Miracle-dz/UniFAR-FTUs huggingface dataset:
Word frequency effects in phonological retrieval in Modern Standard Arabic

To produce a spoken word both a wholistic lexical representation, a “lemma” and its phonological representation, a “lexeme” are accessed. The retrieval of these components is influenced by various lexical factors. Word frequency has the most empirically established effect. This effect is assumed to arise at the phonological retrieval step of lexical access. The current study asked if this word frequency effect on phonological retrieval can also be evident in the context of a diglossic language, where two varieties of a language are used by native speakers. Native Arabic speakers were asked to shadow words that varied in their frequency in Modern Standard Arabic. Latency and accuracy rates showed that shadowing was facilitated by word frequency. Implication of these finding to theories of lexical access in speech production is discussed.
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes

Wide neural networks with random weights and biases are Gaussian processes, as observed by Neal (1995) for shallow networks, and more recently by Lee et al.~(2018) and Matthews et al.~(2018) for deep fully-connected networks, as well as by Novak et al.~(2019) and Garriga-Alonso et al.~(2019) for deep convolutional networks. We show that this Neural Network-Gaussian Process correspondence surprisingly extends to all modern feedforward or recurrent neural networks composed of multilayer perceptron, RNNs (e.g. LSTMs, GRUs), (nD or graph) convolution, pooling, skip connection, attention, batch normalization, and/or layer normalization. More generally, we introduce a language for expressing neural network computations, and our result encompasses all such expressible neural networks. This work serves as a tutorial on the \emph{tensor programs} technique formulated in Yang (2019) and elucidates the Gaussian Process results obtained there. We provide open-source implementations of the Gaussian Process kernels of simple RNN, GRU, transformer, and batchnorm+ReLU network at github.com/thegregyang/GP4A. Please see our arxiv version for the complete and up-to-date version of this paper.
MULTI-TASK DEEP LEARNING WITH INCOMPLETE TRAINING SAMPLES FOR THE
IMAGE-BASED PREDICTION OF VARIABLES DESCRIBING SILK FABRICS

Abstract. This paper presents a method for the classification of images of silk fabrics with the aim to predict properties such as the place and time of origin and the production technique. The proposed method was developed in the context of the EU project SILKNOW (http://silknow.eu/). In the context of classification, we address the problem of limited as well as not fully labelled data and investigate the connection between the distinct variables. A pre-trained Convolutional Neural Network (CNN) is used for the feature extraction and a classification network realizing Multi-task learning (MTL) is trained based on these features. The training procedure is adapted to enable the consideration of images that do not have a label for all tasks. Additionally, MTL with fully labeled training data is investigated for the classification of silk fabrics. The impact of both MTL approaches is compared to singletask learning based on two different class structures. We achieve overall accuracies of 92–95 % and average F1-scores of 88–90 % in our best experiments.
Search, Solve, Create, and Share (SSCS) Learning Model: The Impact on the Students’ Creative Problem-Solving Ability on the Concept of Substance Pressure

This study aimed to determine the effectiveness of the Search, Solve, Create, and Share (SSCS) learning model and its impact on the students’ creative problem-solving ability. The study was conducted on eighth-grade students of Junior High School 24 (SMPN 24) Bandar Lampung, Indonesia, on the Substances Pressure subject matter. This is quasi-experimental with non-equivalent control group design. The independent sample t-test showed that the SSCS model influences the students’ creative problem-solving ability, of which the experimental class is higher than the control class. Furthermore, the results of the effect size obtained the d value of 2.39, indicating the SSCS learning model is effective for the students' creative problem-solving ability which belongs to the high category.
Mobile health service adoption in China

Purpose Mobile health service (MHS) can provide users with convenient health services and information to reduce their medical costs from aging or other health issues. Previous studies confirm the underdevelopment of the Chinese MHS. The purpose of this paper is to analyze the factors that affect the intention to use MHS. Design/methodology/approach This paper develops a research model that integrates personal health differences with theory of planned behavior (TPB) and protection motivation theory (PMT). This model is empirically tested using data from 494 valid questionnaires. Structural equation modeling is used to test the hypotheses. Findings Perceived vulnerability to disease, perceived severity of disease, response efficacy and self-efficacy positively affect attitude, thereby exerting a positive influence on the behavioral intention to use MHS. Subjective norms also influence users’ behavioral intention. Personal health status and personal health value have quasi-moderating effects on the relationship between attitude and behavioral intention. Originality/value This paper presents an early attempt to conceptualize and validate a research model of MHS acceptance by integrating TPB and PMT in a complementary manner. The integrated model provides a holistic view of people’s intention to use MHS by considering health threat beliefs, individual role (i.e. attitude and self-efficacy) and social influences (i.e. subjective norms). Furthermore, this research highlights the role of two individual health characteristics (i.e. personal health status and personal health value) in MHS adoption. These new findings are beneficial toward an in-depth understanding of technology adoption in the MHS context.
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks

The skip-connections used in residual networks have become a standard architecture choice in deep learning due to the increased generalization and stability of networks with this architecture, although there have been limited theoretical guarantees for this improved performance. In this work, we analyze overparameterized deep residual networks trained by gradient descent following random initialization, and demonstrate that (i) the class of networks learned by gradient descent constitutes a small subset of the entire neural network function class, and (ii) this subclass of networks is sufficiently large to guarantee small training error. By showing (i) we are able to demonstrate that deep residual networks trained with gradient descent have a small generalization gap between training and test error, and together with (ii) this guarantees that the test error will be small. Our optimization and generalization guarantees require overparameterization that is only logarithmic in the depth of the network, which helps explain why residual networks are preferable to fully connected ones.
Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog

We propose a semantic parser for parsing compositional utterances into Task Oriented Parse (TOP), a tree representation that has intents and slots as labels of nesting tree nodes. Our parser is span-based: it scores labels of the tree nodes covering each token span independently, but then decodes a valid tree globally. In contrast to previous sequence decoding approaches and other span-based parsers, we (1) improve the training speed by removing the need to run the decoder at training time; and (2) introduce edge scores, which model relations between parent and child labels, to mitigate the independence assumption between node labels and improve accuracy. Our best parser outperforms previous methods on the TOP dataset of mixed-domain task-oriented utterances in both accuracy and training speed.
Disturbance Observer-Based Fuzzy Adapted S-Surface Controller for Spatial Trajectory Tracking of Autonomous Underwater Vehicle

A new adaptive robust control scheme combining the disturbance-observer-based control (DOBC) with fuzzy adapted S-Surface control is proposed for trajectory tracking control of autonomous underwater vehicle. The main contribution of the proposed method is that control configuration does not require the bounds of uncertainty of the vehicle to be known and disturbances effect can be estimated and rejected. The proposed control law is mainly composed of three parts: a feed forward control along with disturbance estimator, S-Surface control and single input adaptive fuzzy proportional-integral (PI) compensator. The nominal feed forward controller specifies desired closed loop dynamics with extravagance from known preferred acceleration vector. Meanwhile, the disturbance observer and adaptive fuzzy PI control term to compensate the unknown effects that are disturbances and unmodeled dynamics. An additional term as S-Surface control assures fast convergence due to a nonlinear expression in to surface and also enhance stability of the underwater vehicle in oceanic environment. Moreover, the disturbance observer enhances the robustness performance of the adaptive fuzzy system for disturbances that cannot be modeled by fuzzy logic. The stability of closed loop controlled system is proven to be guaranteed according to Lyapunov theory. Finally, numerical simulation results illustrate the effectiveness and robustness of the proposed control method.
Multi-Stream Scale-Insensitive Convolutional and Recurrent Neural Networks for Liver Tumor Detection in Dynamic Ct Images

Convolutional neural networks (CNNs) have achieved great success in numerous challenging vision tasks, and have great potential for object detection in natural images. Compared with the natural images, medical images exhibit some unique characteristics. Therefore, substantial challenges still remain in this field. The first challenge is to develop a method for effectively distilling enhancement patterns from the dynamic CT images. Moreover, since tumor sizes vary greatly and small lesions are important for early liver tumor detection, lesion detection with a widely variable scale is another challenge. In this paper, we propose a multi-stream scale-insensitive convolutional and recurrent neural network (MSCR) for liver tumor detection. Specifically, we propose the use of grouped convolutional long short-term memory (GCLSTM) to extract enhancement patterns, which is developed as a plug-and-play module. Experiments show that the MSCR framework exhibits superior performance over state-of-the-art approaches, achieving an average precision of 77.06% for detection of focal liver lesions. We have released the code of MSCR in 1
Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond

Several important families of computational and statistical results in machine learning and randomized algorithms rely on uniform bounds on quadratic forms of random vectors or matrices. Such results include the Johnson-Lindenstrauss (J-L) Lemma, the Restricted Isometry Property (RIP), randomized sketching algorithms, and approximate linear algebra. The existing results critically depend on statistical independence, e.g., independent entries for random vectors, independent rows for random matrices, etc., which prevent their usage in dependent or adaptive modeling settings. In this paper, we show that such independence is in fact not needed for such results which continue to hold under fairly general dependence structures. In particular, we present uniform bounds on random quadratic forms of stochastic processes which are conditionally independent and sub-Gaussian given another (latent) process. Our setup allows general dependencies of the stochastic process on the history of the latent process and the latent process to be influenced by realizations of the stochastic process. The results are thus applicable to adaptive modeling settings and also allows for sequential design of random vectors and matrices. We also discuss stochastic process based forms of J-L, RIP, and sketching, to illustrate the generality of the results.
Controlling a Parent System Through the Abstraction of a Swarm Acting Upon It

Controlling a Parent System Through the Abstraction of a Swarm Acting Upon It This dissertation discusses the control of parent-child systems in which a parent system is acted on by a set of controllable child systems (i.e. a swarm). Using swarms of robots to accomplish complex tasks has several benefits over using a single robot; for example, members of the swarm can each focus on simpler sub-problems, allowing for less complex design, and if a swarm member breaks down, it may be easier to replace than a larger single robot. Many such complex tasks can be broken into this parent-child class of system. Examples of such systems include a swarm of robots pushing an object over a surface or a swarm of aerial vehicles carrying a large load. There are also many not-so-obvious problems that can be framed this way; for example, a multi-legged robot could be viewed as a swarm of legs manipulating a body, or an array of flaps on an aircraft wing could be considered a swarm controlling the flow over the wing. The first part of this dissertation outlines a general approach for decoupling the swarm from the parent system through a low-dimensional abstract state space. The conditions that enable this approach are given along with how constraints on both systems propagate through the abstract state and impact the requirements of their controllers. Next a method using Deep Learning techniques is proposed to learn such an abstract state, rather than design it by hand. This allows for broader applications in which an abstract state may not be obvious. Lastly, a method of estimating the abstract state in a decentralized fashion is presented, allowing the methods discussed previously to be implemented without global state observers. To demonstrate the techniques presented, we consider a representative problem consisting of a tilting plane with a swarm of robots driving on top. Both homogeneous and heterogeneous swarms of varying sizes, properties, and dynamics are used to demonstrate the modularity of these methods. The controllers are shown to be locally asymptotically
An Adaptive Empirical Bayesian Method for Sparse Deep Learning

We propose a novel adaptive empirical Bayesian (AEB) method for sparse deep learning, where the sparsity is ensured via a class of self-adaptive spike-and-slab priors. The proposed method works by alternatively sampling from an adaptive hierarchical posterior distribution using stochastic gradient Markov Chain Monte Carlo (MCMC) and smoothly optimizing the hyperparameters using stochastic approximation (SA). We further prove the convergence of the proposed method to the asymptotically correct distribution under mild conditions. Empirical applications of the proposed method lead to the state-of-the-art performance on MNIST and Fashion MNIST with shallow convolutional neural networks (CNN) and the state-of-the-art compression performance on CIFAR10 with Residual Networks. The proposed method also improves resistance to adversarial attacks.
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

Previous works (Donahue et al., 2018a; Engel et al., 2019a) have found that generating coherent raw audio waveforms with GANs is challenging. In this paper, we show that it is possible to train GANs reliably to generate high quality coherent waveforms by introducing a set of architectural changes and simple training techniques. Subjective evaluation metric (Mean Opinion Score, or MOS) shows the effectiveness of the proposed approach for high quality mel-spectrogram inversion. To establish the generality of the proposed techniques, we show qualitative results of our model in speech synthesis, music domain translation and unconditional music synthesis. We evaluate the various components of the model through ablation studies and suggest a set of guidelines to design general purpose discriminators and generators for conditional sequence synthesis tasks. Our model is non-autoregressive, fully convolutional, with significantly fewer parameters than competing models and generalizes to unseen speakers for mel-spectrogram inversion. Our pytorch implementation runs at more than 100x faster than realtime on GTX 1080Ti GPU and more than 2x faster than real-time on CPU, without any hardware specific optimization tricks.
SecureSurgiNET: A framework for ensuring security in telesurgery

The notion of surgical robotics is actively being extended to enable telesurgery, where both the surgeon and patient are remotely located and connected via a public network, which leads to many security risks. Being a safety-critical application, it is highly important to make telesurgery robust and secure against active and passive attacks. In this article, we propose the first complete framework, called SecureSurgiNET, for ensuring security in telesurgery environments. SecureSurgiNET is primarily based on a set of well-established protocols to provide a fool-proof telesurgical robotic system. For increasing the efficiency of secured telesurgery environments, the idea of a telesurgical authority is introduced that ensures the integrity, identity management, authentication policy implementation, and postoperative data security. An analysis is provided describing the security and throughput of Advanced Encryption Standard during the intraoperative phase of SecureSurgiNET. Moreover, we have tabulated the possible attacks on SecureSurgiNET along with the devised defensive measures. Finally, we also present a time complexity analysis of the SecureSurgiNET through simulations.
SOM-Based Direct Inverse Trajectory Control System for Double-Propeller Boat Maneuvers

This paper presents the development of neural-network-based control system using self-organizing-maps (SOMs) for the maneuvers of a double-propeller boat. The performance characteristics of the developed SOM controller system are compared with a widely-used supervised learning mechanism, the backpropagation neural network (BPNN) controller. The experimental results show that the proposed unsupervised SOM controller can control the boat model with very low error, although most artificial neural network (ANN)-based controllers are usually designed using supervised learning approaches. The important characteristic of the proposed SOM controller system is that it utilizes a mapping principle instead of an error calculation such as that in the BPNN controller system; consequently, the proposed SOM controller system is not very sensitive to non-ideal training data, which produces a low control error for the generated elliptical trajectory data. It is also confirmed in these experiments that when more mapping neurons are utilized in the SOM controller, a lower control error is achieved. It is expected that in a real implementation, the SOM controller could provide more robust control than the BPNN controller in handling small disturbances such as light winds and small waves.
Low-Complexity Compressed-Sensing-Based Watermark Cryptosystem and Circuits Implementation for Wireless Sensor Networks

The emerging compressed sensing (CS) technique provides lightweight data compression with zero-cost encryption. Therefore, CS enables reduced-complexity designs for sensor nodes and saves transmission power in wireless sensor networks (WSNs). However, CS’s linear encoding process makes it vulnerable to several attacks, which may lead to privacy leakage issues. In this article, leveraging the characteristic that CS reconstruction is sensitive to measurement noise, we propose a CS-based watermark cryptosystem for WSNs. In the front-end sensor, a low-dimension watermark is embedded in measurements. In the back-end solver, we present a CS-based watermark decryption/reconstruction engine for the Internet of Things (IoT) gateway. Without synchronization of the key, the proposed cryptosystem can resist ciphertext-only attack and known-plaintext attack effectively. Furthermore, leveraging watermarks as a digital signature, the proposed engine can detect denial of service attack effectively before signal reconstruction. For real-time signal processing, multiple-indices updating algorithm and VLSI architecture are applied to eliminate the throughput degradation from watermark removal. Finally, this CS decoder is fabricated in 40-nm CMOS, and it can support the simultaneous reconstruction of over 10 000 wireless sensors in real time while offering synchronization-free watermark decryption. Therefore, the proposed cryptosystem is suitable for the emerging IoT applications that need encryption strength with limited complexity.
Burnout Among Student Affairs Professionals at Metropolitan Universities

The purpose of this study was to determine the level of burnout among student affairs professionals at the 52 U.S. member institutions of the Coalition of Urban and Metropolitan Universities. Packets containing the Maslach Burnout Inventory (MBI), the Moos Work Environment Scale (WES), and a demographic survey were mailed to 371 senior student affairs administrators at the member institutions, with a completed response rate of 58.22%. The senior student affairs administrators surveyed included the chief student affairs officers and the professional staff who reported to them. The research design employed t-tests, analyses of variance, and Pearson's Product Moment correlations. The scores obtained from the MBI and WES subscales were compared overall and along 9 independent variablestitle of position, size of institution, appointment, salary, years in current position, years in profession, age, gender, and highest degree attained. Average levels of burnout were found on each of the MBI subscores. Contrary to earlier studies, women did not suffer from statistically significant higher levels of burnout than men, and burnout levels decreased with age and years in the profession for both sexes. Lower scores on the MBI depersonalization subscale were found in employees in mid-career and in professionals from smaller schools. Emotional exhaustion was not a factor. Environmental factors relating to burnout and job satisfaction were also explored. Statistically significant differences on the WES were found on all of the independent variables except the years in the current position variable. The metropolitan environment may have been effective in reducing the amount of burnout felt by this group of student affairs professionals. The study underscored the need for continuing research in burnout for student affairs professionals and for continued professional development throughout the career span.