Reviewerly/exHarmony huggingface dataset:
The University of Pittsburgh English Language Institute Corpus (PELIC)

Abstract This report introduces the University of Pittsburgh English Language Institute Corpus (PELIC; Juffs et al., 2020 ), a publicly available 4.2-million-word learner corpus of written texts. Collected over seven years in the University of Pittsburgh’s Intensive English Program, these texts were produced by more than 1,100 students with diverse linguistic backgrounds and proficiency levels. Unlike most learner corpora which are cross-sectional, PELIC is longitudinal, offering greater opportunities for tracking development in a natural classroom setting. This potential is illustrated in an overview of the research conducted to date with these data. The report also provides a description of PELIC’s creation and contents, including how the texts have been managed to facilitate natural language processing. Overall, the corpus contributes to the field of learner corpus research by adding to the pool of freely and publicly available learner corpora, supplemented by a useful set of Python tools and tutorials for accessing these data.
Modeling of structured light projection on vibrating surfaces: Amplitude and phase mapping by fringe visibility evaluation

Vibration analysis is an extremely important issue in many fields of production systems like quality control as well as maintenance procedures. In this work it is theoretically studied the behavior of structured light in the form of straight sinusoidal fringes projected on vibrating objects. It was shown that by time averaging the moving fringes on the vibrating surface the resulting light pattern can be described with the help of the zero-order Bessel function of the vibration amplitude and constant factors related to the optical setup geometry. A simple formula was derived allowing to determine the Bessel function by combining the projected light patterns in different object configurations. The amplitude distribution in turn was retrieved from the Bessel function with the help of the explicit Euler method. It was also demonstrated that the phase mapping can be carried out by illuminating the object with a properly oscillating light pattern. The vibration analyses of a rectangular bar and of a square plate was simulated and its possible applications were pointed out.
Ensembles strategies for backtracking search algorithm with application to engineering design optimization problems

This paper proposes two ensemble strategies for the backtracking search algorithm (BSA). The first one is an ensemble of two sets of evolutionary operators that balances exploration and exploitation abilities. The second one is an ensemble of values for each parameter associated with the evolutionary operators. The second strategy provides diverse search moves with various search step lengths that are essential for searching different search landscapes. In addition to the ensemble strategies, another strategy is used to reinitialize specific individuals of the population to escape from local optima. Sixteen variants of the BSA are built based on different combinations of these strategies or their modified versions. The best variant for solving 29 problems of CEC2017 test suite is statistically compared with nineteen state-of-the-art algorithms. The results confirm its superiority to all the considered algorithms. Remarkably, according to the Wilcoxon rank-sum test with a significance level of 0.05, it is better than others for solving at least 20 and 18 functions with 30 and 50 dimensions, respectively. Furthermore, it is applied to five engineering design optimization problems. Its solutions are at least as well as or better than those obtained by the best existing algorithms in the literature for three problems.
Towards achieving efficient access control of medical data with both forward and backward secrecy

Healthcare service providers store the patients’ electronic medical records in the cloud in order to provide high quality healthcare services. Patients’ sensitive data is typically encrypted before storing in the cloud. However, it gives rise to a new challenge, namely access control over encrypted data . Attribute-Based Encryption (ABE) is a promising cryptographic technique to achieve the fine grained access control on outsourced encrypted data. Traditional ABE schemes assume a fixed access policy that is not suitable for the present dynamic environment. Although some ABE schemes with a dynamic access control policy have been proposed in the literature, these schemes have not addressed forward security, backward security, and user revocation after a policy update. In this paper, we propose an ABE scheme that supports access policy updates, and also provides forward security, backward security and user revocation at the same time. We have formally shown that the proposed scheme is Chosen Plaintext Attack (CPA)-secure under the Decisional Bilinear Diffie–Hellman (DBDH) assumption. Finally, the performance analysis exhibits that the proposed scheme is efficient in communication and computation, and is also suitable for resource constrained devices.
Cost-Effective Strong Consistency on Scalable Geo-Diverse Data Replicas

The Raft algorithm maintains strong consistency across data replicas in Cloud. This algorithm places nodes, i.e., leader and follower, to serve read/write requests spanning geo-diverse sites. As the workload increases, Raft shall provide proportional scale-out performance. However, traditional scale-out techniques are bottlenecked in Raft with an exponentially increased performance penalty when provisioned sites exhaust local resources. To provide scalability in Raft, this paper presents a cost-effective mechanism that enables elastic auto scaling in Raft, called BW-Raft. BW-Raft extends the original Raft with the following abstractions: (1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">secretary</i> nodes that take over expensive log synchronization operations from the leader, relaxing the performance constraint on locks. (2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">observer</i> nodes that handle reads only, improving throughput for typical data intensive services. These abstractions are stateless, allowing elastic scale-out on unreliable yet cheap spot instances. In theory, we prove that BW-Raft can preserve the strong consistency guarantee from Raft at scale-out, handling 50X more nodes, compared to the original Raft. We have prototyped the BW-Raft on key-value services and evaluated it with many state-of-the-arts on Amazon EC2 and Alibaba Cloud. Our results show that within the same budget, BW-Raft incurs 5-7X less resource footprint increment than Multi-Raft. Using spot instances, BW-Raft can reduces costs by 84.5%, compared to Multi-Raft. In the real world experiments, BW-Raft improves goodput of the 95th-percentile SLO by 9X, thus, serves an alternative for distributed service scaling out with strong consistency.
Graph Signal Processing and Applications: A Survey

Graph signal processing (GSP) is a field of research with many potential applications, specially in signals with irreg-ular structures. Graphs are structures that represent irregular data attributes. Signal processing approaches mainly focus on analyzing the underlying data. Given the increasing availability of multisensor and multinode measurements, recorded in irregular grids, it can be extremely advantageous to analyze data structured as &#x201C;signals on graphs&#x201D;. In this paper, we present a survey on applications and methods to solve problems in different areas of digital signal processing applying GSP.
AEROKEY

Wireless connectivity is becoming common in increasingly diverse personal devices, enabling various interoperation- and Internet-based applications and services. More and more interconnected devices are simultaneously operated by a single user with short-lived connections, making usable device authentication methods imperative to ensure both high security and seamless user experience. Unfortunately, current authentication methods that heavily require human involvement, in addition to form factor and mobility constraints, make this balance hard to achieve, often forcing users to choose between security and convenience. In this work, we present a novel over-the-air device authentication scheme named AEROKEY that achieves both high security and high usability. With virtually no hardware overhead, AEROKEY leverages ubiquitously observable ambient electromagnetic radiation to autonomously generate spatiotemporally unique secret that can be derived only by devices that are closely located to each other. Devices can make use of this unique secret to form the basis of a symmetric key, making the authentication procedure more practical, secure and usable with no active human involvement. We propose and implement essential techniques to overcome challenges in realizing AEROKEY on low-cost microcontroller units, such as poor time synchronization, lack of precision analog front-end, and inconsistent sampling rates. Our real-world experiments demonstrate reliable authentication as well as its robustness against various realistic adversaries with low equal-error rates of 3.4% or less and usable authentication time of as low as 24 s.
Lung Cancer Detection using Ensemble of Machine Learning Models

Lung cancer is a fatal genetic disease that has an abnormal growth of cancerous cells in the lungs of the human body. Since the lungs are one of the vital human body organs, lung cancer can have serious implications. In this work, we have focused on fast detection of lung cancer to be beneficial for patients and doctors. Lung cancer can be detected using Histopathology images and other diagnostic tools as well. The proposed work contains a hybridized model of Convolutional neural networks and an ensemble of Machine Learning algorithms: Support Vector Classifier, Random Forest, and XG Boost that detect the lung cancer using histopathology images. The overall accuracy achieved by this work is 99.13 &#x0025;.
HandyPose: Multi-level framework for hand pose estimation

Hand pose estimation is a challenging task due to the large number of degrees of freedom and the frequent occlusions of joints. To address these challenges, we propose HandyPose, a single-pass, end-to-end trainable architecture for 2D hand pose estimation using a single RGB image as input. Adopting an encoder-decoder framework with multi-level features, along with a novel multi-level waterfall atrous spatial pooling module for multi-scale representations, our method achieves high accuracy in hand pose while maintaining manageable size complexity and modularity of the network. HandyPose takes a multi-scale approach to representing context by incorporating spatial information at various levels of the network to mitigate the loss of resolution due to pooling. Our advanced multi-level waterfall module leverages the efficiency of progressive cascade filtering while maintaining larger fields-of-view through the concatenation of multi-level features from different levels of the network in the waterfall module. The decoder incorporates both the waterfall and multi-scale features for the generation of accurate joint heatmaps in a single stage. Our results demonstrate state-of-the-art performance on popular datasets and show that HandyPose is a robust and efficient architecture for 2D hand pose estimation.
Discussion and analysis on the improvement of the widening technology of cement concrete pavement of rural highway

In order to improve the widening technology of cement concrete pavement of rural highway, this paper further studies the blockage simulation process of permeable concrete pavement. Moreover, this paper separately studies various external conditions such as sediment gradation, horizontal runoff velocity, seepage velocity, porosity, etc., and reveals the mechanism of pore blockage in permeable pavement. After decades of self-gravity consolidation and the effect of traffic load on the subgrade of the rural old road, the consolidation of the old road has been basically completed. At the same time, after the new road is built, due to its own load and vehicle load, it gradually generates its own settlement, resulting in differential settlement of the old and new subgrade. In addition, this paper explores this difference through digital simulation, and puts forward several suggestions in combination with the simulation model. Finally, on this basis, this paper verifies the method proposed in this paper through experimental research. The research results show that the improvement strategy proposed in this paper is suitable for the widening construction of cement concrete pavement of rural highway.
Generalizing the Orbifold Model for Voice Leading

We generalize orbifold models for chords and voice leading to incorporate loudness, allowing for the modeling of resting voices, which are used frequently by composers and arrangers across genres. In our generalized setting (strictly speaking, that of orbispaces rather than an orbifolds), passages with resting voices, passages with two or more voices in unison, and fully harmonized passages occupy distinct subspaces that interact in mathematically precise and musically interesting ways. In particular, our setting includes previous orbifold models by way of constant-loudness subspaces, and provides a way to model voice leading between chords of different cardinalities. We model voice leading in this general setting by morphisms in the orbispace path groupoid, a category for which we give a formal definition. We demonstrate how to visualize such morphisms as singular braids, and explore how our approach relates to (and is consistent with) selected previous work.
Face Mask Detection Using Machine Learning

During this global pandemic, Face mask detection has seen an outstanding development in image processing and Computer vision. Face masks can be detected by observing patterns in an image using various machine learning algorithms. Here three machine learning algorithms were considered, namely, CNN(Convolutional Neural Network), SVM(Support Vector Machine), and PCA (Principal Component Analysis). Dataset used consists of images of people with masks and without masks extracted from Kaggle. In this literature, the efficiency of the detection of face masks after performing K-cross validation has been calculated. All the algorithms yielded an accuracy above 90&#x0025; with CNN being the highest with an accuracy of 98.6&#x0025;.
Generative Model Using Knowledge Graph for Document-Grounded Conversations

Document-grounded conversation (DGC) is a natural language generation task to generate fluent and informative responses by leveraging dialogue history and document(s). Recently, DGCs have focused on fine-tuning using pretrained language models. However, these approaches have a problem in that they must leverage the background knowledge under capacity constraints. For example, the maximum length of the input is limited to 512 or 1024 tokens. This problem is fatal in DGC because most documents are longer than the maximum input length. To address this problem, we propose a document-grounded generative model using a knowledge graph. The proposed model converts knowledge sentences extracted from the given document(s) into knowledge graphs and fine-tunes the pretrained model using the graph. We validated the effectiveness of the proposed model using a comparative experiment on the well-known Wizard-of-Wikipedia dataset. The proposed model outperformed the previous state-of-the-art model in our experiments on the Doc2dial dataset.
Implementing Prioritized-Breadth-First-Search for Instagram Hashtag Recommendation

Instagram is one of the most used social media platforms where around 500 million users interact with content daily, which creates excellent marketing opportunities. Instagram&#x2019;s marketing growth is primarily focused on the page&#x2019;s content, but suitable hashtags are equally essential to traffic. Hashtag research is one of the most complex parts of an Instagram marketing campaign, and usually, online tools are used to get a set of hashtags from one niche-specific hashtag. These tools are suitable for initial days, but their recommendations are often less in number, outdated, and lack connectivity among hashtags. This paper focuses on creating a set of highly related hashtags gathered from real-time data and sorting it in the best way possible using a parent hashtag as an input. This algorithm introduces a prioritization system that takes likes, occurrence, and rank into account and implements a Prioritized-Breadth-First-Search considering each hashtag as a node in a graph instead of an isolated entity.
LRP-net: A lightweight recursive pyramid network for single image deraining

Single image deraining, as a low-level computer vision task, has been drawn extensive attention in recent years. Rain streaks can degrade subjective visibility quality, meanwhile, bring significant difficulties to subsequent high-level computer vision tasks such as object detection. Nowadays, deep-learning based methods, specifically Convolutional Neural Networks (CNN) based ones are adopted to remove the rain streaks and become the state-of-the-art. However, existing popular deep-networks have complicated branches and numerous layers, which strengthen the ability of removing rain streaks and result in high memory and computational cost inevitably. This restricts many applications in real-time and limited computation resource scene, especially on mobile or edge devices. To handle this issue, this paper proposes a novel Lightweight Recursive Pyramid network (LRP-Net) with a small number of parameters for single image deraining. To begin with, we propose a novel Lightweight Pyramid Deraining (LPD) block which consists of a multi-scale pyramid convolution for sufficient feature extraction and a pointwise convolution for feature fusion. Meanwhile, we also design a novel group convolution strategy in LPD for the sake of remarkable parameter reduction. Secondly, we combine a recursive deraining mechanism, a critical component that serves as a feature fusion iterator to construct our LRPNet a powerful and lightweight multi-stage model. In the benefit of the combination between the LPD block and recursive mechanism, the total number of parameters in LRP-Net is only 130 k, which is nearly a 40- reduction compared with the latest state-of-the-art models. The extensive experiments demonstrate the superiority of LRP-Net in both quantitative assessments and visual quality.
Human Action Categorization System using Body Pose Estimation for Multimodal Observations from Single Camera

We propose a system using a multimodal probabilistic approach to solve the human action recognition challenge. This is achieved by extracting the human pose from an ongoing activity from a single camera. This pose is used to capture additional body information using generalized features such as location, time, distances, and angles. A probabilistic model, multimodal latent Dirichlet allocation (MLDA), which uses this multimodal information, is then used to recognize actions through topic modeling. We also investigate the influence of each modality and their combinations to recognize human actions from multimodal observations. The experiments show that the proposed generalized features captured significant information that enabled the classification of various daily activities without requiring prior labeled data.
Research on Fitness Movement Monitoring System Based on Internet of Things

A multiuser motion-monitoring system based on MEMS is proposed for fitness movement, it is used to monitor the three important parameters of movement type, movement times, and movement cycle in the body movement and supports the simultaneous use of multiple users. The specific content of the method: (1) In terms of system design, a motion-monitoring system framework based on the Internet of things is proposed considering the motion-monitoring scene oriented to intelligent fitness. (2) In the aspect of algorithm, the relevant research of motion pattern recognition and cycle calculation method is carried out. For action pattern recognition, SVM-based algorithm to adapt to different computing capabilities of the scene is applied. (3) Experiments on 7 kinds of actions show that the proposed deep neural network has a good learning effect on small datasets, the recognition accuracy of the proposed deep neural network reaches 97.61%, and the recognition accuracy of SVM also reaches over 96%. In the 50 times of operation cycle calculation experiments, the frequency statistics algorithm has reached 100% of the calculation accuracy, and the calculation results of the operation cycle are close to the real value, which proves the validity of the method of cycle calculation. The experiment proves that the zero-crossing detection and wavelet analysis methods have a good overall effect and can accurately count and calculate the period when the number of actions is more, improve fitness efficiency, and provide guarantee for human health.
Real-Time Directed Rapidly Exploring Random Tree Path Planning for Air Collision Avoidance

In this paper, a path planning algorithm is proposed for dynamic obstacle avoidance by improving a rapidly exploring random tree (RRT) algorithm, a sampling-based path-planning algorithm. Although guaranteeing a globally optimal path is impossible when using RRT algorithm, it is advantageous because it can generate the path quickly. Considering this advantage of the RRT algorithm, the directed RRT (DRRT) algorithm was developed, which reduces the generation time and supplements optimality by improving node generation and tree expansion process. It is also added the path smoothing method for the flight. The anytime DRRT algorithm is based on the DRRT algorithm and repeats path generation within the time limit considering the direction of movement of the obstacle and selects the shortest path for the flight. Air collision avoidance between unmanned aerial vehicles (UAVs) considering the intruders as a dynamic obstacle was simulated using the Gazebo simulator. Based on the simulation, the performance of the proposed anytime DRRT algorithm was verified with varying the host UAV and intruder conditions, and its applicability to UAVs was affirmed.
Semi-supervised graph convolutional network for Ethereum phishing scam recognition

In recent years, as the blockchain and the cryptocurrency built on it have become popular, a large number of decentralized financial applications have been built on the Ethereum network. This makes the security issues on Ethereum attract more and more researchers' attention. Phishing scams on Ethereum have caused people to suffer huge economic losses. Recently, with the popularity of graph convolutional neural networks (GCN), many models based on GCN for node classification have emerged. However, these current GCN models are difficult to cope with the challenges caused by the lack of side information and labels of nodes in the Ethereum network. In this paper, we propose a semisupervised graph convolutional neural network model based on important neighbors for the identification of phishing scam nodes on Ethereum. In our work, we design the pretext task for the node embedding module so that our model can learn the appropriate node embedding by using a large amount of unlabeled node data. Subsequent experiments show that our proposed model is better than all other baselines, which proves the effectiveness of our model.