Reviewerly/exHarmony huggingface dataset:
Aksara: An Indonesian Morphological Analyzer that Conforms to the UD v2 Annotation Guidelines

The objective of this work is to build an Indonesian morphological analyzer named Aksara that conforms to the Universal Dependencies (UD), especially UD v2. Many works had developed Indonesian morphological analyzer, but as far as we know none conforms to the UD annotation guidelines. In building Aksara we use the same approach with MorphInd, another Indonesian morphological analyzer, that uses finite state compiler named Foma. Aksara has capability to perform four tasks: 1) word segmentation, 2) lemmatization, 3) POS tagging, and 4) morphological features analysis. To evaluate the quality of this tool, we used an Indonesian dependency treebank that conforms to UD v2 as the gold standard. We also compare the performance measures of Aksara with MorphInd, by mapping MorphInd output to CoNNL-U format. The experiment results show that for all the four tasks Aksara outperforms MorphInd. For word segmentation task, Aksara has accuracy of 96.9%, for lemmatization with case-sensitive it has accuracy of 94.83%, for POS tagging it has F1-score of 88.2% and finally for morphological features analysis, among 18 feature-value tags already implemented, nine tags already have F1-score more than 80%.
Improving Object Detection Quality by Incorporating Global Contexts via Self-Attention

Fully convolutional structures provide feature maps acquiring local contexts of an image by only stacking numerous convolutional layers. These structures are known to be effective in modern state-of-the-art object detectors such as Faster R-CNN and SSD to find objects from local contexts. However, the quality of object detectors can be further improved by incorporating global contexts when some ambiguous objects should be identified by surrounding objects or background. In this paper, we introduce a self-attention module for object detectors to incorporate global contexts. More specifically, our self-attention module allows the feature extractor to compute feature maps with global contexts by the self-attention mechanism. Our self-attention module computes relationships among all elements in the feature maps, and then blends the feature maps considering the computed relationships. Therefore, this module can capture long-range relationships among objects or backgrounds, which is difficult for fully convolutional structures. Furthermore, our proposed module is not limited to any specific object detectors, and it can be applied to any CNN-based model for any computer vision task. In the experimental results on the object detection task, our method shows remarkable gains in average precision (AP) compared to popular models that have fully convolutional structures. In particular, compared to Faster R-CNN with the ResNet-50 backbone, our module applied to the same backbone achieved +4.0 AP gains without the bells and whistles. In image semantic segmentation and panoptic segmentation tasks, our module improved the performance in all metrics used for each task.
Efficient Detection in Aerial Images for Resource-Limited Satellites

Object detection in aerial images is a challenging task because of the complex background and various orientations of the objects. Currently, many detectors have made significant progress in improving mAP scores, but they do not achieve the improvements in efficiency and model size. In this letter, we propose a detector for resource-limited satellite network, namely, the simple convolutional neural networks (simple-CNNs), which can be directly applied in actual application scenarios using small sample data. Specifically, a new sample 16-layer network is devised to reduce the model size. Meanwhile, we calculate a new anchor for ten classes and carefully design a change-IOU Loss (CI-Loss) function for horizontal bounding box detection to improve the accuracy. Extensive experiments on two remote sensing public data sets (NWPU VHR-10 and part of DIOR) show good performance for the efficiency of our detector. In particular, our simple-CNN achieves 68.9 mAP on the NWPU VHR-10 test-dev data set with 90 M parameters and a test speed of 72 ms, and it also has good performance on a part of DIOR.
CrypSH: A Novel IoT Data Protection Scheme Based on BGN Cryptosystem

The Internet of Things (IoT) is an emerging paradigm and has penetrated deeply into our daily life. Due to the seamless connections of the IoT devices with the physical world through the Internet, the IoT applications use the cloud to store and provide ubiquitous access to collected data. Sharing of data with third party services and other users incurs potential risks and leads to unique security and privacy concerns, e.g., data breaches. Existing cryptographic solutions are inapt for resource-constrained IoT devices, because of their significant computational overhead. To address these concerns, we propose a data protection scheme to store the encrypted IoT data in a cloud, while still allowing query processing over the encrypted data. Our proposed scheme features a novel encrypted data sharing scheme based on Boneh-Goh-Nissim (BGN) cryptosystem, with revocation capabilities and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> key updates. We perform exhaustive experiments on real datasets, to assess the feasibility of the proposed scheme on the resource constrained IoT devices. The results show the feasibility of our scheme, together with the ability to provide a high level of security. The results also show that our scheme significantly reduces the computation, storage and energy overheads than the best performed scheme in the state-of-the-art.
Learning Min-norm Stabilizing Control Laws for Systems with Unknown Dynamics

This paper introduces a framework for learning a minimum-norm stabilizing controller for a system with unknown dynamics using model-free policy optimization methods. The approach begins by first designing a Control Lyapunov Function (CLF) for a (possibly inaccurate) dynamics model for the system, along with a function which specifies a minimum acceptable rate of energy dissipation for the CLF at different points in the state-space. Treating the energy dissipation condition as a constraint on the desired closed-loop behavior of the real-world system, we use penalty methods to formulate an unconstrained optimization problem over the parameters of a learned controller, which can be solved using model-free policy optimization algorithms using data collected from the plant. We discuss when the optimization learns a stabilizing controller for the real world system and derive conditions on the structure of the learned controller which ensure that the optimization is strongly convex, meaning the globally optimal solution can be found reliably. We validate the approach in simulation, first for a double pendulum, and then generalize the framework to learn stable walking controllers for underactuated bipedal robots using the Hybrid Zero Dynamics framework. By encoding a large amount of structure into the learning problem, we are able to learn stabilizing controllers for both systems with only minutes or even seconds of training data.
A Privacy-Protecting Authorization System Based on Blockchain and zk-SNARK

With the rapid development of blockchain, many service providers tend to provide users with network resources and services on the basis of blockchain, and most network resources are inseparable from the authorization. However, the current authorization mechanism based on blockchain is not perfect, which is easy to cause the user's identity attributes to be leaked, and not conducive to protecting the privacy of users. In order to improve the authorization mechanism based on the blockchain and protect the user's privacy to the greatest extent during the authorization process, we proposed a privacy-protecting authorization system based on blockchain and zk-SNARK. First of all, SP can authorize users through the blockchain smart contract. Second, in the authorization process, we introduced zk-SNARK. Using the zero-knowledge property of zk-SNARK, the SP can still authorize the users without knowing the identity attribute value of the users. Therefore, the proposed system can better protect the users' attribute privacy. Finally, we analyze the security of the proposed system, and the results show that it is computationally infeasible for an attacker to attack the system.
Automatic Text Summarization Using Genetic Algorithm and Repetitive Patterns

Taking into account the increasing volume of text documents, automatic summarization is one of the important tools for quick and optimal utilization of such sources. Automatic summarization is a text compression process for producing a shorter document in order to quickly access the important goals and main features of the input document. In this study, a novel method is introduced for selective text summarization using the genetic algorithm and generation of repetitive patterns. One of the important features of the proposed summarization is to identify and extract the relationship between the main features of the input text and the creation of repetitive patterns in order to produce and optimize the vector of the main document features in the production of the summary document compared to other previous methods. In this study, attempts were made to encompass all the main parameters of the summary text including unambiguous summary with the highest precision, continuity and consistency. To investigate the efficiency of the proposed algorithm, the results of the study were evaluated with respect to the precision and recall criteria. The results of the study evaluation showed the optimization the dimensions of the features and generation of a sequence of summary document sentences having the most consistency with the main goals and features of the input document.
The Role of Regularization in Overparameterized Neural Networks

In this paper, we consider gradient descent on a regularized loss function for training an overparametrized neural network. We model the algorithm as an ODE and show how overparameterization and regularization work together to provide the right tradeoff between training and generalization errors.
A Contactless Laser Doppler Strain Sensor for Fatigue Testing with Resonance-Testing Machine

In this article, a non-contact laser Doppler strain sensor designed for fatigue testing with the resonance-testing machine is presented. The compact sensor measures in-plane displacements simultaneously from two adjacent points using the principle of in-plane, laser-Doppler vibrometry. The strain is computed from the relative displacements divided by the distance between these two points. The optical design, the mathematical model for estimating noise-limited resolution, the simulation results of this model, and the first measurement results are presented. The comparison of the measurement results of our sensor with the results of a conventional strain gauge shows that our design meets the measurement requirements. The maximum strain deviation compared to conventional strain gauges of the laser-Doppler extensometer is below 4×10−5 in all performed experiments.
On the feasibility of IoT-based smart meters for earthquake early warning

The microelectromechanical systems (MEMS) accelerometer built into a smart meter (SM) has a nominal digital resolution of 16 bits. However, this resolution collapses to 7 bits of information per sample when used in an urban environment. This collapse in resolution limits the sensitivity required to effectively operate the earthquake early warning platform (EEWP). In this study, we evaluate the performance of the MEMS sensor in present SMs with respect to a reference sensor, with a special focus on its poor noise power spectral density (PSD, [Formula: see text]). We also explore the general capacity of the SM in an IoT-based EEWP and provide explicit information regarding the 16-bit digital MEMS accelerometer. Then, we investigate the functionality of the sensor in the context of event detection in the presence of background vibration. When the value of acceleration root mean square (RMS) exceeds 20 mg, the meter’s error decreases to &lt;20%, whereas the peak ground acceleration error decreases to &lt;20% for the peak value greater than ~70 mg. The MEMS sensor is unreliable for motions with a peak acceleration of less than 148 mg or those with an RMS value less than 46 mg. However, we note that SMs exhibit reasonable amplitude and phase coherence for frequencies above 1 Hz with respect to the reference accelerometer. To enhance the sensitivity, averaging 1000 coherent accelerometer observations enhances the digital resolution to 14 bits, which allows the efficient usage of the network bandwidth. Since the accelerometer is used as an anti-tampering mechanism, the SM is similar to a tiltmeter. Therefore, it is necessary to reconfigure SMs for early warning systems. Despite the challenges, the use of SM for an IoT-based EEWP is technically feasible.
Building ontologies for solving compute-intensive problems

Abstract The aim of the study, results of which are presented in this paper, is to analyse methods and tools for constructing an ontology related to solving compute-intensive problems and to form algorithms of its use. This problem arises from the need to solve problems using modern and future supercomputers, containing millions and, in the long term, billions of simultaneously operating computing cores and having a huge degree of parallelism. In solving such problems, the researcher should be well versed in both computational methods for solving the problem and modern supercomputer technologies, which is not always the case. One of the solutions to this problem is the creation of a knowledge base that includes ontological descriptions of methods for solving compute-intensive problems and architectures of supercomputers that can be used to solve them. The development of ontologies for a given subject area is one of the most important stage in creating an intelligent support system for solving specific compute-intensive problems. The paper discusses the methods and tools that are used to build the ontology. The paper also presents examples of the development of ontologies for astrophysics and geophysics problems.
Improving Robustness of DASH Against Unpredictable Network Variations

Most video players use adaptive bitrate (ABR) algorithms to provide good quality-of-experience (QoE) in dynamic network conditions. To deal with the adaptation challenges, many ABR algorithms select bitrate by optimizing a defined QoE function. Within the framework, various algorithms mainly differ in how the optimization problem is solved, including prediction-based approaches and learn-based approaches. However, these algorithms suffer from limited performance in the current popular mobile streaming which has limited resources and rapidly changing link rates. Existing machine-learning approaches face deployment difficulties on mobile devices, and prediction-based approaches that rely on throughput prediction experience large buffer occupancy variations in cellular networks, resulting in rebuffering frequently. To provide a robust and lightweight ABR algorithm for mobile streaming, this work improves the robustness of prediction-based scheme against unpredictable network variations and develops RBC (Robust Bitrate Controller) algorithm. Rather than optimizing QoE over the entire buffer capacity, RBC creates buffer margins to absorb the impact of throughput jitters and solves QoE maximization on the narrowed buffer range. The amount of buffer margin is dynamically adjusted based on the real-time throughput fluctuation to ensure sufficient de-jitter space. For online lightweight deployment, RBC provides a closed-form solution of the desired bitrate with small computation complexity by using adaptive control approach. Trace-driven experiments and real-world tests show that RBC effectively reduces the playback freezing and gains an improvement in overall QoE.
Spatially Aware Metadata for Raw Reconstruction

A camera sensor captures a raw-RGB image that is then processed to a standard RGB (sRGB) image through a series of onboard operations performed by the camera's image signal processor (ISP). Among these processing steps, local tone mapping is one of the most important operations used to enhance the overall appearance of the final rendered sRGB image. For certain applications, it is often desirable to de-render or unprocess the sRGB image back to its original raw-RGB values. This "raw reconstruction" is a challenging task because many of the operations performed by the ISP, including local tone mapping, are nonlinear and difficult to invert. Existing raw reconstruction methods that store specialized metadata at capture time to enable raw recovery ignore local tone mapping and assume that a global transformation exists between the raw-RGB and sRGB color spaces. In this work, we advocate a spatially aware metadata-based raw reconstruction method that is robust to local tone mapping, and yields significantly higher raw reconstruction accuracy (6 dB average PSNR improvement) compared to existing raw reconstruction methods. Our method requires only 0.2% samples of the full-sized image as metadata, has negligible computational overhead at capture time, and can be easily integrated into modern ISPs.
Continuous-variable quantum key distribution based on photon addition operation*

It is shown that the non-Gaussian operations can not only be used to prepare the nonclassical states, but also to improve the entanglement degree between Gaussian states. Thus these operations are naturally considered to enhance the performance of continuous variable quantum key distribution (CVQKD), in which the non-Gaussian operations are usually placed on the right-side of the entangled source. Here we propose another scheme for further improving the performance of CVQKD with the entangled-based scheme by operating photon-addition operation on the left-side of the entangled source. It is found that the photon-addition operation on the left-side presents both higher success probability and better secure key rate and transmission distance than the photon subtraction on the right-side, although they share the same maximal tolerable noise. In addition, compared to both photon subtraction and photon addition on the right-side, our scheme shows the best performance and the photon addition on the right-side is the worst.
Taming Convergence for Asynchronous Stochastic Gradient Descent with Unbounded Delay in Non-Convex Learning

Understanding the convergence performance of asynchronous stochastic gradient descent method (Async-SGD) has received increasing attention in recent years due to their foundational role in machine learning. To date, however, most of the existing works are restricted to either bounded gradient delays or convex settings. In this paper, we focus on Async-SGD and its variant Async-SGDI (which uses increasing batch size) for non-convex optimization problems with unbounded gradient delays. We prove o(1/√ (k)) convergence rate for Async-SGD and o(1/k) for Async-SGDI. Also, a unifying sufficient assumption for Async-SGD's convergence is proposed, which includes two major gradient delay models in the literature as special cases.
Development of Trust Infrastructures for Virtual Asset Service Providers

Virtual asset service providers (VASPs) currently face a number of challenges, both from the technological and the regulatory perspectives. In the context of virtual asset transfers one key issue is the need for VASPs to securely exchange customer information to comply to the Travel Rule. We discuss a VASP information sharing network as one form of a trust infrastructure for VASP-to-VASP interactions. Related to this is the need for a trusted identity infrastructure for VASPs that would permit other entities to quickly ascertain the legal business status of a VASP. For customers of VASPs there is a need for seamless integration between the VASP services and the existing consumer identity management infrastructure, providing a user-friendly experience for transferring virtual assets to other users. Finally, for regulated wallets, an attestation infrastructure may provide VASPs and insurance providers with better visibility into the state of wallets based on trusted hardware.
Towards Quantum-Enhanced Machine Learning for Network Intrusion Detection

Network Intrusion Detection Systems (NIDSs) are commonly used today to detect malicious activities. Quantum computers, despite not being practical yet, are becoming available for experimental purposes. We present the first approach for applying unsupervised Quantum Machine Learning (QML) in the context of network intrusion detection from the perspective of quantum information, based on the concept of quantum-assisted ML. We evaluate it using IBM QX in simulation mode and show that the accuracy of a Quantum-Assisted NIDS, based on our approach, can be high, rivaling with the the best conventional SVM results, with a dependence on the characteristics of the dataset.
A Hybrid Deep Learning Framework using CNN and GRU-based RNN for Recognition of Pairwise Similar Activities

A challenging task in human activity recognition is to classify very naturally similar activities. In this paper, we introduce a unified deep learning model working as a hybrid framework of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) modules to solve the problem of recognizing activities that are similar and which occur in pairs in their distributions. We have trained and tested our model on two datasets comprising of pairwise similar activities. The proposed framework has been successful as it outperformed most of the state-of-the-art models for this task. This hybrid model achieves activity recognition accuracies of 89.14% and 87.76% on the two datasets respectively and proves itself accurate and scalable. Contribution- A CNN-GRU deep learning framework for recognition of pairwise similar activities using multimodal wearable data.
The Role of the Magnitude of Change in Detecting Fixed Enumeration Units on Dynamic Choropleth Maps

Dynamic maps are commonly used for the depiction of quantitative information. However, their users often fail to notice changes in the intensity of geographic phenomena. Moreover, if the distribution of colour values between two scenes changes, the user might have a problem with recalling the colour arrangement from the previous scene. A commonly occurring mistake is indicating that the colour changed its value, while in reality it did not. This paper examines the potential impact of the magnitude of change on the detection of the fixed enumeration units of a dynamic choropleth map. The research is based on Signal Detection Theory methodology and uses eye-tracking technology to examine the change blindness phenomenon on spatiotemporal maps. The results show that regardless of the magnitude of change and the number of enumeration units, the participants were convinced that the colour value in a particular place changed, even though it did not.