Miracle-dz/UniFAR-FTUs huggingface dataset:
User-Centric Base-Station Wireless Access Virtualization for Future 5G Networks

User-centric wireless access virtualization (WAV) allows each user to be served by a set of carefully selected transmission points (TPs) forming a user-specific virtual base station (uVBS) adapted to its environment and quality-of-service (QoS) requirement. In this way, this new concept breaks away from the conventional cell-centric architecture to provide boundaryless communications in future fifth-generation (5G) networks. This fundamental structural 5G evolution and the ultra-dense multi-tier heterogeneous context foreseen in such networks require an inevitable rethinking of efficient scalable TP clustering. As such, this paper proposes three innovative low-cost clustering approaches that enable the user-centric WAV and provide dynamic, adaptive, and overlapping TP clusters while requiring not only negligible overhead cost but also minimum signaling changes at both network and user sides. Contrary to existing clustering techniques, the new ones we propose better leverage the 5G features such as extreme densification and massive connectivity as well as new concepts such as millimeter wave (mmWave) spectrum and massive multiple-input-multiple-output (MIMO). The simulations show that they may achieve until 154% and 282% of throughput and coverage gains, respectively. Furthermore, these approaches are flexible enough to be adapted to different network dimensions (i.e., space and time), thereby paving the way for achieving the dramatic performance improvements required by the 5G networks to cope with the upcoming mobile data deluge.
SAX: a new and efficient assembler for solving DNA Fragment Assembly Problem

In the past, the Fragment Assembly Problem has been solved efficiently by many metaheuristics. In this work, we propose a new one, called SAX, which consists in combining two metaheuristics: a trajec- tory method as Simulated Annealing and a population-based method as Genetic Algorithm. We also analyze the relative advantages of this hybridization against other assemblers from literature. From this analy- sis, we conclude that SAX improves the quality results found by other metaheuristic and non-metaheuristic assemblers for solving 100% of the largest instances for this problem.
Impacts of Fractional Hot-Deck Imputation on Learning and Prediction of Engineering Data

In broad engineering fields, missing data is a common issue which often causes undesired bias and sparseness impeding rigorous data analyses. To tackle this problem, many imputation theories have been proposed and widely used. However, prior methods often require distributional assumptions and prior knowledge regarding data which may cause some difficulty for engineering research. Essentially, the fractional hot-deck imputation (FHDI) is an assumption-free imputation method, holding broad applicability in the engineering domains. FHDIs internal parameters and impact on statistical and machine learning methods, however, have been rarely understood. Thus, this study investigates the behavior and impacts of FHDI on prediction methods including generalized additive model, support vector machine, extremely randomized trees, and artificial neural network, for which four practical datasets (appliance energy, air quality, phenotypes, and weather) are used. Results show that FHDI performs better for improving the prediction accuracy compared to a simple naive method which cures missing data using the mean value of attributes, and FHDI has an asymptotically positive effect on prediction accuracy with decreasing response rates. Regarding an optimal setting, 30 to 35 is recommended for the FHDIs internal categorization number while 5 is recommended for the FHDI donors, which is aligned with Rubins recommendation.
Abstracts Presented at the American Chemical Society National Meetings: Analysis of Their Characteristics

ABSTRACT This paper analyzes the abstracts from the 244th – 248th American Chemical Society national meetings, 2012–2014 for the Divisions of Biological Chemistry and Organic Chemistry. The main objective of the study was to identify the percentage of abstracts that were published as related papers and the time it took to do so. It also investigated likely factors that determined whether the abstracts will be published as papers. These factors included the number of author(s) per abstract, the number of institutions involved in the research, and the country of origin of the research. The paper also highlights the percentage of papers that were published before and after the conferences and the quality of the journals in which they were published. The citation characteristics of the articles published before and after the conference are also covered.
A Provably Secure and Lightweight Anonymous User Authenticated Session Key Exchange Scheme for Internet of Things Deployment

With the ever increasing adoption rate of Internet-enabled devices [also known as Internet of Things (IoT) devices] in applications such as smart home, smart city, smart grid, and healthcare applications, we need to ensure the security and privacy of data and communications among these IoT devices and the underlying infrastructure. For example, an adversary can easily tamper with the information transmitted over a public channel, in the sense of modification, deletion, and fabrication of data-in-transit and data-in-storage. Time-critical IoT applications such as healthcare may demand the capability to support external parties (users) to securely access IoT data and services in real-time. This necessitates the design of a secure user authentication mechanism, which should also allow the user to achieve security and functionality features such as anonymity and un-traceability. In this paper, we propose a new lightweight anonymous user authenticated session key agreement scheme in the IoT environment. The proposed scheme uses three-factor authentication, namely a user’s smart card, password, and personal biometric information. The proposed scheme does not require the storing of user specific information at the gateway node. We then demonstrate the proposed scheme’s security using the broadly accepted real-or-random (ROR) model, Burrows–Abadi–Needham (BAN) logic, and automated validation of Internet security protocols and applications (AVISPAs) software simulation tool, as well as presenting an informal security analysis to demonstrate its other features. In addition, through our simulations, we demonstrate that the proposed scheme outperforms existing related user authentication schemes, in terms of its security and functionality features, and computation costs.
Executive Information Systems: Successes and Failures, Insights and Misconceptions

Executive information systems have come a long way in a short period of time. Management consultants, software and hardware vendors, the MIS department, and executive users have all played important rotes in this development. This article reviews executive information systems, assesses where they are today, and looks ahead to what is Just over the horizon.
Understanding Undesirable Word Embedding Associations

Word embeddings are often criticized for capturing undesirable word associations such as gender stereotypes. However, methods for measuring and removing such biases remain poorly understood. We show that for any embedding model that implicitly does matrix factorization, debiasing vectors post hoc using subspace projection (Bolukbasi et al., 2016) is, under certain conditions, equivalent to training on an unbiased corpus. We also prove that WEAT, the most common association test for word embeddings, systematically overestimates bias. Given that the subspace projection method is provably effective, we use it to derive a new measure of association called the relational inner product association (RIPA). Experiments with RIPA reveal that, on average, skipgram with negative sampling (SGNS) does not make most words any more gendered than they are in the training corpus. However, for gender-stereotyped words, SGNS actually amplifies the gender association in the corpus.
Are Red Roses Red? Evaluating Consistency of Question-Answering Models

Although current evaluation of question-answering systems treats predictions in isolation, we need to consider the relationship between predictions to measure true understanding. A model should be penalized for answering “no” to “Is the rose red? ” if it answers “red” to “What color is the rose? ”. We propose a method to automatically extract such implications for instances from two QA datasets, VQA and SQuAD, which we then use to evaluate the consistency of models. Human evaluation shows these generated implications are well formed and valid. Consistency evaluation provides crucial insights into gaps in existing models, while retraining with implication-augmented data improves consistency on both synthetic and human-generated implications.
Exploring the Use of Synthetic Gradients for Distributed Deep Learning across Cloud and Edge Resources

With the explosive growth of data, largely contributed by the rapidly and widely deployed smart devices on the edge, we need to rethink the training paradigm for learning on such realworld data. The conventional cloud-only approach can hardly keep up with the computational demand from these deep learning tasks; and the traditional back propagation based training method also makes it difficult to scale out the training. Fortunately, the continuous advancement in System on Chip (SoC) hardware is transforming edge devices into capable computing platforms, and can potentially be exploited to address these challenges. These observations have motivated this paper’s study on the use of synthetic gradients for distributed training cross cloud and edge devices. We employ synthetic gradients into various neural network models to comprehensively evaluate its feasibility in terms of accuracy and convergence speed. We distribute the training of the various layers of a model using synthetic gradients, and evaluate its effectiveness on the edge by using resource-limited containers to emulate edge devices. The evaluation result shows that the synthetic gradient approach can achieve comparable accuracy compared to the conventional back propagation, for an eight-layer model with both fully-connected and convolutional layers. For a more complex model (VGG16), the training suffers from some accuracy degradation (up to 15%). But it achieves 11% improvement in training speed when the layers of a model are decoupled and trained on separate resource-limited containers, compared to the training of the whole model using the conventional method on the physical machine.
2D Contour Following with an Unmanned Aerial Manipulator: Towards Tactile-Based Aerial Navigation

In this paper a force controller via energy tanks is implemented for novel applications in aerial contour follow. This control approach allows the aerial vehicle to trace out a boundary whilst in continuous contact with a surface by means of an actively compliant manipulator. This represents the first step towards tactile-based aerial navigation, which can be used to complement more traditional mapping approaches such as visual SLAM. Key results show that the energy-based approach can be used to apply a continuous shear force through the manipulator while the vehicle remains in contact with the surface of interest. Results also show the robustness and repeatability of this approach for prolonged aerial interaction, and the potential for future use in more complex, un-modeled environments.
Look Harder: A Neural Machine Translation Model with Hard Attention

Soft-attention based Neural Machine Translation (NMT) models have achieved promising results on several translation tasks. These models attend all the words in the source sequence for each target token, which makes them ineffective for long sequence translation. In this work, we propose a hard-attention based NMT model which selects a subset of source tokens for each target token to effectively handle long sequence translation. Due to the discrete nature of the hard-attention mechanism, we design a reinforcement learning algorithm coupled with reward shaping strategy to efficiently train it. Experimental results show that the proposed model performs better on long sequences and thereby achieves significant BLEU score improvement on English-German (EN-DE) and English-French (ENFR) translation tasks compared to the soft attention based NMT.
Optimization and evaluation of probabilistic-logic sequence models

Analysis of biological sequence data demands more and more sophisticated and fine-grained models, but these in turn introduce hard computational problems. A class of probabilistic-logic models is considered, which increases the expressive power from HMM’s and SCFG’s regular and context-free languages to, in principle, Turing complete languages. In general, such models are computationally far too complex for direct use, so optimization by pruning and approximation is needed. The first steps are made towards a methodology for optimizing the models by approximations using auxiliary models for preprocessing or splitting them into submodels. An evaluation method for approximating models is suggested based on automatic generation of samples. These models and evaluation processes are illustrated in the PRISM system developed by other authors.
Better Generalization with Adaptive Adversarial Training

An effective method to obtain an adversarial robust network is to train the network with adversar-ially perturbed samples. Training with adversari-ally perturbed samples increases the robustness of the network signiﬁcantly, but affects the generalization of the network to unperturbed points. We propose an adaptive training method which aims to perturb only a small fraction of the training samples which aids not only adversarial robustness but also better generalization as compared to perturbing all the training samples. This method is also faster than perturbing the entire training set.
Machine Translation Human Evaluation: an investigation of evaluation based on Post-Editing and its relation with Direct Assessment

In this paper we present an analysis of the two most prominent methodologies used for the human evaluation of MT quality, namely evaluation based on Post-Editing (PE) and evaluation based on Direct Assessment (DA). To this purpose, we exploit a publicly available large dataset containing both types of evaluations. We first focus on PE and investigate how sensitive TER-based evaluation is to the type and number of references used. Then, we carry out a comparative analysis of PE and DA to investigate the extent to which the evaluation results obtained by methodologies addressing different human perspectives are similar. This comparison sheds light not only on PE but also on the so-called reference bias related to monolingual DA. Also, we analyze if and how the two methodologies can complement each other’s weaknesses.
Multi-dimensional extension of the alternating minimization algorithm in x-ray computed tomography

OF THE DISSERTATION Multi-Dimensional Extension of the Alternating Minimization Algorithm in X-ray Computed Tomography by Jingwei Lu Doctor of Philosophy in Electrical Engineering Washington University in St. Louis, 2019 Professor Joseph A. O’Sullivan, Chair X-ray computed tomography (CT) is an important and effective tool in medical and industrial imaging applications. The state-of-the-art methods to reconstruct CT images have had great development but also face challenges. This dissertation derives novel algorithms to reduce bias and metal artifacts in a wide variety of imaging modalities and increase performance in low-dose scenarios. The most widely available CT systems still use the single-energy CT (SECT), which is good at showing the anatomic structure of the patient body. However, in SECT image reconstruction, energy-related information is lost. In applications like radiation treatment planning and dose prediction, accurate energy-related information is needed. Spectral CT has shown the potential to extract energy-related information.
Language identification in texts

This work investigates the task of identifying the language of digitally encoded text. Automatic methods for language identification have been developed since the 1960s. During the years, the significance of language identification as an important preprocessing element has grown at the same time as other natural language processing systems have become mainstream in day-to-day applications. The methods used for language identification are mostly shared with other text classification tasks as almost any modern machine learning method can be trained to distinguish between different languages. We begin the work by taking a detailed look at the research so far conducted in the field. As part of this work, we provide the largest survey on language identification available so far (Publication 1). Comparing the performance of different language identification methods presented in the literature has been difficult in the past. Before the introduction of a series of language identification shared tasks at the VarDial workshops, there were no widely accepted standard datasets which could be used to compare different methods. The shared tasks mostly concentrated on the issue of distinguishing between similar languages, but other open issues relating to language identification were addressed as well. In this work, we present the methods for language identification we have developed while participating in the shared tasks from 2015 to 2017 (Publications 2, 3, and 4). Most of the research for this work was accomplished within the Finno-Ugric Languages and the Internet project. In the project, our goal was to find and collect texts written in rare Uralic languages on the Internet (Publication 6). In addition to the open issues addressed at the shared tasks, we dealt with issues concerning domain compatibility and the number of languages. We created an evaluation setup for addressing short out-of-domain texts in a large number of languages. Using the set-up, we evaluated our own method as well as other promising methods from the literature (Publication 5). The last issue we address in this work is the handling of multilingual documents. We developed a method for language set identification and used a previously published dataset to evaluate its performance (Publication 7).
Multidimensional Warnings: Evaluating Curve Warning Stimuli in an On-Road Environment

Horizontal curves on roadways are the site of numerous crashes and motorist deaths each year. Traditional methods to warn drivers of curve hazards, including static roadside signs, are sometimes ineffective at influencing driver behavior for reasons such as driver acclimation to inconsistency in posted advisory speeds. In-vehicle curve warning devices (CWDs) may be an effective alternative for reducing the number of collisions at curves. Multi-modality displays have elicited positive driver results and should be further explored. The objective of this study was to determine the most effective curve warning system using on-road performance and subjective evaluation. Two top-performing warning stimulus presentations, as determined by a recent simulator study, were tested at the Virginia Smart Road closed test highway. Both warnings exhibited auditory (speech) and visual (Heads Down Display) stimuli, however one included a throttle pushback haptic stimulus and the other did not. No on-road studies of this type of haptic stimulus, nor of CWDs, have been published to date. Forty-eight individuals, 24 age 18-25 years and 24 age >60 years, participated in the study. A 2 (Age) x 3 (Stimulus Presentation) between-subjects design was used to examine participant performance and ratings for the first “surprise” experience with the stimulus while driving on the Smart Road, and a 2x3 mixed factors design examined stimulus as a repeated measure. Participant braking reaction times, speed, and subjective evaluations were compared between stimulus presentations as well as driver age. Throttle reaction times and brake reaction times were significantly quicker, and curve entry speed significantly closer to an advisory speed for participants receiving a warning presentation than those without a warning presentation at α=0.05. No statistical differences between objective measures were found between the stimulus presentation with the haptic and the stimulus presentation without the haptic stimulus. Age was a significant main effect as older drivers reached more appropriate curve entry speeds than younger drivers. Driver risk-taking style was significantly related to age and to curve entry speed. During an interview, participants demonstrated higher comprehension when presented with the stimulus lacking the haptic component, and ranked this presentation higher, though ratings gathered from questionnaires were not significantly different between the two stimulus presentations. Driver comments were examined using a content analysis technique organized by design guideline topics. Discussion was presented in terms of four main research questions and recommendations toward CWD design guidelines were developed based on the objective and subjective results of this study. In addition, this research developed a foundation for further on-road testing of CWDs and other multi-modal in-vehicle warning systems.
Matrix completion from a computational statistics perspective

In the matrix completion problem, we seek to estimate the missing entries of a matrix from a small sample of the total number of entries in a matrix. While this task is hopeless in general, structured matrices that are appropriately sampled can be completed with surprising accuracy. In this review, we examine the success behind low‐rank matrix completion, one of the most studied and employed versions of matrix completion. Formulating the matrix completion problem as a low‐rank matrix estimation problem admits several strengths: good empirical performance on real data, statistical guarantees, and practical algorithms with convergence guarantees. We also examine how matrix completion relates to the classical study of missing data analysis (MDA) in statistics. By drawing on the MDA perspective, we see opportunities to weaken the commonly enforced assumption of missing completely at random in matrix completion.
Desinformación: retos profesionales para el sector de la comunicación

El auge de la desinformacion propicia el debate sobre su impacto en la sociedad y las posibilidades y herramientas para luchar contra ella. Los profesionales de la comunicacion se encuentran ante nuevos contextos que afectan a la credibilidad de su trabajo. Este estudio trata de conocer como perciben el fenomeno los expertos, identificando posibles cambios en sus rutinas de trabajo. Se realizaron 24 entrevistas estructuradas a consultores politicos, verificadores de informacion, periodistas y responsables de redes sociales de medios digitales. Se evidencia que los cambios en los protocolos de trabajo resultan aun menores, aunque todos los actores sociales se hayan visto directamente afectados por el impacto de la desinformacion. Tampoco hay consenso en torno a la creacion de un posible marco legislativo que la aborde. Los resultados pueden ayudar a enfocar nuevas investigaciones en este campo, asi como ofrecer una aproximacion al estado actual de los profesionales de la comunicacion ante el fenomeno.