Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Prototyping of a Spherical Parallel Machine Based on 3-CPU Kinematics

the other. In one case, joints’ axes are set in space so that the mobile platform can freely translate (without rotating) inside its 3D workspace: this is easily obtained by arranging the universal joint of each limb so that the axis of the outer revolute joint is parallel to the base cylindrical joint; such three directions are mutually orthogonal to maximise the workspace and grant optimal manipulability. With a different setting of the joints, three degrees of freedom of pure rotation are obtained at the terminal of the spherical wrist: in this case the axes of the cylindrical joints and those of the outer revolute pairs in the universal joints all intersect at a common point, which is the centre of the spherical motion. This solution, at the cost of a more sophisticated controller, would lead to the design of simpler machines that could be used also stand-alone for 3-dof tasks and would increase the modularity and reconfigurability of the robotised industrial process. The two robots have been developed till the prototypal stage by means of a virtual prototyping environment and a sketch of the whole system is shown in Fig. 1: while the translating machine has been presented already elsewhere (Callegari & Palpacelli, 2008), the present article describes the design process of the orienting device and the outcoming prototype.
Willingness to Pay for Farm Insurance by Smallholder Cocoa Farmers in Ghana

This study analyzes the willingness to pay for cocoa price insurance in the Ghanaian cocoa industry using contingent valuation (CV) method to collect primary data from 201 cocoa farmers in Bibiani-Anhiawso-Bekwai district, Ghana. The study employed descriptive statistics to analyze the demographic characteristics of the sampled farmers in the study area. 57.71 percent of the sampled cocoa farmers were found to respond positively to cocoa price insurance. Independent double-hurdle model was used to determine factors influencing farmer’s adoption of cocoa price insurance and the premium farmers are willing to pay. Empirical results from the study revealed that farmers interest in cocoa price insurance was affected by range of explanatory variables such as marital status, number of years in cocoa farming, educational attainment, household size, farm size, ownership of farm land for farming, age of cocoa farm, age squared of cocoa farm, farmers being aware of the insurance scheme and income from cocoa farm. On the other hand, the premium farmers were willing to pay was significantly influence by marital status, educational attainment, ownership of farm land for farming, farmer’s awareness of insurance scheme and income from cocoa farm. Cocoa farmers are on average willing to pay between 9.3% and 10.5% of the option value they intend to receive as premium depending on the value. The study recommends that particular attention be given to education of farmers on the significance of insuring their cocoa farms.
An Automated Testing Framework for Conversational Agents

Conversational agents are systems with a conversational interface that afford interaction in spoken language. These systems are becoming prevalent and are preferred in various contexts and for many users. Despite their increasing success, the automated testing infrastructure to support the effective and efficient development of such systems compared to traditional software systems is still limited. Automated testing framework for conversational systems can improve the quality of these systems by assisting developers to write, execute, and maintain test cases. In this paper, we introduce our work-in-progress automated testing framework, and its realization in the Python programming language. We discuss some research problems in the development of such an automated testing framework for conversational agents. In particular, we point out the problems of the specification of the expected behavior, known as test oracles, and semantic comparison of utterances.
Prophet Inequality for Bipartite Matching: Merits of Being Simple and Non Adaptive

We consider Bayesian online selection problem of a matching in bipartite graphs, i.e., online weighted matching problem with edge arrivals where online algorithm knows distributions of weights, that corresponds to the intersection of two matroids in Kleinberg and Weinberg [35] model. We consider a simple class of non adaptive vertex-additive policies that assign static prices to all vertices in the graph and accept each edge only if its weight exceeds the sum of the prices of the edge's endpoints. We show existence of a vertex-additive policy with the expected payoff of at least one third of the prophet's payoff and present gradient decent type algorithm that quickly converges to the desired vector of vertex prices. This improves the adaptive online policy of Kleinberg and Weinberg for the intersection of two matroids in two ways: our policy is non adaptive and has better approximation guarantee of 3 instead of previous guarantees 5.82 of Kleinberg and Weinberg and 2•e=5.43 of Feldman et al. [23] against the prophet. We give a complementary lower bound of 2.25 for any online algorithm in the bipartite matching setting.
Multiple Document Representations from News Alerts for Automated Bio-surveillance Event Detection

Due to globalization, geographic boundaries no longer serve as effective shields for the spread of infectious diseases. In order to aid bio-surveillance analysts in disease tracking, recent research has been devoted to developing information retrieval and analysis methods utilizing the vast corpora of publicly available documents on the internet. In this work, we present methods for the automated retrieval and classification of documents related to active public health events. We demonstrate classification performance on an auto-generated corpus, using recurrent neural network, TF-IDF, and Naive Bayes log count ratio document representations. By jointly modeling the title and description of a document, we achieve 97% recall and 93.3% accuracy with our best performing bio-surveillance event classification model: logistic regression on the combined output from a pair of bidirectional recurrent neural networks.
A Fleet of Miniature Cars for Experiments in Cooperative Driving

We introduce a unique experimental testbed that consists of a fleet of 16 miniature Ackermann-steering vehicles. We are motivated by a lack of available low-cost platforms to support research and education in multi-car navigation and trajectory planning. This article elaborates the design of our miniature robotic car, the Cambridge Minicar, as well as the fleet’s control architecture. Our experimental testbed allows us to implement state-of-the-art driver models as well as autonomous control strategies, and test their validity in a real, physical multi-lane setup. Through experiments on our miniature highway, we are able to tangibly demonstrate the benefits of cooperative driving on multi-lane road topographies. Our setup paves the way for indoor large-fleet experimental research.
Author Profiling for Hate Speech Detection

The rapid growth of social media in recent years has fed into some highly undesirable phenomena such as proliferation of abusive and offensive language on the Internet. Previous research suggests that such hateful content tends to come from users who share a set of common stereotypes and form communities around them. The current state-of-the-art approaches to hate speech detection are oblivious to user and community information and rely entirely on textual (i.e., lexical and semantic) cues. In this paper, we propose a novel approach to this problem that incorporates community-based profiling features of Twitter users. Experimenting with a dataset of 16k tweets, we show that our methods significantly outperform the current state of the art in hate speech detection. Further, we conduct a qualitative analysis of model characteristics. We release our code, pre-trained models and all the resources used in the public domain.
Towards Non-saturating Recurrent Units for Modelling Long-term Dependencies

Modelling long-term dependencies is a challenge for recurrent neural networks. This is primarily due to the fact that gradients vanish during training, as the sequence length increases. Gradients can be attenuated by transition operators and are attenuated or dropped by activation functions. Canonical architectures like LSTM alleviate this issue by skipping information through a memory mechanism. We propose a new recurrent architecture (Non-saturating Recurrent Unit; NRU) that relies on a memory mechanism but forgoes both saturating activation functions and saturating gates, in order to further alleviate vanishing gradients. In a series of synthetic and real world tasks, we demonstrate that the proposed model is the only model that performs among the top 2 models across all tasks with and without long-term dependencies, when compared against a range of other architectures.
Analysis of the Main Factors Affecting M-Commerce Adoption in Iraq

The telecommunications sector in Iraq is one of the most dynamic and active industries. This sector has prime importance only next to oil & gas, as revealed by recent reports. This industry has grown in leaps and bounds with the rising demand for mobile services. Mobile services were inaccessible prior to 2003. As of 2016, there are 29 million mobile telephone subscribers, with only 2.3 million fixed-line users. With the easy availability of telephone services, the telecommunication sector has reported a rapid boom. Subsequent to the fall of Saddam Hussein's regime on April 9, 2003, mobile commerce has reached new heights. This phenomenon is mainly attributed to the increasing internet and smartphone usage. Telecommunication is now an imperative element in the social and economic growth of the country. This sector has also expanded access to various prospects and has altered the manner of consumer interaction, comparing prices, researching goods, and making purchases. Several studies have reviewed the penetration of mobile commerce to varied sectors. These studies have also delved on the challenges, prospects, concerns, and impediments in this sector. However, none of these studies has considered the situation in Iraq. This paper examines the current literature to ascertain the parameters affecting m-commerce adoption in Iraq
Complexity of the quorum intersection property of the Federated Byzantine Agreement System

A Federated Byzantine Agreement System is defined as a pair $(V, Q)$ comprising a set of nodes $V$ and a quorum function $Q: V \mapsto 2^{2^{V}} \setminus \{\emptyset\}$ specifying for each node a set of subsets of nodes, called quorum slices. A subset of nodes is a quorum if and only if for each of its nodes it also contains at least one of its quorum slices. The Disjoint Quorums Problem answers the question whether a given instance of \acrlong{fbas} contains two quorums that have no nodes in common. We show that this problem is $\mathsf{NP-complete}$. We also study the problem of finding a quorum of minimal size and show it is $\mathsf{NP-hard}$. Further, we consider the problem of checking whether a given subset of nodes contains a quorum for some selected node. We show this problem is $\mathsf{P-complete}$ and describe a method that solves it in linear time with respect to number of nodes and the total size of all quorum slices. Moreover, we analyze the complexity of some of these problems using the parametrized point of view.
Structured Group Local Sparse Tracker

Sparse representation is considered as a viable solution to visual tracking. In this paper, we propose a structured group local sparse tracker (SGLST), which exploits local patches inside target candidates in the particle filter framework. Unlike the conventional local sparse trackers, the proposed optimization model in SGLST not only adopts local and spatial information of the target candidates but also attains the spatial layout structure among them by employing a group-sparsity regularization term. To solve the optimization model, we propose an efficient numerical algorithm consisting of two subproblems with the closed-form solutions. Both qualitative and quantitative evaluations on the benchmarks of challenging image sequences demonstrate the superior performance of the proposed tracker against several state-of-the-art trackers.
Engineered Biosynthesis of Fluorinated Polyketides

Author(s): Thuronyi, Benjamin Williams | Advisor(s): Chang, Michelle C Y | Abstract: Natural products use exquisite structural complexity to tailor a limited repertoire of functional groups for different molecular functions. These molecules have been adopted or adapted by humans to create a large fraction of current therapeutics. However, those adaptations have not always been able to combine the chemical diversity possible for totally synthetic compounds with the many advantages of biosynthesis. Extremely rare in nature but ubiquitous in synthetic pharmaceuticals, fluorine is a unique tool for molecular design, and an excellent candidate for expanding biosynthetic diversity. This dissertation describes our work to enable efficient fluorine incorporation into complex biosynthetic pathways, both in vitro and in the context of a whole organism, and our progress in enabling fluorinated polyketide biosynthesis. We hope to set the stage for a dramatic expansion in polyketide structural diversity via engineered biosynthesis, using a design element that could improve bioactive properties and furnish new therapeutics.
Synthetic aperture microwave imaging with active probing for fusion plasma diagnostics

A Synthetic Aperture Microwave Imaging (SAMI) system has been designed and built to obtain 2-D images at several frequencies from fusion plasmas. SAMI uses a phased array of linearly polarised antennas. The array configuration has been optimised to achieve maximum synthetic aperture beam efficiency. The signals received by antennas are down-converted to the intermediate frequency range and then recorded in a full vector form. Full vector signals allow beam focusing and image reconstruction in both real time and a post processing mode. SAMI can scan over 16 preprogrammed frequencies in the range of 10–35 GHz with a switching time of 300ns. The system operates in 2 different modes simultaneously: both a `passive' imaging of plasma emission and also an `active' imaging of the back-scattered signal of the radiation launched by one of the antennas from the same array. This second mode is similar to so-called Doppler backscattering (DBS) reflectometry with 2-D resolution of the propagation velocity of turbulent structures. Both modes of operation show good performance in a real fusion plasma experiments on Mega Amp Spherical Tokamak (MAST). We have obtained the first ever 2-D images of BXO mode conversion windows. With active probing, the first ever turbulence velocity maps have been obtained. In this article we present an overview of the diagnostic and discuss recent results.
LapEPI-Net: A Laplacian Pyramid EPI structure for Learning-based Dense Light Field Reconstruction

For dense sampled light field (LF) reconstruction problem, existing approaches focus on a depth-free framework to achieve non-Lambertian performance. However, they trap in the trade-off "either aliasing or blurring" problem, i.e., pre-filtering the aliasing components (caused by the angular sparsity of the input LF) always leads to a blurry result. In this paper, we intend to solve this challenge by introducing an elaborately designed epipolar plane image (EPI) structure within a learning-based framework. Specifically, we start by analytically showing that decreasing the spatial scale of an EPI shows higher efficiency in addressing the aliasing problem than simply adopting pre-filtering. Accordingly, we design a Laplacian Pyramid EPI (LapEPI) structure that contains both low spatial scale EPI (for aliasing) and high-frequency residuals (for blurring) to solve the trade-off problem. We then propose a novel network architecture for the LapEPI structure, termed as LapEPI-net. To ensure the non-Lambertian performance, we adopt a transfer-learning strategy by first pre-training the network with natural images then fine-tuning it with unstructured LFs. Extensive experiments demonstrate the high performance and robustness of the proposed approach for tackling the aliasing-or-blurring problem as well as the non-Lambertian reconstruction.
Self-attention Aligner: A Latency-control End-to-end Model for ASR Using Self-attention Network and Chunk-hopping

Self-attention network, an attention-based feedforward neural network, has recently shown the potential to replace recurrent neural networks (RNNs) in a variety of NLP tasks. However, it is not clear if the self-attention network could be a good alternative of RNNs in automatic speech recognition (ASR), which processes the longer speech sequences and may have online recognition requirements. In this paper, we present a RNN-free end-to-end model: self-attention aligner (SAA), which applies the self-attention networks to a simplified recurrent neural aligner (RNA) framework. We also propose a chunk-hopping mechanism, which enables the SAA model to encode on segmented frame chunks one after another to support online recognition. Experiments on two Mandarin ASR datasets show the replacement of RNNs by the self-attention networks yields a 8.4%-10.2% relative character error rate (CER) reduction. In addition, the chunk-hopping mechanism allows the SAA to have only a 2.5% relative CER degradation with a 320ms latency. After jointly training with a self-attention network language model, our SAA model obtains further error rate reduction on multiple datasets. Especially, it achieves 24.12% CER on the Mandarin ASR benchmark (HKUST), exceeding the best end-to-end model by over 2% absolute CER.
Detecting Colorized Images via Convolutional Neural Networks: Toward High Accuracy and Good Generalization

Image colorization achieves more and more realistic results with the increasing computation power of recent deep learning techniques. It becomes more difficult to identify the fake colorized images by human eyes. In this work, we propose a novel forensic method to distinguish between natural images (NIs) and colorized images (CIs) based on convolutional neural network (CNN). Our method is able to achieve high classification accuracy and cope with the challenging scenario of blind detection, i.e., no training sample is available from "unknown" colorization algorithm that we may encounter during the testing phase. This blind detection performance can be regarded as a generalization performance. First, we design and implement a base network, which can attain better performance in terms of classification accuracy and generalization (in most cases) compared with state-of-the-art methods. Furthermore, we design a new branch, which analyzes smaller regions of extracted features, and insert it into the above base network. Consequently, our network can not only improve the classification accuracy, but also enhance the generalization in the vast majority of cases. To further improve the performance of blind detection, we propose to automatically construct negative samples through linear interpolation of paired natural and colorized images. Then, we progressively insert these negative samples into the original training dataset and continue to train the network. Experimental results demonstrate that our method can achieve stable and high generalization performance when tested against different state-of-the-art colorization algorithms.
Metric Properties of Homogeneous and Spatially Inhomogeneous $F$ -Divergences

In this paper we investigate the construction and the properties of spatially inhomogeneous divergences, functionals arising from optimal Entropy-Transport problems that are computed in terms of an entropy function <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> and a cost function. Starting from the power-like entropy <inline-formula> <tex-math notation="LaTeX">$F(s)=(s^{p}-p(s-1)-1)/ (p(p-1))$ </tex-math></inline-formula> and a suitable cost depending on a metric <inline-formula> <tex-math notation="LaTeX">$\mathsf {d}$ </tex-math></inline-formula> on a space <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>, our main result ensures that for every <inline-formula> <tex-math notation="LaTeX">$p>1$ </tex-math></inline-formula> the related inhomogeneous divergence induces a distance on the space of finite measures over <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>. We also study in detail the pure entropic setting, that can be recovered as a particular case when the transport is forbidden. In this situation, corresponding to the classical theory of <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula>-divergences, we show that the construction naturally produces a symmetric divergence and we highlight the important role played by the class of Matusita’s divergences.
On the Equivalence of Semidifinite Relaxations for MIMO Detection with General Constellations

The multiple-input multiple-output (MIMO) detection problem is a fundamental problem in modern digital communications. Semidefinite relaxation (SDR) based algorithms are a popular class of approaches to solving the problem because the algorithms have a polynomial-time worst-case complexity and generally can achieve a good detection error rate performance. In spite of the existence of various different SDRs for the MIMO detection problem in the literature, very little is known about the relationship between these SDRs. This paper aims to fill this theoretical gap. In particular, this paper shows that two existing SDRs for the MIMO detection problem, which take quite different forms and are proposed by using different techniques, are equivalent. As a byproduct of the equivalence result, the tightness of one of the above two SDRs under a sufficient condition can be obtained.
Group testing: an information theory perspective

The group testing problem concerns discovering a small number of defective items within a large population by performing tests on pools of items. A test is positive if the pool contains at least one defective, and negative if it contains no defectives. This is a sparse inference problem with a combinatorial flavour, with applications in medical testing, biology, telecommunications, information technology, data science, and more. In this monograph, we survey recent developments in the group testing problem from an information-theoretic perspective. We cover several related developments: efficient algorithms with practical storage and computation requirements, achievability bounds for optimal decoding methods, and algorithm-independent converse bounds. We assess the theoretical guarantees not only in terms of scaling laws, but also in terms of the constant factors, leading to the notion of the {\em rate} of group testing, indicating the amount of information learned per test. Considering both noiseless and noisy settings, we identify several regimes where existing algorithms are provably optimal or near-optimal, as well as regimes where there remains greater potential for improvement. In addition, we survey results concerning a number of variations on the standard group testing problem, including partial recovery criteria, adaptive algorithms with a limited number of stages, constrained test designs, and sublinear-time algorithms.