four techniques adapted from tf-idf concept. The result showed additional techniques could increase MAP score up to 26.6% in monolingual information retrieval and up to 26.2% in cross-language information retrieval compare with monolingual information retrieval and cross-language information retrieval using average vectors technique only.