Reviewerly/exHarmony huggingface dataset:
SpeakingEyes: Enabling Paralyzed People to Communicate

SpeakingEyes is a Human-Computer Interaction (HCI) based desktop application. It enables people with paralysis, locked-in syndrome, communication disorders, Lou Gehrig’s disease, spinal cord injuries, neurological disorders and related disabilities, who are unable to perform the voluntary movement of muscles, to express themselves and perform essential activities independently via eye gaze. These include communicating with people using onscreen keyboard, requesting various services such as food, water, natural calls, phone calls, asking for assistance, raising alarm during an emergency, turning on/off switches for lights, fan, and other home appliances. The eye gaze is determined by performing image processing on image of user’s eyes. Web-camera/external camera is used to acquire image of the user's eyes. After preprocessing it is fed into Convolutional Neural Network (CNN) which is used as a classifier to predict eye gaze. This CNN was trained on a custom dataset and gives an overall accuracy of 98.89% with an average processing time of 0.6 seconds per frame on Intel i5 CPU. After the prediction, the subsequent action is taken by the computer.
Ux Design of Artificial Intelligence News Robot

Abstract Background: Artificial Intelligence and 5G are coming with an unstoppable tide. As a representative of traditional industries, the news industry is transitioning from labor-intensive to intellectual-intensive by using the advanced AI technologies. Product Design: In order to help news industry to catch up with the rest of high-tech world, a UX designer’s job is to make the interaction between users and AI useable and useful. By understanding journalists’ pain points and what AI can do to help, UX designers found out the best landing scenarios and designed 25 intelligent media robots. With the robots’ help, reporters can collect and process news resources faster and better. Conclusions: This study proves that developing intelligent news robots among journalists by using a theory-based, evidence-driven, and user-centered approach has the potential benefits of low cost, high accessibility, and good adherence. The productivity of journalists has been greatly improved with intelligent media robots. Intelligent news robots have advantages such as fast producing speed, high efficiency, low cost, high accuracy and strong news digging ability. However, it also has limitations such as limited reporting fields, poor readability and lack of depth.
Mobile Devices in Education

This chapter reviewed the importance of using ICT in the educational process with the example of mobile devices. Mobile devices as teaching tools are becoming a more and more common part of the educational process in classrooms. Every day the share of mobile Internet and mobile device users are increasing. The level of introduction of mobile devices into the educational environment at the leading universities worldwide is quite high. In Kazakhstan, this system is spread slightly. However, the University of International Business implemented the application for learning the English language which is called “The UIB English.” It is obvious that the use of mobile learning in the educational process requires organizational, research and methodological work. Universities should create and implement projects to operate the mobile version of the portal; identify and develop the most popular mobile services integrated with the information system of the university; develop and implement a mobile security policy; and create its own applications.
Visualizing Feature-Level Evolution in Product Lines: A Research Preview

[Context and motivation] Software product lines evolve frequently to address customer requirements in different domains. This leads to a distributed engineering process with frequent updates and extensions. [Question/problem] However, such changes are typically managed and tracked at the level of source code while feature-level awareness about software evolution is commonly lacking. In this research preview paper we thus present an approach visualizing the evolution in software product lines at the level of features. [Principal ideas/results] Specifically, we extend feature models with feature evolution plots to visualize changes at a higher level. Our approach uses static code analyses and a variation control system to compute the evolution data for visualisation. As a preliminary evaluation we report selected examples of applying our approach to a cyberphysical ecosystem from the field of industrial automation. [Contribution] Integrating visualisations into state-of-the-art feature models can contribute to better integrate requirements-level and code-level perspectives during product line evolution.
Prior Knowledge Driven Label Embedding for Slot Filling in Natural Language Understanding

Traditional slot filling in natural language understanding (NLU) predicts a one-hot vector for each word. This form of label representation lacks semantic correlation modelling, which leads to severe data sparsity problem, especially when adapting an NLU model to a new domain. To address this issue, a novel label embedding based slot filling framework is proposed in this paper. Here, distributed label embedding is constructed for each slot using prior knowledge. Three encoding methods are investigated to incorporate different kinds of prior knowledge about slots: atomic concepts, slot descriptions, and slot exemplars. The proposed label embeddings tend to share text patterns and reuses data with different slot labels. This makes it useful for adaptive NLU with limited data. Also, since label embedding is independent of NLU model, it is compatible with almost all deep learning based slot filling models. The proposed approaches are evaluated on three datasets. Experiments on single domain and domain adaptation tasks show that label embedding achieves significant performance improvement over traditional one-hot label representation as well as advanced zero-shot approaches.
Recommending Curated Content Using Implicit Feedback

Matrix factorization (MF) which is a Collaborative filtering (CF) based model, is widely used in the recommendation systems (RS). For our experiment, we collected data from a company's internal web site where curated contents are published and pushed to the employees. However, the size of the dataset is small and interaction data is also limited. We got a sparse matrix when we generated a user-item rating matrix. We have used Multi-Layer Perceptron (MLP) to calculate the rating scores from the implicit feedbacks. However, on this sparse dataset traditional content only or CF-only RSs do not work well. Here, we propose ahybrid RS that incorporates content similarity scores into an MLP-based MF-model. To integrate the content similarity scores into the MF, we have defined an objective function based on a regularization term. The experimental result shows that our proposed model demonstrates a better result than the traditional MF-based models.
Phase transition for parameter learning of hidden Markov models

We study a phase transition in parameter learning of hidden Markov models (HMMs). We do this by generating sequences of observed symbols from given discrete HMMs with uniformly distributed transition probabilities and a noise level encoded in the output probabilities. We apply the Baum-Welch (BW) algorithm, an expectation-maximization algorithm from the field of machine learning. By using the BW algorithm we then try to estimate the parameters of each investigated realization of an HMM. We study HMMs with n=4,8, and 16 states. By changing the amount of accessible learning data and the noise level, we observe a phase-transition-like change in the performance of the learning algorithm. For bigger HMMs and more learning data, the learning behavior improves tremendously below a certain threshold in the noise strength. For a noise level above the threshold, learning is not possible. Furthermore, we use an overlap parameter applied to the results of a maximum a posteriori (Viterbi) algorithm to investigate the accuracy of the hidden state estimation around the phase transition.
Application of Hand Recognition System Based on Electromyography and Gyroscope Using Deep Learning

With the advent of new technological, the interaction between people and computers has become more and more inseparable. The human-computer interaction (HCI) technology improves the operations in people's lives, and the gesture recognition is the basic operation and is one of the hot research topics. In this paper, we developed a virtual theater system that using gesture to control the changes between different scene using the Myo armband. The proposed system used a deep learning method to classify dynamic gestures, and then send instructions to the virtual theater. By using this system, users can easily control the scenes and objects in the theater developed by Unity and make the virtual stage more enriched during the performance.
Analyzing the impact of power subsystem failures and checkpoint mechanisms on availability of cloud applications

The widespread adoption of cloud computing has been driven by technological advantages, such as pay-as-you-go plan, scalability and high availability. From cloud service providers point-of-view, managing all these high customer expectations is a growing and critical challenge. This paper examines the relationship among energy infrastructure architectures, checkpoint mechanisms, and availability of software applications in a cloud data center. We consider four power architectures, three checkpoint mechanisms (cold, warm, and hot) with different sizes of files, and two applications (Video on Demand and digital library). For that, we propose a set of stochastic models (based on Petri Net and Reliability Block Diagram) and perform sensitivity and availability analysis. From results, we note that the power architecture has a great impact on application availability while the checkpoint mechanism only impacts with the application has a low mean time to failure value.
BAREB: A Bayesian repulsive biclustering model for periodontal data

Summary Preventing periodontal diseases (PD) and maintaining the structure and function of teeth are important goals for personal oral care. To understand the heterogeneity in patients with diverse PD patterns, we develop a Bayesian repulsive biclustering method that can simultaneously cluster the PD patients and their tooth sites after taking the patient‐ and site‐level covariates into consideration. BAREB uses the determinantal point process prior to induce diversity among different biclusters to facilitate parsimony and interpretability. Since PD progression is hypothesized to be spatially referenced, BAREB factors in the spatial dependence among tooth sites. In addition, since PD is the leading cause for tooth loss, the missing data mechanism is nonignorable. Such nonrandom missingness is incorporated into BAREB. For the posterior inference, we design an efficient reversible jump Markov chain Monte Carlo sampler. Simulation studies show that BAREB is able to accurately estimate the biclusters, and compares favorably to alternatives. For real world application, we apply BAREB to a dataset from a clinical PD study, and obtain desirable and interpretable results. A major contribution of this article is the Rcpp implementation of our methodology, available in the R package BAREB .
An Architecture of a Two-Layer Cloud-Based Web System Using a Fuzzy-Neural Request Distribution

Nowadays, a significant part of human activity is supported by information systems, especially Web systems, hosted in the Web clouds. The architectures of Web cloud systems are in most cases complex. In this paper, we try to determine which cloud configuration in double-layer architecture is the best when using the fuzzy-neural intelligent method of request distribution. We present the distribution strategy, a testbed and the results of the conducted experiments. At the end of this paper, we discuss results and present final conclusions.
Comparative Analysis of Gender Identification using Speech Analysis and Higher Order Statistics

Gender identification via speech processing is one of the hot research topics among the security research community. Many cyber systems are being developed to recognize human speech type. These systems mainly comprise of a feature segment process which extracts and selects the features of human speeches. Feature extraction and feature selection are the most noteworthy phase of speech recognition involving numerous strategies. The purpose of this paper is to investigate the potential effectiveness of spectral analysis and higher-order statistics performed over the speech segments of different genders. Spectral analysis is done via spectral descriptors consisting of varied parameters which are widely used in machine learning applications. The varied gender speeches are distinguished by means of parameters, i.e., higher order statistics, like spectral centroid, spectral entropy, spectral kurtosis, spectral slope and spectral flatness. The results obtained show successful discrimination of male and female speeches based on the peakiness of speech, voiced and unvoiced and higher and lower formants.
Cluster analysis of deep embeddings in real-time strategy games

Given that many readily available datasets consist of large amounts of unlabeled data,1 unsupervised learning methods are an important component of many data-driven applications. In many instances, ground-state truth labels may be unavailable or obtainable only at a costly expense. As a result, there is an acute need for the ability to understand and interpret unlabeled datasets as thoroughly as possible. In this article, we examine the effectiveness of learned deep embeddings via internal clustering metrics on a dataset comprised of unlabelled StarCraft 2 game replays. The results of this work indicate that the use of deep embeddings provides a promising basis for clustering and interpreting player behavior in complex game domains.
Subspace stabilization analysis for a class of non-Markovian open quantum systems

Studied in this article is non-Markovian open quantum systems parametrized by Hamiltonian $H$, coupling operator $L$, and memory kernel function $\ensuremath{\gamma}$, which is a proper candidate for describing the dynamics of various solid-state quantum information processing devices. We look into the subspace stabilization problem of the system from the perspective of dynamical systems and control. The problem translates itself into finding analytic conditions that characterize invariant and attractive subspaces. Necessary and sufficient conditions are found for subspace invariance based on algebraic computations, and sufficient conditions are derived for subspace attractivity by applying a double integral Lyapunov functional. Mathematical proof is given for those conditions and a numerical example is provided to illustrate the theoretical result.
A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization

Manifold regularization is a commonly used technique in semi-supervised learning. It enforces the classification rule to be smooth with respect to the data-manifold. Here, we derive sample complexity bounds based on pseudo-dimension for models that add a convex data dependent regularization term to a supervised learning process, as is in particular done in Manifold regularization. We then compare the bound for those semi-supervised methods to purely supervised methods, and discuss a setting in which the semi-supervised method can only have a constant improvement, ignoring logarithmic terms. By viewing Manifold regularization as a kernel method we then derive Rademacher bounds which allow for a distribution dependent analysis. Finally we illustrate that these bounds may be useful for choosing an appropriate manifold regularization parameter in situations with very sparsely labeled data.
Online Multilingual Plagiarism Detection System Using Multi Search Engines

Using someone else's work or ideas without attribution is plagiarism, whether you meant to do it or not. Unintended plagiarism of snippet of text can have serious consequences and be a serious form of ethical misconduct. The current system is a web application that enables you to check a multilingual text, with special focus on Arabic, for duplicate contents on the World Wide Web. In this system, you can simply input or paste your text through the online system and for each sentence in the text it will go through three popular search engines: Google, Bing, and Yandex SERP and try to find the top three results on the first page for each search engine where duplicate contents already exist. This system is getting data from the three-search engines custom search APIs. Then, the system uses a text similarity technique between the suspicious sentence and the retrieved text snippet for all nine results. The result is the one that gives the highest similarity rate. The results were encouraging and will open doors for new and innovative techniques for researchers in this field.
Gadget3 on GPUs with OpenACC

We present preliminary results of a GPU porting of all main Gadget3 modules (gravity computation, SPH density computation, SPH hydrodynamic force, and thermal conduction) using OpenACC directives. Here we assign one GPU to each MPI rank and exploit both the host and accellerator capabilities by overlapping computations on the CPUs and GPUs: while GPUs asynchronously compute interactions between particles within their MPI ranks, CPUs perform tree-walks and MPI communications of neighbouring particles. We profile various portions of the code to understand the origin of our speedup, where we find that a peak speedup is not achieved because of time-steps with few active particles. We run a hydrodynamic cosmological simulation from the Magneticum project, with $2\cdot10^{7}$ particles, where we find a final total speedup of $\approx 2.$ We also present the results of an encouraging scaling test of a preliminary gravity-only OpenACC porting, run in the context of the EuroHack17 event, where the prototype of the porting proved to keep a constant speedup up to $1024$ GPUs.
Actionable knowledge discovery from social networks using causal structures of structural features

Knowledge discovery and data mining provide an array of solutions for real-world problems. When facing business requirements, the ultimate goal of knowledge discovery is not the knowledge itself but rather making the gained knowledge practical. Consequently, the models and patterns found by the min ing methods often require post-processing. To this end, actionable knowledge discovery has been introduced which is developed to extract actionable knowledge from data. The output of actionable knowledge discovery is a set of actions that help the domain expert to gain the desired outcome. Such a process where a set of actions are extracted is called action extraction. One of the challenges of action extraction is to incorporate causal dependencies among the variables to find actions with higher effectiveness compared to when no such dependencies are used. The goal of this paper is to dive into the lesser studied subject of “action discovery in social networks” and intends to extract actions by utilizing the casual structures discovered from such data. Furthermore, in order to capture the underlying information within a social network, we extract the corresponding structural features. We propose a method called SF-ICE-CREAM (Social Features included Inductive Causation Enabled Causal Relationship-based Economical Action Mining) to overcome the challenges introduced above. This method uses structural features to find the underlying causal structures within a social network and incorporates them into the action extraction process.
Deep Ordinal Reinforcement Learning

Reinforcement learning usually makes use of numerical rewards, which have nice properties but also come with drawbacks and difficulties. Using rewards on an ordinal scale (ordinal rewards) is an alternative to numerical rewards that has received more attention in recent years. In this paper, a general approach to adapting reinforcement learning problems to the use of ordinal rewards is presented and motivated. We show how to convert common reinforcement learning algorithms to an ordinal variation by the example of Q-learning and introduce Ordinal Deep Q-Networks, which adapt deep reinforcement learning to ordinal rewards. Additionally, we run evaluations on problems provided by the OpenAI Gym framework, showing that our ordinal variants exhibit a performance that is comparable to the numerical variations for a number of problems. We also give first evidence that our ordinal variant is able to produce better results for problems with less engineered and simpler-to-design reward signals.
Content-Sensitive Superpixel Generation with Boundary Adjustment

Superpixel segmentation has become a crucial tool in many image processing and computer vision applications. In this paper, a novel content-sensitive superpixel generation algorithm with boundary adjustment is proposed. First, the image local entropy was used to measure the amount of information in the image, and the amount of information was evenly distributed to each seed. It placed more seeds to achieve the lower under-segmentation in content-dense regions, and placed the fewer seeds to increase computational efficiency in content-sparse regions. Second, the Prim algorithm was adopted to generate uniform superpixels efficiently. Third, a boundary adjustment strategy with the adaptive distance further optimized the superpixels to improve the performance of the superpixel. Experimental results on the Berkeley Segmentation Database show that our method outperforms competing methods under evaluation metrics.
On using electronic health records to improve optimal treatment rules in randomized trials

Individualized treatment rules (ITRs) tailor medical treatments according to patient-specific characteristics in order to optimize patient outcomes. Data from randomized controlled trials (RCTs) are used to infer valid ITRs using statistical and machine learning methods. However, RCTs are usually conducted under specific inclusion/exclusion criteria, thus limiting their generalizability to a broader patient population in real-world practice settings. Because electronic health records (EHRs) document treatment prescriptions in the real world, transferring information in EHRs to RCTs, if done appropriately, could potentially improve the performance of ITRs, in terms of precision and generalizability. In this work, we propose a new domain adaptation method to learn ITRs by incorporating information from EHRs. Unless we assume that there is no unmeasured confounding in EHRs, we cannot directly learn the optimal ITR from the combined EHR and RCT data. Instead, we first pretrain "super" features from EHRs that summarize physician treatment decisions and patient observed benefits in the real world, as these are likely to be informative of the optimal ITRs. We then augment the feature space of the RCT and learn the optimal ITRs by stratifying by super features using subjects enrolled in RCT. We adopt Q-learning and a modified matched-learning algorithm for estimation. We present heuristic justification of our method and conduct simulation studies to demonstrate the performance of super features. Finally, we apply our method to transfer information learned from EHRs of patients with type 2 diabetes to learn individualized insulin therapies from RCT data.