Reviewerly/exHarmony huggingface dataset:
Ensemble Self-Training for Low-Resource Languages: Grapheme-to-Phoneme Conversion and Morphological Inflection

We present an iterative data augmentation framework, which trains and searches for an optimal ensemble and simultaneously annotates new training data in a self-training style.We apply this framework on two SIG-MORPHON 2020 shared tasks: graphemeto-phoneme conversion and morphological inflection.With very simple base models in the ensemble, we rank the first and the fourth in these two tasks.We show in the analysis that our system works especially well on lowresource languages.
Experimental Verification of Objective Visual Fatigue Measurement Based on Accurate Pupil Detection of Infrared Eye Image and Multi-Feature Analysis

As the use of electronic displays increases rapidly, visual fatigue problems are also increasing. The subjective evaluation methods used for visual fatigue measurement have individual difference problems, while objective methods based on bio-signal measurement have problems regarding motion artifacts. Conventional eye image analysis-based visual fatigue measurement methods do not accurately characterize the complex changes in the appearance of the eye. To solve this problem, in this paper, an objective visual fatigue measurement method based on infrared eye image analysis is proposed. For accurate pupil detection, a convolutional neural network-based semantic segmentation method was used. Three features are calculated based on the pupil detection results: (1) pupil accommodation speed, (2) blink frequency, and (3) eye-closed duration. In order to verify the calculated features, differences in fatigue caused by changes in content color components such as gamma, color temperature, and brightness were compared with a reference video. The pupil detection accuracy was confirmed to be 96.63% based on the mean intersection over union. In addition, it was confirmed that all three features showed significant differences from the reference group; thus, it was verified that the proposed analysis method can be used for the objective measurement of visual fatigue.
The impact of career planning and career satisfaction on employee's turnover intention

This study aims to examine the impact of career planning and career satisfaction on employees' turnover intention and the mediating role of career satisfaction from the perspective of banks employees' in the Jordanian capital Amman.The survey questionnaire was gathered from 412 employees located in 25 banks in the Jordanian capital Amman.The study used the software SmartPLS (version 3.2.8) to test the study hypotheses.The findings indicate that career planning and career satisfaction negatively impact employees' turnover intention, and career satisfaction partially mediated the relationship between career planning and employees' turnover intention.Additionally, this study aims to evaluate previous items that were developed by Gould (1979) to measure career planning, using focus group interview for six managers of the human resource department in the Jordanian banks' sector.According to the results of the interview, some items were reformulated and three new items were created.Future studies may include expansion of this model by adding variables related to organizational behaviours such as individual career management or career adaptation.
Stochastic DCA for minimizing a large sum of DC functions with application to multi-class logistic regression

We consider the large sum of DC (Difference of Convex) functions minimization problem which appear in several different areas, especially in stochastic optimization and machine learning. Two DCA (DC Algorithm) based algorithms are proposed: stochastic DCA and inexact stochastic DCA. We prove that the convergence of both algorithms to a critical point is guaranteed with probability one. Furthermore, we develop our stochastic DCA for solving an important problem in multi-task learning, namely group variables selection in multi class logistic regression. The corresponding stochastic DCA is very inexpensive, all computations are explicit. Numerical experiments on several benchmark datasets and synthetic datasets illustrate the efficiency of our algorithms and their superiority over existing methods, with respect to classification accuracy, sparsity of solution as well as running time.
Multi-area economic dispatch with stochastic wind power using Salp Swarm Algorithm

Salp Swarm Algorithm (SSA) is a newly developed swarm intelligence based optimization method. Its analytical model mimics self-organized swarming and foraging behavior of salps. Salps are found in the ocean and they form a swarm called salp chain for their organized movement and foraging. Since its inception in 2017, SSA has been applied to many engineering domains. This paper presents the solution of complex constrained multi-area economic dispatch problem of power system with and without stochastic wind integration using SSA. Simulation analysis has been carried on four different constrained test cases with diverse dimensions/complexity level. They are (i) four area system with sixteen thermal units (ii) four area system with forty thermal units (iii) two area system with forty thermal units and (iv) two area forty-unit system with wind integration. Comparison of simulation results obtained by SSA with other methods reported in the recent literature has proven SSA’s capability of constrained handling and offered promising and efficient solutions.
Color standardization and optimization in whole slide imaging

Whole slide imaging (WSI) has various uses, including the development of decision support systems, image analysis, education, conferences, and remote diagnostics. It is also used to develop artificial intelligence using machine learning methods. In the clinical setting, however, many issues have hindered the implementation of WSI. These issues are becoming more important as WSI is gaining wider use in clinical practice, particularly with the implementation of artificial intelligence in pathological diagnosis. One of the most important issues is the standardization of color for WSI, which is an important component of digital pathology. In this paper, we review the major factors of color variation and how to evaluate and modify color variation to establish color standardization. There are five major reasons for color variation, which include specimen thickness, staining, scanner, viewer, and display. Recognizing that the color is not standardized is the first step towards standardization, and it is difficult to ascertain whether the appropriate color of the WSI is displayed at the reviewers' end.
CompLex --- A New Corpus for Lexical Complexity Predicition from Likert Scale Data

Predicting which words are considered hard to understand for a given target population is a vital step in many NLP applications such astext simplification. This task is commonly referred to as Complex Word Identification (CWI). With a few exceptions, previous studieshave approached the task as a binary classification task in which systems predict a complexity value (complex vs. non-complex) fora set of target words in a text. This choice is motivated by the fact that all CWI datasets compiled so far have been annotated using abinary annotation scheme. Our paper addresses this limitation by presenting the first English dataset for continuous lexical complexityprediction. We use a 5-point Likert scale scheme to annotate complex words in texts from three sources/domains: the Bible, Europarl,and biomedical texts. This resulted in a corpus of 9,476 sentences each annotated by around 7 annotators.
Developments for Smart Classrooms

This paper presents results of a focus group that gathered data from principals of several public schools in Catalonia regarding the educational impact of Industry 4.0 technologies. Conclusions and recommendations are drawn from qualitative analysis with a view to improving smart classrooms via educational innovation. Results show that the schools see development and implementation of technology leveraging Industry 4.0 as necessary for future classrooms, always based on knowledge and pedagogical principles. Furniture, lighting, acoustics, and air quality are parameters that need to be adjusted in a smart classroom. Technologies such as internet of things, artificial intelligence, and big data make it possible to automate these adjustments, with a risk that the economic cost of such developments could create a gap between schools. Further research in other contexts or countries could report slightly different results. Nevertheless, this paper offers a qualified perspective from school principals about what industry should work on in order to develop smart classrooms, and why.
MOTION – A Framework for Mixed-Protocol Multi-Party Computation

We present MOTION, an efficient and generic open-source framework for mixed-protocol secure multi-party computation (MPC) . MOTION is built in a user-friendly, modular, and extensible way, intended to be used as a tool in MPC research and to increase adoption of MPC protocols in practice. Our framework incorporates several important engineering decisions such as full communication serialization, which enables MPC over arbitrary messaging interfaces and removes the need of owning network sockets. MOTION also incorporates several performance optimizations that improve the communication complexity and latency, e.g., \( 2\times \) better online round complexity of precomputed correlated Oblivious Transfer (OT) . We instantiate our framework with protocols for N parties and security against up to \( N-1 \) passive corruptions: the MPC protocols of Goldreich-Micali-Wigderson (GMW) in its arithmetic and Boolean version and OT-based BMR (Ben-Efraim et al., CCS’16), as well as novel and highly efficient conversions between them, including a non-interactive conversion from BMR to arithmetic GMW. MOTION is highly efficient, which we demonstrate in our experiments. Compared to secure evaluation of AES-128 with \( N=3 \) parties in a high-latency network with OT-based BMR, we achieve a 16 \( \times \) better throughput of 16 AES evaluations per second using BMR. With this, we show that BMR is much more competitive than previously assumed. For \( N=3 \) parties and full-threshold protocols in a LAN, MOTION is \( 10\times \) – \( 18\times \) faster than the previous best passively secure implementation from the MP-SPDZ framework, and \( 190\times \) – \( 586\times \) faster than the actively secure SCALE-MAMBA framework. Finally, we show that our framework is highly efficient for privacy-preserving neural network inference.
Dynamic Information Flow Tracking for Embedded Binaries using SystemC-based Virtual Prototypes

Avoiding security vulnerabilities is very important for embedded systems. Dynamic Information Flow Tracking (DIFT) is a powerful technique to analyze SW with respect to security policies in order to protect the system against a broad range of security related exploits. However, existing DIFT approaches either do not exist for Virtual Prototypes (VPs) or fail to model complex hardware/software interactions.In this paper, we present a novel approach that enables early and accurate DIFT of binaries targeting embedded systems with custom peripherals. Leveraging the SystemC framework, our DIFT engine tracks accurate data flow information alongside the program execution to detect violations of security policies at run-time. We demonstrate the effectiveness and applicability of our approach by extensive experiments.
Proof-of-Authentication Consensus Algorithm: Blockchain-based IoT Implementation

Internet of Things (IoT) refers to the network of interconnected smart physical objects that collect and exchange information for enabling smart applications and services. IoT devices are often battery powered and lack computational resources and as a consequence, traditional measures of providing security is not feasible in IoT systems which leads to major security vulnerabilities. Blockchain technology has proven to be a secure medium of transaction in trustless public networks and has the potential to resolve the security vulnerabilities intrinsic to IoT systems. However, reaching consensus throughout a blockchain network is computationally demanding and requires large amount of energy. Moreover, typical transactions in blockchain based applications are slow, compounding the issues in adopting blockchain technology in IoT systems. In this work, a lightweight consensus algorithm called Proof-of-Authentication (PoAh), was implemented on resource-constrained IoT edge nodes and evaluated in terms of latency and energy consumption. The implemented consensus algorithm reduces latency in block validation to 29.35 ms and energy consumption to 44.31 mJ for each transaction, demonstrating promise and potential, to feasibly implement blockchain technology in IoT systems.
Multi-Cluster Jumping Particle Swarm Optimization for Fast Convergence

Particle Swarm Optimization (PSO) is a metaheuristic evolutionary computation technique inspired by the social behavior of birds and fish flock.The classical PSO has limitations of slow convergence rate and trapping in local minima, as the dimensions of the data increase.Moreover, majority of improvements made by the researchers in optimization techniques have focused on the accuracy of solution and have overlooked the convergence speed of an algorithm.Keeping in view the need of an optimization algorithm with fast convergence speed, suitable for high dimensional data space, this article proposes a novel concept of Multi-Cluster Jumping PSO.In the proposed method, the particles in the swarm are divided in different clusters to search for the global optimum solution.Each cluster in the swarm has its own cluster best position which is the best position within a cluster and the global best position is located by clusters communication.In order to avoid trapping in the local optima, a jumping strategy is incorporated for stuck particles through relocation of particles to a random new position.Instead of random initialization of the particles, a semi-random initialization is opted by dividing the entire search space and the distribution of particles over a search space is done in independent slots.The proposed approach has the ability to overcome the limitations of classical evolutionary computation methods and is suitable for high dimensional dynamic data.An extensive experimentation is carried out to optimize twelve benchmark functions using the proposed Multi-Cluster Jumping PSO and a significant difference is observed in the convergence speed of the proposed method over the existing state-of-the-art approaches.
Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks

Human collaborators can effectively communicate with their partners to finish a common task by inferring each other's mental states (e.g., goals, beliefs, and desires). Such mind-aware communication minimizes the discrepancy among collaborators' mental states, and is crucial to the success in human ad-hoc teaming. We believe that robots collaborating with human users should demonstrate similar pedagogic behavior. Thus, in this paper, we propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations, where the robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications based on its online Bayesian inference of the user's mental state. To evaluate our framework, we conduct a user study on a real-time human-robot cooking task. Experimental results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot. Code and video demos are available on our project website: https://xfgao.github.io/xCookingWeb/.
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems

In real-world classification tasks, each class often comprises multiple finer-grained As the subclass labels are frequently unavailable, models trained using only the coarser-grained class labels often exhibit highly variable performance across different subclasses. This phenomenon, known as hidden stratification, has important consequences for models deployed in safety-critical applications such as medicine. We propose GEORGE, a method to both measure and mitigate hidden stratification even when subclass labels are unknown. We first observe that unlabeled subclasses are often separable in the feature space of deep models, and exploit this fact to estimate subclass labels for the training data via clustering techniques. We then use these approximate subclass labels as a form of noisy supervision in a distributionally robust optimization objective. We theoretically characterize the performance of GEORGE in terms of the worst-case generalization error across any subclass. We empirically validate GEORGE on a mix of real-world and benchmark image classification datasets, and show that our approach boosts worst-case subclass accuracy by up to 22 percentage points compared to standard training techniques, without requiring any information about the subclasses.
Faster subgradient methods for functions with Hölderian growth

The purpose of this manuscript is to derive new convergence results for several subgradient methods applied to minimizing nonsmooth convex functions with Hölderian growth. The growth condition is satisfied in many applications and includes functions with quadratic growth and weakly sharp minima as special cases. To this end there are three main contributions. First, for a constant and sufficiently small stepsize, we show that the subgradient method achieves linear convergence up to a certain region including the optimal set, with error of the order of the stepsize. Second, if appropriate problem parameters are known, we derive a decaying stepsize which obtains a much faster convergence rate than is suggested by the classical $$O(1/\sqrt{k})$$ result for the subgradient method. Thirdly we develop a novel "descending stairs" stepsize which obtains this faster convergence rate and also obtains linear convergence for the special case of weakly sharp functions. We also develop an adaptive variant of the "descending stairs" stepsize which achieves the same convergence rate without requiring an error bound constant which is difficult to estimate in practice.
Learning Camera-Aware Noise Models

Modeling imaging sensor noise is a fundamental problem for image processing and computer vision applications. While most previous works adopt statistical noise models, real-world noise is far more complicated and beyond what these models can describe. To tackle this issue, we propose a data-driven approach, where a generative noise model is learned from real-world noise. The proposed noise model is camera-aware, that is, different noise characteristics of different camera sensors can be learned simultaneously, and a single learned noise model can generate different noise for different camera sensors. Experimental results show that our method quantitatively and qualitatively outperforms existing statistical noise models and learning-based methods. The source code and more results are available at https://arcchang1236.github.io/CA-NoiseGAN/ .
Suppression of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) After a Second Wave in Victoria, Australia

Abstract Countries worldwide are experiencing a second wave of coronavirus disease 2019 (COVID-19), which is proving to be difficult to control. We describe the combination of physical distancing, mandatory mask wearing, movement restrictions, and enhanced test, trace, and isolation efforts that can be used to successfully suppress community transmission to zero.
ContAuth

User authentication is key in user authorization on smart and personal devices. Over the years, several authentication mechanisms have been proposed: these also include behavioral-based biometrics. However, behavioral-based biometrics suffer from two issues: they are prone to degradation in performance (accuracy) over time (e.g., due to data distribution changes arising from user behavior) and the need to learn the machine learning model from scratch, when adding new users. In this paper, we propose ContAuth, a system that can enhance the robustness of behavioral-based authentication. ContAuth continuously adapts to new incoming data (data incremental learning) and is able to add new users without retraining (class incremental learning). Specifically, ContAuth combines deep learning models with online learning models to achieve learning on the fly, thereby preventing a severe drop in the accuracy between sessions (over time). To add new users, ContAuth employs class incremental learning methods. We evaluate ContAuth on multiple behavior-based user authentication modalities: breathing, gait. and EMG. Our results show that our framework can help True Positive Rate (TPR) to remain high (&gt;85 %) compared to other methods for all the modalities except EMG (&gt;70%) across the sessions while keeping False Positive Rates (FPR) at a minimum (0-10%). It can achieve up to 35% improvement in TPR over a traditional deep learning model. Additionally, iCaRL (an incremental learning method) enables ContAuth to allow the addition of new users by alleviating catastrophic forgetting, to a large extent. Finally, we also show that ContAuth can be deployed efficiently and effectively on device, further providing data privacy.
SpanBERT: Improving Pre-training by Representing and Predicting Spans

We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. SpanBERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERT-Large, our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0 respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6% F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE.
Perils of Zero-Interaction Security in the Internet of Things

The Internet of Things (IoT) demands authentication systems which can provide both security and usability. Recent research utilizes the rich sensing capabilities of smart devices to build security schemes operating without human interaction, such as zero-interaction pairing (ZIP) and zero-interaction authentication (ZIA). Prior work proposed a number of ZIP and ZIA schemes and reported promising results. However, those schemes were often evaluated under conditions which do not reflect realistic IoT scenarios. In addition, drawing any comparison among the existing schemes is impossible due to the lack of a common public dataset and unavailability of scheme implementations. In this paper, we address these challenges by conducting the first large-scale comparative study of ZIP and ZIA schemes, carried out under realistic conditions. We collect and release the most comprehensive dataset in the domain to date, containing over 4250 hours of audio recordings and 1 billion sensor readings from three different scenarios, and evaluate five state-of-the-art schemes based on these data. Our study reveals that the effectiveness of the existing proposals is highly dependent on the scenario they are used in. In particular, we show that these schemes are subject to error rates between 0.6% and 52.8%.