Reviewerly/exHarmony huggingface dataset:
Voice-Based Monitoring and Control System of Electronic Appliance Using Dialog Flow API Via Google Assistant

The development of the control system, such as monitoring and control system using voice command, in the electronic device recently increased. One of the biggest and most comprehensive interface platforms was Google Assistant. There was a great opportunity in developing Google Assistant application due to its high support, its minimum size, and the minimal Google-based applications. The Dialog flow API (Application Programming Interface), an API that functioned to process humans' conversation based on artificial intelligent, fully support this possibility. Checking and control appliance through voice required a cell phone to lead the communication. The cell phone conveyed the sound to Google Assistant server through the web. Voice change into content happened in the server at that point proceeded into the Dialog flow API. This API prepared the content as per the discussion characterization or plan. The test outcomes demonstrating that creating electronic gadget application on checking and control dependent on voice had incredible potential. The accomplishment pace of talk affirmation structures is 75% of success rate.
How do Dancers Want to Use Interactive Technology?

There has been an increased interest in HCI research regarding the possibilities of interactive technology applied to the field of dance performance, particularly contemporary dance. This has produced numerous strategies to capture data from the moving bodies of the dancers and to map that data into different types of display formats. In this paper, we look at the role of interactive technology in dance performance from a broader perspective, aiming at understanding the needs of dancers and their relation with the audience. To this end, we ran a focus group with ten dancers with expertise in technology. We analysed the focus group using thematic analysis. We discuss the implications for design of our results by framing the role of technology in dance performance, proposing design guidelines related to the communication to the audience, use of technology, and mapping. Moreover, we propose different levels of ambiguity and appropriation related to the creators of the performance and the audience.
Capacity of Quantum Private Information Retrieval with Collusion of All But One of Servers

Quantum private information retrieval (QPIR) is the problem to retrieve one of f classical files by downloading quantum systems from non-communicating n servers each of which contains the copy of f files, while the identity of the retrieved file is unknown to each server. As an extension, we consider the (n-l)-private QPIR that the identity of the retrieved file is secret even if any n-l servers collude, and derive the QPIR capacity for this problem which is defined as the maximum rate of the retrieved file size over the download size. For an even number n of servers, we show that the capacity of the (n-1)-private QPIR is 2/n, when we assume that there are preexisting entanglements among the servers and require that no information of the non-retrieved files is downloaded. We construct an (n-1)-private QPIR protocol of rate $\lceil n/2\rceil^{-1}$ and prove that the capacity is upper bounded by $2/n$. The (n-1)-private QPIR capacity is strictly greater than the classical counterpart. The full version of this paper is accessible at: https:larxiv.orglpdfl1903.12556.
Audio-driven Talking Face Video Generation with Natural Head Pose

Real-world talking faces often accompany with natural head movement. However, most existing talking face video generation methods only consider facial animation with fixed head pose. In this paper, we address this problem by proposing a deep neural network model that takes an audio signal A of a source person and a very short video V of a target person as input, and outputs a synthesized high-quality talking face video with personalized head pose (making use of the visual information in V), expression and lip synchronization (by considering both A and V). The most challenging issue in our work is that natural poses often cause in-plane and out-of-plane head rotations, which makes synthesized talking face video far from realistic. To address this challenge, we reconstruct 3D face animation and re-render it into synthesized frames. To fine tune these frames into realistic ones with smooth background transition, we propose a novel memory-augmented GAN module. By first training a general mapping based on a publicly available dataset and fine-tuning the mapping using the input short video of target person, we develop an effective strategy that only requires a small number of frames (about 300 frames) to learn personalized talking behavior including head pose. Extensive experiments and two user studies show that our method can generate high-quality (i.e., personalized head movements, expressions and good lip synchronization) talking face videos, which are naturally looking with more distinguishing head movement effects than the state-of-the-art methods.
Auto-Encoder Variants for Solving Handwritten Digits Classification Problem

Muhammad Aamir, Nazri Mohd Nawi, Hairulnizam Bin Mahdin, Rashid Naseem, and Muhammad Zulqarnain. International Journal of Fuzzy Logic and Intelligent Systems 2020;20:8-16. https://doi.org/10.5391/IJFIS.2020.20.1.8
Simulation of Daily Diffuse Solar Radiation Based on Three Machine Learning Models

Solar radiation is an important parameter in the fields of computer modeling, engineering technology and energy development. This paper evaluated the ability of three machine learning models, i.e., Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Multivariate Adaptive Regression Splines (MARS), to estimate the daily diffuse solar radiation (Rd). The regular meteorological data of 1966-2015 at five stations in China were taken as the input parameters (including mean average temperature (Ta), theoretical sunshine duration (N), actual sunshine duration (n), daily average air relative humidity (RH), and extra-terrestrial solar radiation (Ra)). And their estimation accuracies were subjected to comparative analysis. The three models were first trained using meteorological data from 1966 to 2000. Then, the 2001-2015 data was used to test the trained machine learning model. The results show that the XGBoost had better accuracy than the other two models in coefficient of determination (R2 ), root mean square error (RMSE), mean bias error (MBE) and normalized root mean square error (NRMSE). The MARS performed better in the training phase than the testing phase, but became less accurate in the testing phase, with the R2 value falling by 2.7-16.9% on average. By contrast, the R2 values of SVM and XGBoost increased by 2.9-12.2% and 1.9-14.3%, respectively. Despite trailing slightly behind the SVM at the Beijing station, the XGBoost showed good performance at the rest of the stations in the two phases. In the training phase, the accuracy growth is small but observable. In addition, the XGBoost had a slightly lower RMSE than the SVM, a signal of its edge in stability. Therefore, the three machine learning models can estimate the daily Rd based on local inputs and the XGBoost stands out for its excellent performance and stability.
Low-Rank Hypergraph Hashing for Large-Scale Remote Sensing Image Retrieval

As remote sensing (RS) images increase dramatically, the demand for remote sensing image retrieval (RSIR) is growing, and has received more and more attention. The characteristics of RS images, e.g., large volume, diversity and high complexity, make RSIR more challenging in terms of speed and accuracy. To reduce the retrieval complexity of RSIR, a hashing technique has been widely used for RSIR, mapping high-dimensional data into a low-dimensional Hamming space while preserving the similarity structure of data. In order to improve hashing performance, we propose a new hash learning method, named low-rank hypergraph hashing (LHH), to accomplish for the large-scale RSIR task. First, LHH employs a l2-1 norm to constrain the projection matrix to reduce the noise and redundancy among features. In addition, low-rankness is also imposed on the projection matrix to exploit its global structure. Second, LHH uses hypergraphs to capture the high-order relationship among data, and is very suitable to explore the complex structure of RS images. Finally, an iterative algorithm is developed to generate high-quality hash codes and efficiently solve the proposed optimization problem with a theoretical convergence guarantee. Extensive experiments are conducted on three RS image datasets and one natural image dataset that are publicly available. The experimental results demonstrate that the proposed LHH outperforms the existing hashing learning in RSIR tasks.
Developing a composite index for intrapreneurial orientation in small and medium-sized enterprises<scp>:</scp> A comprehensive dual methodology

Intrapreneurship has long been recognised as a way to develop competitive advantages. This is particularly evident in small and medium-sized enterprises (SMEs), so the intrapreneurial orientation (IO) of these companies should be evaluated from multiple perspectives. Research on this topic involves a wide variety of elements that not only have a direct impact on intrapreneurial behaviour but also change over time. Nonetheless, the development of a holistic system for evaluating IO in SMEs appears to be of great interest to academics and practitioners. Based on the epistemological convictions embodied in multiple criteria decision analysis (MCDA), this study sought to combine cognitive mapping techniques and the Choquet integral (CI) to develop a new IO evaluation system for SMEs. To this end, a panel of experienced decision makers with expertise in the subject under analysis was recruited. The results of a practical application of the proposed evaluation system and composite index were validated both by the panel members and a board member of the Entrepreneurship and Innovation Department of the Portuguese Institute for the Support of Small and Medium-Sized Enterprises and Innovation (in Portuguese, Instituto de Apoio às Pequenas e Médias Empresas e à Inovação (IAPMEI)). The contributions and limitations of the proposed dual methodology are also discussed.
Medical Image Segmentation Algorithm Based on Optimized Convolutional Neural Network-Adaptive Dropout Depth Calculation

Medical image segmentation is a key technology for image guidance. Therefore, the advantages and disadvantages of image segmentation play an important role in image-guided surgery. Traditional machine learning methods have achieved certain beneficial effects in medical image segmentation, but they have problems such as low classification accuracy and poor robustness. Deep learning theory has good generalizability and feature extraction ability, which provides a new idea for solving medical image segmentation problems. However, deep learning has problems in terms of its application to medical image segmentation: one is that the deep learning network structure cannot be constructed according to medical image characteristics; the other is that the generalizability y of the deep learning model is weak. To address these issues, this paper first adapts a neural network to medical image features by adding cross-layer connections to a traditional convolutional neural network. In addition, an optimized convolutional neural network model is established. The optimized convolutional neural network model can segment medical images using the features of two scales simultaneously. At the same time, to solve the generalizability problem of the deep learning model, an adaptive distribution function is designed according to the position of the hidden layer, and then the activation probability of each layer of neurons is set. This enhances the generalizability of the dropout model, and an adaptive dropout model is proposed. This model better addresses the problem of the weak generalizability of deep learning models. Based on the above ideas, this paper proposes a medical image segmentation algorithm based on an optimized convolutional neural network with adaptive dropout depth calculation. An ultrasonic tomographic image and lumbar CT medical image were separately segmented by the method of this paper. The experimental results show that not only are the segmentation effects of the proposed method improved compared with those of the traditional machine learning and other deep learning methods but also the method has a high adaptive segmentation ability for various medical images. The research work in this paper provides a new perspective for research on medical image segmentation.
Development and Testing of a Single-Axis Photovoltaic Sun Tracker through the Internet of Things

Solar tracking systems enable increased efficiency of a photovoltaic system through a continuous adjustment of its position with respect to the sun, thus increasing the generation of electrical energy. The integration of photovoltaic solar tracking systems in buildings and houses enables the energy needs of users in a broader way to be covered. This integration is facilitated through the existence of technologies such as access to the Internet through Wi-Fi, which allows developing systems to be encompassed within the domain of the “Internet of Things” (IoT). In this study, a first-of-its-kind “open-loop” solar tracker was designed and developed, which executes the tracking algorithm in the Firebase web service and allows the exchange of data with said service through a NodeMCU development board, which has an integrated Wi-Fi module. After an experimental campaign in Aguascalientes, central Mexico, gains in terms of collected energy on average were measured at 29.9% in May compared to an optimally tilted fixed photovoltaic system. This study opens the possibility of integrating power generation systems into the IoT domain, which, among other things, allows for constant monitoring of the behavior of the system.
Threat modeling

Threat modeling involves the systematic identification and analysis of security threats in the context of a specific system. This paper starts from an assessment of its current state of practice, based on interactions with threat modeling professionals. We argue that threat modeling is still at a low level of maturity and identify the main criteria for successful adoption in practice. Furthermore, we identify a set of key research challenges for aligning threat modeling research to industry practice, thereby raising the technology-readiness levels of the ensuing solutions, approaches, and tools.
A Novel Singular Value Decomposition-Based Denoising Method in 4-Dimensional Computed Tomography of the Brain in Stroke Patients with Statistical Evaluation

Computed tomography (CT) is a widely used medical imaging modality for diagnosing various diseases. Among CT techniques, 4-dimensional CT perfusion (4D-CTP) of the brain is established in most centers for diagnosing strokes and is considered the gold standard for hyperacute stroke diagnosis. However, because the detrimental effects of high radiation doses from 4D-CTP may cause serious health risks in stroke survivors, our research team aimed to introduce a novel image-processing technique. Our singular value decomposition (SVD)-based image-processing technique can improve image quality, first, by separating several image components using SVD and, second, by reconstructing signal component images to remove noise, thereby improving image quality. For the demonstration in this study, 20 4D-CTP dynamic images of suspected acute stroke patients were collected. Both the images that were and were not processed via the proposed method were compared. Each acquired image was objectively evaluated using contrast-to-noise and signal-to-noise ratios. The scores of the parameters assessed for the qualitative evaluation of image quality improved to an excellent rating (p &lt; 0.05). Therefore, our SVD-based image-denoising technique improved the diagnostic value of images by improving their quality. The denoising technique and statistical evaluation can be utilized in various clinical applications to provide advanced medical services.
Towards Automating the Synthesis of Chatbots for Conversational Model Query

Conversational interfaces (also called chatbots) are being increasingly adopted in various domains such as e-commerce or customer service, as a direct communication channel between companies and end-users. Their advantage is that they can be embedded within social networks, and provide a natural language (NL) interface that enables their use by non-technical users. While there are many emerging platforms for building chatbots, their construction remains a highly technical, challenging task. In this paper, we propose the use of chatbots to facilitate querying domain-specific models. This way, instead of relying on technical query languages (e.g., OCL), models are queried using NL as this can be more suitable for non-technical users. To avoid manual programming, our solution is based on the automatic synthesis of the model query chatbots from a domain meta-model. These chatbots communicate with an EMF-based modelling backend using the Xatkit framework.
Infobox-to-text Generation with Tree-like Planning based Attention Network

We study the problem of infobox-to-text generation that aims to generate a textual description from a key-value table. Representing the input infobox as a sequence, previous neural methods using end-to-end models without order-planning suffer from the problems of incoherence and inadaptability to disordered input. Recent planning-based models only implement static order-planning to guide the generation, which may cause error propagation between planning and generation. To address these issues, we propose a Tree-like PLanning based Attention Network (Tree-PLAN) which leverages both static order-planning and dynamic tuning to guide the generation. A novel tree-like tuning encoder is designed to dynamically tune the static order-plan for better planning by merging the most relevant attributes together layer by layer. Experiments conducted on two datasets show that our model outperforms previous methods on both automatic and human evaluation, and demonstrate that our model has better adaptability to disordered input.
Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization

Stochastic gradient methods with momentum are widely used in applications and at the core of optimization subroutines in many popular machine learning libraries. However, their sample complexities have not been obtained for problems beyond those that are convex or smooth. This paper establishes the convergence rate of a stochastic subgradient method with a momentum term of Polyak type for a broad class of non-smooth, non-convex, and constrained optimization problems. Our key innovation is the construction of a special Lyapunov function for which the proven complexity can be achieved without any tuning of the momentum parameter. For smooth problems, we extend the known complexity bound to the constrained case and demonstrate how the unconstrained case can be analyzed under weaker assumptions than the state-of-the-art. Numerical results confirm our theoretical developments.
Provable Representation Learning for Imitation Learning via Bi-level Optimization

A common strategy in modern learning systems is to learn a representation that is useful for many tasks, a.k.a. representation learning. We study this strategy in the imitation learning setting for Markov decision processes (MDPs) where multiple experts' trajectories are available. We formulate representation learning as a bi-level optimization problem where the outer optimization tries to learn the joint representation and the inner optimization encodes the imitation learning setup and tries to learn task-specific parameters. We instantiate this framework for the imitation learning settings of behavior cloning and observation-alone. Theoretically, we show using our framework that representation learning can provide sample complexity benefits for imitation learning in both settings. We also provide proof-of-concept experiments to verify our theory.
Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems.

Differentially private gradient descent (DP-GD) has been extremely effective both theoretically, and in practice, for solving private empirical risk minimization (ERM) problems. In this paper, we focus on understanding the impact of the clipping norm, a critical component of DP-GD, on its convergence. We provide the first formal convergence analysis of clipped DP-GD. More generally, we show that the value which one sets for clipping really matters: done wrong, it can dramatically affect the resulting quality; done properly, it can eliminate the dependence of convergence on the model dimensionality. We do this by showing a dichotomous behavior of the clipping norm. First, we show that if the clipping norm is set smaller than the optimal, even by a constant factor, the excess empirical risk for convex ERMs can increase from $O(1/n)$ to $\Omega(1)$, where $n$ is the number of data samples. Next, we show that, regardless of the value of the clipping norm, clipped DP-GD minimizes a well-defined convex objective over an unconstrained space, as long as the underlying ERM is a generalized linear problem. Furthermore, if the clipping norm is set within at most a constant factor higher than the optimal, then one can obtain an excess empirical risk guarantee that is independent of the dimensionality of the model space. Finally, we extend our result to non-convex generalized linear problems by showing that DP-GD reaches a first-order stationary point as long as the loss is smooth, and the convergence is independent of the dimensionality of the model space.
POPCORN: Partially Observed Prediction Constrained Reinforcement Learning.

Many medical decision-making tasks can be framed as partially observed Markov decision processes (POMDPs). However, prevailing two-stage approaches that first learn a POMDP and then solve it often fail because the model that best fits the data may not be well suited for planning. We introduce a new optimization objective that (a) produces both high-performing policies and high-quality generative models, even when some observations are irrelevant for planning, and (b) does so in batch off-policy settings that are typical in healthcare, when only retrospective data is available. We demonstrate our approach on synthetic examples and a challenging medical decision-making problem.