Miracle-dz/UniFAR-FTUs huggingface dataset:
A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning

Minimizing a non-smooth function over the Grassmannian appears in many applications in machine learning. In this paper we show that if the objective satisfies a certain Riemannian regularity condition with respect to some point in the Grassmannian, then a Riemannian subgradient method with appropriate initialization and geometrically diminishing step size converges at a linear rate to that point. We show that for both the robust subspace learning method Dual Principal Component Pursuit (DPCP) and the Orthogonal Dictionary Learning (ODL) problem, the Riemannian regularity condition is satisfied with respect to appropriate points of interest, namely the subspace orthogonal to the sought subspace for DPCP and the orthonormal dictionary atoms for ODL. Consequently, we obtain in a unified framework significant improvements for the convergence theory of both methods.
Adversarial Music: Real World Audio Adversary Against Wake-word Detection System

Voice Assistants (VAs) such as Amazon Alexa or Google Assistant rely on wake-word detection to respond to people's commands, which could potentially be vulnerable to audio adversarial examples. In this work, we target our attack on the wake-word detection system. Our goal is to jam the model with some inconspicuous background music to deactivate the VAs while our audio adversary is present. We implemented an emulated wake-word detection system of Amazon Alexa based on recent publications. We validated our models against the real Alexa in terms of wake-word detection accuracy. Then we computed our audio adversaries with consideration of expectation over transform and we implemented our audio adversary with a differentiable synthesizer. Next we verified our audio adversaries digitally on hundreds of samples of utterances collected from the real world. Our experiments show that we can effectively reduce the recognition F1 score of our emulated model from 93.4% to 11.0%. Finally, we tested our audio adversary over the air, and verified it works effectively against Alexa, reducing its F1 score from 92.5% to 11.0%. To the best of our knowledge, this is the first real-world adversarial attack against a commercial grade VA wake-word detection system. Our demo video is included in the supplementary material.
Advancing ICT4D Research through Service-dominant Logic

The information systems (IS) discipline has not accorded the same attention to theory testing as it has to theory building. Further, crowdsourcing presents rich opportunities for the theory testing process that have not been fully explored. This paper builds on previous work, employing a design science research (DSR) paradigm in order to develop a decision support system artefact that will help early career researchers identify viable theory testing approaches, and how crowd sourcing can help facilitate the testing process. As part of the DSR build/evaluate cycle, this paper presents a conceptual framework and model of theory testing in IS, and the problem frame in which they are situated is evaluated using Scho_n’s theory of reflective practice and problem/solution framing. Data collected from PhD students revealed an incomplete level of knowledge of theory testing, and a lack of awareness of the possibilities provided by adopting a crowdsourcing strategy.
Cross-layer routing protocol for event-driven M2M communication in IoT-assisted Smart City Planning and Management: CWSN-eSCPM

The robustness of wireless sensor networks (WSNs) has broadened the use of wireless communication systems for modern machine-to-machine (M2M) communication. Internet-of-Things (IoT) being one of the most explored technologies has gained wide attention due to low-cost WSN communication systems. IoT is one of the dominant technologies used in Smart City implementation demands better communication paradigm including mobility assisted transmission paradigms; however, native IEEE 802.15. 4 standard does not have mobility provision that limits its use for real-time applications. On contrary, implementing mobility with classical WSN might impose significantly great topological changes and node or network condition variations which cannot be dealt with the traditional reactive routing approach. Incorporating node and/or network awareness with proactive network management can enable classical WSN to support mobility, which can be vital for IoT based Smart City Planning and Management (SCPM). With this motivation, in this study, a robust Cross-Layer Architecture based WSN Routing Protocol for Event-Driven M2M Communication in SCPM (CWSN-eSCPM) is developed. CWSN-eSCPM encompasses Proactive Node Management Strategy, Data-Centric Service Differentiation and Fair Resource Scheduling (DCSDFRS), Packet Velocity Estimation, Cumulative Congestion Estimation, Dynamic Link Assessment that enables optimal Best Forwarding Node selection for deadline sensitive and reliable data communication. CWSN-eSCPM applies dynamic link quality, cumulative congestion degree, and packet velocity of a node to enable optimal routing decision. DCSDFRS enables optimal resource provision to the real-time data while assuring maximum possible resource for non-real-time data that assures Quality of Service provision for event-driven critical communication in SCPM. Noticeably, CWSN-eSCPM protocol has been applied on top of IEEE 802.15. 4 protocol standard, while preserving backward compatibility feature, it can be used for WSN assisted communication purposes. The authors' proposed protocol has performed better in terms of packet delivery ratio, packet loss ratio and deadline miss ratio for both real-time data as well as non-real-time data.
Remote Controlled Mechtronics System

: This paper presents controlling of mechatronics system using remote operated. This system has a special feature of smart video transfer and capture feature. The images are transferred to the monitor by the surveillance system. The system is always controlled by the remote operated by human.The remote controlled mechatronics system is aim to use in the situation like natural calamities, to inspect and navigate from remote location and collect the video without wires. It Just by throwing from window the ball can send the images inside the room and also can navigate the video ball from one room to another room to look for people. This is perfect for changeable lighting conditions. High-resolution security cameras have additional light sensors on their digital chips. This includes augment the effective image quality. These cameras are effectual only with high-resolution monitors. Small in size and Simpler to setup. The Wireless digital cameras give sharper effects. In video ball the encoders are given as an input and the decoders are the output. The program is given to the system it transmits by the transmitter and the monitor receive the images by receiver. The wireless video transmit the signal and at remote location the live video issued to inspect the areas where are accessible and non accessible.
Fully Dynamic Consistent Facility Location

We consider classic clustering problems in fully dynamic data streams, where data elements can be both inserted and deleted. In this context, several parameters are of importance: (1) the quality of the solution after each insertion or deletion, (2) the time it takes to update the solution, and (3) how different consecutive solutions are. The question of obtaining efficient algorithms in this context for facility location, $k$-median and $k$-means has been raised in a recent paper by Hubert-Chan et al. [WWW'18] and also appears as a natural follow-up on the online model with recourse studied by Lattanzi and Vassilvitskii [ICML'17] (i.e.: in insertion-only streams). In this paper, we focus on general metric spaces and mainly on the facility location problem. We give an arguably simple algorithm that maintains a constant factor approximation, with $O(n\log n)$ update time, and total recourse $O(n)$. This improves over the naive algorithm which consists in recomputing a solution at each time step and that can take up to $O(n^2)$ update time, and $O(n^2)$ total recourse. These bounds are nearly optimal: in general metric space, inserting a point take $O(n)$ times to describe the distances to other points, and we give a simple lower bound of $O(n)$ for the recourse. Moreover, we generalize this result for the $k$-medians and $k$-means problems: our algorithm maintains a constant factor approximation in time $\widetilde{O}(n+k^2)$. We complement our analysis with experiments showing that the cost of the solution maintained by our algorithm at any time $t$ is very close to the cost of a solution obtained by quickly recomputing a solution from scratch at time $t$ while having a much better running time.
Review of treatments for severe personality disorder

This review was commissioned by the Home Office, Department of Health and Prison Service DSPD Programme. The aim of the review was to update the evidence base on treatments for severe personality disorder, as part of policy development on DSPD. The review was also intended to make recommendations about promising treatments for severe personality disorder, to inform the development of services for this group in high secure facilities.
Essentials Of Infectious Disease Epidemiology

As WHO Director-General Nakajima says of infectious disease, "No country is safe from them. " As public health education grows, so, too will the need for methodologic skills in infectious disease epidemiology. Without these crucial skills, there will be insufficient resources to improve the health of the public, both domestically and globally. Essentials of Infectious Disease Epidemiology is one of the few texts devoted specifically to the methods required to study infectious disease making the perfect introduction to the field for undergraduate and introductory masters-level public health students. It will provide students with the requisite skills to conduct, evaluate, and understand the field of infectious disease epidemiology. Looking for more real-life evidence? Check out Cases 1, 2, 19, & 21 in Essential Case Studies in Public Health, Putting Public Health into Practice.
Prosthodontics for the Elderly: Diagnosis and Treatment

Epidemiology - dental and prosthetic status of older adults age-related changes of the masticatory apparatus masticatory function and nutrition prosthodontics treatment planning in older adults prosthodontics treatment planning for the frail or dependent elderly treatment with fixed prosthodontics treatment with removable partial dentures root-supported overdentures treatment of edentulous patients preventative measures and maintenance care after prosthetic treatment.
Web-Based Automation Speech-to-Text Application using Audio Recording for Meeting Speech

The use of technology is very important nowadays. Most of everyday activities are documented and stored in digital form. One of those activity is recording. Activity of an organization is very important, therefore recording of meeting material is one of those important activities. Usually the meeting material is documented by writing them into papers or typing them and stored them into computer. Sometimes, the meeting information is written or typed incorrectly so a speech-to-text application is required to solve this problem. In this study, the solution offered is to implement a web-based automation speed-to-text application which can record the voice of meeting participants then converted them into text automatically, so the results of the recording process of meeting materials are more effective and efficient By using voice recognition feature its called web kit Speech Recognition, this system can be implemented. After successfully implementing this application which is so called Speech Meeting Web kit system, the average value for duration system for Indonesia and English language is 96,63 % and 82,78 %.
Analyzing user typing behaviour in different positions using keystroke dynamics for mobile phones

Abstract Hold time and latency are the most frequently used features for developing a user typing profile for authenticating mobile phone users. In this paper, we combine these features along with motion features, i.e., the X, Y and Z coordinates at which the user holds the mobile while typing and take the input from the user when he/she is sitting, walking and relaxing. We then address three questions (1) whether there is any change in the typing pattern of the user when his typing position (relaxing, sitting or walking) changes? ; (2) in which position the user has the unique typing pattern? ; (3) which is the best feature vector to consider for authentication? The results showed that the user typing pattern differs in different positions and also with the change in phone orientation. A user has the unique pattern when he/she is in relaxing mode and holding the mobile in landscape orientation and the best feature vector to build a user profile is by combining hold time, latency and motion features.
Double-layer Support Vector Machine for Robust and Efficient Classification

In this paper, double-layer support vector machine (DLSVM) for binary classification is proposed that can expeditiously learn a classification model under the premise of ensuring accuracy. In the first layer, least squares support vector machine (LSSVM) is used to evaluate all samples in the data set. Support vector machine (SVM) utilize a sparse data set to train the binary classifier in the second layer. In order to obtain excellent sparse data set for SVM, the normalized norm sequence and the normalized Lagrange multiplier Shannon entropy sequence are introduced. DLSVM provides low computation cost because the two-layer structure reduces lots of iterative operations. Experimental results show that the proposed method not only has similar classification accuracy compared to SVM but also has higher efficiency.
Dynamic management of tracking ressources for hyper-manoeuvring targets

The new generation of radars is facing increasingly threatening targets. These radars are asked to perform several tasks in parallel, including surveillance and tracking. To this aim, they can be equipped with staring antennas, so they overcome the constraints induced by the rotation of the antenna. The tracking function of the radar has thus to be upgraded to respond to the double issue of tracking highly manoeuvring targets and managing the resources to balance time between tasks. In this context, this thesis investigates new means of tracking highly manoeuvring targets. A new target model based on intrinsic coordinates to perform target tracking is proposed. This new target model is expressed in the frame of the target itself, and uses the Frenet-Serret frame, which is well suited to the description of highly dynamic manoeuvres involving normal accelerations that are much larger than earth gravity. A filtering algorithm using the special intrinsic formulation of the target model is developed. This filtering algorithm is very similar in terms of implementation to an Extended Kalman filter, and was implemented using real data. The comparison with standard target models and filtering algorithms show improvements over simple models and algorithms on a large set of trajectories. A new estimation method, relying on the least squares formulation of the smoothing approach, and taking into account kinematic jumps in the trajectory is also developed. This method also shows improvements over a set of common algorithms based on standard manoeuvre detection. And independently, we investigate the issue of update rate adaptation for radar measurements. A very general update rate adaptation algorithm is derived to optimise the time of revisit of each target, allowing to preserve the radar time budget for other tasks simultaneously performed, such as surveillance.
Data-dependence of plateau phenomenon in learning with neural network—statistical mechanical analysis

The plateau phenomenon, wherein the loss value stops decreasing during the process of learning, has been reported by various researchers. The phenomenon was actively inspected in the 1990s and found to be due to the fundamental hierarchical structure of neural network models. Then, the phenomenon has been thought of as inevitable. However, the phenomenon seldom occurs in the context of recent deep learning. There is a gap between theory and reality. In this paper, using statistical mechanical formulation, we clarified the relationship between the plateau phenomenon and the statistical property of the data learned. It is shown that the data whose covariance has small and dispersed eigenvalues tend to make the plateau phenomenon inconspicuous.
Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation

Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information. However, little attention has been paid to this problem. To bridge this gap we propose a model named Fidelity-oriented Product Description Generator (FPDG). FPDG takes the entity label of each word into account, since the product attribute information is always conveyed by entity words. Specifically, we first propose a Recurrent Neural Network (RNN) decoder based on the Entity-label-guided Long Short-Term Memory (ELSTM) cell, taking both the embedding and the entity label of each word as input. Second, we establish a keyword memory that stores the entity labels as keys and keywords as values, and FPDG will attend to keywords through attending to their entity labels. Experiments conducted a large-scale real-world product description dataset show that our model achieves the state-of-the-art performance in terms of both traditional generation metrics as well as human evaluations. Specifically, FPDG increases the fidelity of the generated descriptions by 25%.
Developing quantitative validation metrics to assess quality of computational mechanics models relative to reality

Developing quantitative validation metrics to assess quality of computational mechanics models relative to reality Computational models are widely used to assess and predict behaviour of engineering systems. It is desirable that simulation correctly represents the real behaviour of the system’s intended use and thus establishment of the level of quality for obtained results through validation is vital. The focus of the research presented in this thesis is the development and implementation of a novel generic validation metric for quantifying the quality of simulation results. The choice of the validation metric is governed by the data available, the outcome required and the model’s range of use. Three categories of metrics have been identified: Hypothesis testing, Frequentist and Bayesian. In general, Frequentist methods, in comparison to Hypothesis testing, allow a better understanding of the quality of the current model, through quantifying the differences between the predicted and measured results; whereas Bayesian analysis is typically used for the model parameter calibration. The work presented in this thesis concentrates on developing a Frequentist validation metric, and two novel metrics are proposed, i.e. based on a Theil’s inequality coefficient and a new relative error metric. Previously in solid mechanics validation has been applied to data points obtained from strain gauge measurements; in the present work the application is extended to data fields obtained with the aid of optical measurement techniques, e.g. displacement fields. By incorporating orthogonal decomposition the dimensionality of data fields can be reduced to coefficients in the feature vector, while preserving the key information about the deformation of the entire surface, and equivalent measured and predicted data sets can be obtained, which is essential for validation purposes. Utilising the feature vectors, both of the novel metrics provide a measure of quality of the model’s predictions relative to reality. The outcome of the Theil’s inequality coefficient is a value between 0 and 1, i.e. from excellent to poor correlation of predictions with measured data. Whereas the novel relative error metric combines the use of a threshold based on the uncertainty in the measurement data with a normalised relative error, and the quality of predictions is expressed in terms of a probability statement. Such outcome obtained with the new relative error metric is more quantitative and informative than the previous validation procedures but qualitatively equivalent. Three previously published case studies were successfully employed to demonstrate the efficacy of the novel methodologies. Ksenija Dvurečenska University of Liverpool
Point Cloud Deformation for Single Image 3d Reconstruction

We propose an approach to reconstruct a precise and dense 3d point cloud from a single image. Previous works employed reconstruction to complexity 3D shape or directly regression location from image. However, while the former requires overhead construction of 3D shape or is inefficient because of high computing cost, the latter does not scale well as the number of trainable parameters depends on the number of output points. In this paper, we explore a method to infer a point cloud representation given an input image. We extract shape information from an input image, and then we embed the two kinds of shape information into the point cloud: point-specific and global shape features. After that, we deform a randomly generated point cloud to the final representation based on the embedded point cloud feature. Our method does not require overhead construction, and is efficient and scalable because the number of trainable parameters is independent of the point cloud size, which is the first work to be able to do so according to our knowledge. Thorough experimental results suggest that our proposed method outperforms with other state-of-the-art methods in dense and precise point cloud generation.
Using distribution analysis for parameter selection in repstream

One of the most significant challenges in data clustering is the evolution of the data distributions over time. Many clustering algorithms have been introduced to deal specifically with streaming data, but common amongst them is that they require users to set input parameters. These inform the algorithm about the criteria under which data points may be clustered together. Setting the initial parameters for a clustering algorithm is itself a non-trivial task, but the evolution of the data distribution over time could mean even optimally set parameters could become non-optimal as the stream evolves. In this paper we extend the RepStream algorithm, a combination graph and density-based clustering algorithm, in a way which allows the primary input parameter, the \begin{document}$ K $\end{document} value, to be automatically adjusted over time. We introduce a feature called the edge distribution score which we compute for data in memory, as well as introducing an incremental method for adjusting the \begin{document}$ K $\end{document} parameter over time based on this score. We evaluate our methods against RepStream itself, and other contemporary stream clustering algorithms, and show how our method of automatically adjusting the \begin{document}$ K $\end{document} value over time leads to higher quality clustering output even when the initial parameters are set poorly.