Reviewerly/exHarmony huggingface dataset:
Contextualized Sparse Representations for Real-Time Open-Domain Question Answering

Open-domain question answering can be formulated as a phrase retrieval problem, in which we can expect huge scalability and speed benefit but often suffer from low accuracy due to the limitation of existing phrase representation models.In this paper, we aim to improve the quality of each phrase embedding by augmenting it with a contextualized sparse representation (SPARC).Unlike previous sparse vectors that are term-frequencybased (e.g., tf-idf) or directly learned (only few thousand dimensions), we leverage rectified self-attention to indirectly learn sparse vectors in n-gram vocabulary space.By augmenting the previous phrase retrieval model (Seo et al., 2019) with SPARC, we show 4%+ improvement in CuratedTREC and SQuAD-Open.Our CuratedTREC score is even better than the best known retrieve & read model with at least 45x faster inference speed. 1
Enhancing Recommendation Diversity using Determinantal Point Processes on Knowledge Graphs

Top-N recommendations are widely applied in various real life domains and keep attracting intense attention from researchers and industry due to available multi-type information, new advances in AI models and deeper understanding of user satisfaction. Whileaccuracy has been the prevailing issue of the recommendation problem for the last decades, other facets of the problem, namelydiversity andexplainability, have received much less attention. In this paper, we focus on enhancing diversity of top-N recommendation, while ensuring the trade-off between accuracy and diversity. Thus, we propose an effective framework DivKG leveraging knowledge graph embedding and determinantal point processes (DPP). First, we capture different kinds of relations among users, items and additional entities through a knowledge graph structure. Then, we represent both entities and relations as k-dimensional vectors by optimizing a margin-based loss with all kinds of historical interactions. We use these representations to construct kernel matrices of DPP in order to make top-N diversified predictions. We evaluate our framework on MovieLens datasets coupled with IMDb dataset. Our empirical results show substantial improvement over the state-of-the-art regarding both accuracy and diversity metrics.
Improving Neural Machine Translation with Soft Template Prediction

Although neural machine translation (NMT) has achieved significant progress in recent years, most previous NMT models only depend on the source text to generate translation.Inspired by the success of template-based and syntax-based approaches in other fields, we propose to use extracted templates from tree structures as soft target templates to guide the translation procedure.In order to learn the syntactic structure of the target sentences, we adopt the constituency-based parse tree to generate candidate templates.We incorporate the template information into the encoder-decoder framework to jointly utilize the templates and source text.Experiments show that our model significantly outperforms the baseline models on four benchmarks and demonstrate the effectiveness of soft target templates.
A Synthetic Minority Based on Probabilistic Distribution (SyMProD) Oversampling for Imbalanced Datasets

Handling an imbalanced class problem is a challenging task in real-world applications. This problem affects various prediction models that predict only the majority class and fail to identify the minority class because of the skewed data. The oversampling technique is one of the exciting solutions that handles the imbalanced class problem. However, several existing oversampling methods do not consider the distribution of the target variable and cause an overlapping class problem. Therefore, this study introduces a new oversampling technique, namely Synthetic Minority based on Probabilistic Distribution (SyMProD), to handle skewed datasets. Our technique normalizes data using a Z-score and removes noisy data. Then, the proposed method selects minority samples based on the probability distribution of both classes. The synthetic instances are generated from selected points and several minority nearest neighbors. Our technique aims to create synthetic instances that cover the minority class distribution, avoid the noise generation, and reduce the possibilities of overlapping classes and overgeneralization problems. Our proposed technique is validated using 14 benchmark datasets and three classifiers. Moreover, we compare the performance with seven other conventional oversampling algorithms. The empirical results show that our method achieves better performance compared with other oversampling techniques.
HOGO: Hide Objects Game Optimization

A number of metaphysical algorithms have been developed in recent years.Most of these algorithms are inspired by physical processes or living beings' behaviour.In this paper, a new algorithm namely "Hide Objects Game Optimization (HOGO)" is presented to obtain quasi-optimal solution.It is inspired by an old game and the searcher agents who try to find a hidden object in a given space.In this game, any player must notice the following points: (a) pay attention to the voices made by the coach for players, (b) get closer to the best player for whom the coach made the loudest voice, (c) take influence from the voices made by the coach for other players, (d) compare the new voice after a move with the old voice before the move and return back in case the voice gets lower.HOGO is tested on 23 well-known benchmark test functions and is compared with eight optimization algorithms: Genetic Algorithm, Particle Swarm Optimization, Gravitational Search Algorithm, Teaching Learning Based Optimization, Grey Wolf Optimizer, Grasshopper Optimization Algorithm, Spotted Hyena Optimizer, and Emperor Penguin Optimizer.The results and data obtained from applying HOGO and other said algorithms show that HOGO is able to provide better results in comparison with other well-known optimization algorithms.
A tuneable telecom wavelength entangled light emitting diode deployed in an installed fibre network

Entangled light emitting diodes based on semiconductor quantum dots are promising devices for security sensitive quantum network applications, thanks to their natural lack of multi photon-pair generation. Apart from telecom wavelength emission, network integrability of these sources ideally requires electrical operation for deployment in compact systems in the field. For multiplexing of entangled photons with classical data traffic, emission in the telecom O-band and tuneability to the nearest wavelength channel in compliance with coarse wavelength division multiplexing standards (20 nm channel spacing) is highly desirable. Here we show the first fully electrically operated telecom entangled light emitting diode with wavelength tuneability of more than 25nm, deployed in an installed fiber network. With the source tuned to 1310.00 nm, we demonstrate multiplexing of true single entangled photons with classical data traffic and achieve entanglement fidelities above 95% on an installed fiber in a city.
An Energy-Efficient Dual-Field Elliptic Curve Cryptography Processor for Internet of Things Applications

This brief presents an energy-efficient elliptic curve cryptography (ECC) processor for Internet of Things (IoT) security applications. The proposed processor supports dual-field computations, and employs various design techniques across the algorithm, architecture, and arithmetic circuit levels to minimize power and energy consumption. The proposed elliptic curve point multiplication (ECPM) algorithm employs signed binary representation (SBR) with the m-ary method to reduce both area and energy consumption, while avoiding attack from simple power analysis (SPA). In addition, the proposed hybrid modular arithmetic architecture effectively increases the hardware utilization to reduce both area and energy cost. Finally, the proposed processor uses an energy-efficient data flow to further minimize memory overhead for group operations. The proposed ECC processor achieves 51.6% and 50.5% lower energy consumption for each GF(p) and GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) ECPM operation, respectively, when compared to state-of-the-art ECC designs.
Analysis on the Influence of the Epidemic on the Education in China

The sudden outbreak of the novel corona virus, Covid-19, brought a lot of loss to China and the Chinese people.To contain the epidemic, people had to stay where they were.They could not go back to work or to school.Some dormitories in the universities were requisitioned temporarily as ward for Covid-19-infectious patient.The offline courses were due many reasons infeasible, what brought unexpected changes to education.With the analysis on the latest news about policies and relevant data, this paper focuses on the impact of the epidemic caused by Covid-19 on the education in China.In order to make the influence clearly and comprehensive, the education in China is divided into varies of parts, namely, compulsory education, extracurricular education and higher education, which further divided into study abroad and study inland.And the influence concentrates on the teaching and learning-effect, the transformation of the teaching forms, the employment of the students and the enrollment of the schools.The analysis result shows that the compulsory education is the less affected than higher education, because the students who are taking compulsory education do not face the employment pressure.Although online courses are lack of supervision, so that the learning effect of the students with poor self-control will get worse and the gap between good students and bad students will widen, after they return to school and take the normal offline courses, the "Matthew Effect" will be eliminated.In comparison, the employment and enrollment stress that the students and universities are suffering from will last for a long time.Besides, the extracurricular education also encounter irreversible loss, because companies that incapable to open online courses are forced to close.In addition, problems of the online courses platforms are found through the research.Professional teaching platforms are still a blank and urged to be developed.In a word, despite all the impact on the Chinese education, education sector is still an optimistic area because of the hope and potential brought and presented by the online education.
Pseudo-Image and Sparse Points: Vehicle Detection With 2D LiDAR Revisited by Deep Learning-Based Methods

Detecting and locating surrounding vehicles robustly and efficiently are essential capabilities for autonomous vehicles. Existing solutions often rely on vision-based methods or 3D LiDAR-based methods. These methods are either too expensive in both sensor pricing (3D LiDAR) and computation (camera and 3D LiDAR) or less robust in resisting harsh environment changes (camera). In this work, we revisit the LiDAR based approaches for vehicle detection with a less expensive 2D LiDAR by utilizing modern deep learning approaches. We aim at filling in the gap as few previous works conclude an efficient and robust vehicle detection solution in a deep learning way in 2D. To this end, we propose a learning based method with the input of pseudo-images, named Cascade Pyramid Region Proposal Convolution Neural Network (Cascade Pyramid RCNN), and a hybrid learning method with the input of sparse points, named Hybrid Resnet Lite. Experiments are conducted with our newly 2D LiDAR vehicle dataset recorded in complex traffic environments. Results demonstrate that the Cascade Pyramid RCNN outperforms state-of-the-art methods in accuracy while the proposed Hybrid Resnet Lite provides superior performance of the speed and lightweight model by hybridizing learning based and non-learning based modules. As few previous works conclude an efficient and robust vehicle detection solution with 2D LiDAR, our research fills in this gap and illustrates that even with limited sensing source from a 2D LiDAR, detecting obstacles like vehicles efficiently and robustly is still achievable.
Regulatory Sandboxes: A Cure for mHealth Pilotitis?

Mobile health (mHealth) and related digital health interventions in the past decade have not always scaled globally as anticipated earlier despite large investments by governments and philanthropic foundations. The implementation of digital health tools has suffered from 2 limitations: (1) the interventions commonly ignore the “law of amplification” that states that technology is most likely to succeed when it seeks to augment and not alter human behavior; and (2) end-user needs and clinical gaps are often poorly understood while designing solutions, contributing to a substantial decrease in usage, referred to as the “law of attrition” in eHealth. The COVID-19 pandemic has addressed the first of the 2 problems—technology solutions, such as telemedicine, that were struggling to find traction are now closely aligned with health-seeking behavior. The second problem (poorly designed solutions) persists, as demonstrated by a plethora of poorly designed epidemic prediction tools and digital contact-tracing apps, which were deployed at scale, around the world, with little validation. The pandemic has accelerated the Indian state’s desire to build the nation’s digital health ecosystem. We call for the inclusion of regulatory sandboxes, as successfully done in the fintech sector, to provide a real-world testing environment for mHealth solutions before deploying them at scale.
Underwater image enhancement framework and its application on an autonomous underwater vehicle platform

Underwater imaging has been increasingly employed in vision-based marine research. However, the inappropriate installation of a light source and the complex underwater environment will result in the uneven illumination and overexposure on the captured images. To address these issues, an underwater image enhancement framework for autonomous underwater vehicles platform is proposed, which consists of underwater light source optimization and illumination nonuniformity correction. The light source optimization method improves the imaging quality by computing an appropriate angle of the light casting. In this way, the center of the field of view is always well lit. In addition, an adaptive filter-based illumination correction algorithm is proposed to solve the uneven illumination caused by the artificial light source. During this process, image block segmentation and the measure of image enhancement index are applied to improve the adaptability and reduce the calculation errors of the filter parameters. A dataset with real underwater images collected under different natural conditions has been built and tested. The experimental results indicate that the proposed method is more adaptive and effective than the typical methods.
Improved software defect prediction using Pruned Histogram-based isolation forest

Software defect prediction (SDP) is a hot topic in the modern software engineering research community. It has been used for evaluating software quality and reliability and allocating limited testing resources effectively. Based on analyzing the software source code and development process and extracting critical metrics, many data mining and machine learning methods have been used for SDP. However, these existing learning methods have difficulty with handling the imbalanced data distribution of accumulated training dataset. Isolation forest, an anomaly detection method based on the ensemble learning, has been studied to deal with the imbalanced data distribution issue for obtaining high prediction performance. However, the isolation forest method suffers from a main drawback of slow convergence, which is caused by selecting the feature value at random during the process of building isolation trees. To conquer this problem, in this paper histogram is constructed for the value set of selected isolation feature helping identify feature values preferable to build isolation trees. Motivated by the “many could be better than all” principle in the ensemble learning, the ensemble pruning strategy is further employed to optimize the obtained isolation forest, leading to a novel SDP method named PHIForest (Pruned Histogram-based Isolation Forest) in this work. The proposed method can provide fast convergence through the histogram-based splitting feature value selection, and decrease the ensemble scale and improve prediction performance through the ensemble pruning. Comprehensive experiments conducted on ten real datasets are performed to demonstrate effectiveness of the proposed SDP method.
Detecting Malicious DNS over HTTPS Traffic in Domain Name System using Machine Learning Classifiers

This paper presents a systematic two-layer approach for detecting DNS over HTTPS (DoH) traffic and distinguishing Benign-DoH traffic from Malicious-DoH traffic using six machine learning algorithms. The capability of machine learning classifiers is evaluated considering their accuracy, precision, recall, and F-score, confusion matrices, ROC curves, and feature importance. The results show that LGBM and XGBoost algorithms outperform the other algorithms in almost all the classification metrics reaching the maximum accuracy of 100% in the classification tasks of layers 1 and 2. LGBM algorithms only misclassified one DoH traffic test as non-DoH out of 4000 test datasets. It has also found that out of 34 features extracted from the CIRA-CIC-DoHBrw-2020 dataset, SourceIP is the critical feature for classifying DoH traffic from non-DoH traffic in layer one followed by DestinationIP feature. However, only DestinationIP is an important feature for LGBM and gradient boosting algorithms when classifying Benign-DoH from Malicious-DoH traffic in layer 2.
Secure neural network watermarking protocol against forging attack

Abstract In order to protect the intellectual property of neural network, an owner may select a set of trigger samples and their corresponding labels to train a network, and prove the ownership by the trigger set without revealing the inner mechanism and parameters of the network. However, if an attacker is allowed to access the neural network, he can forge a matching relationship between fake trigger samples and fake labels to confuse the ownership. In this paper, we propose a novel neural network watermarking protocol against the forging attack. By introducing one-way hash function, the trigger samples used to prove ownership must form a one-way chain, and their labels are also assigned. By this way, an attacker without the right of network training is impossible to construct a chain of trigger samples or the matching relationship between the trigger samples and the assigned labels. Our experiments show that the proposed protocol can resist the watermark forgery without sacrificing the network performance.
A 117 Line 2D Digital Image Correlation Code Written in MATLAB

Digital Image Correlation (DIC) has become a popular tool in many fields to determine the displacements and deformations experienced by an object from images captured of the object. Although there are several publications which explain DIC in its entirety while still catering to newcomers to the concept, these publications neglect to discuss how the theory presented is implemented in practice. This gap in literature, which this paper aims to address, makes it difficult to gain a working knowledge of DIC, which is necessary in order to contribute towards its development. The paper attempts to address this by presenting the theory of a 2D, subset-based DIC framework that is predominantly consistent with state-of-the-art techniques, and discussing its implementation as a modular MATLAB code. The correlation aspect of this code is validated, showing that it performs on par with well-established DIC algorithms and thus is sufficiently reliable for practical use. This paper, therefore, serves as an educational resource to bridge the gap between the theory of DIC and its practical implementation. Furthermore, although the code is designed as an educational resource, its validation combined with its modularity makes it attractive as a starting point to develop the capabilities of DIC.
Weighted Generalized Cross-Validation-Based Regularization for Broad Learning System

The broad learning system (BLS) is an emerging flat network, which has demonstrated its outstanding performance in classification and regression problems. The regularization plays an important role in the performance of the BLS. In real applications, since the BLS network is usually expanded dynamically, a predetermined regularization parameter may reduce the performance of the network. Using a fixed regularization in some cases, the classification accuracy of the BLS decreases dramatically when we expand the network. To alleviate this problem, we propose a method that automatically finds appropriate regularization parameters for different datasets, which is based on the weighted generalized cross-validation (WGCV). The experimental results indicate that the WGCV method improves the performance of the BLS, and alleviates the accuracy decrease of the incremental learning algorithm.
A longitudinal study of static analysis warning evolution and the effects of PMD on software quality in Apache open source projects

Automated static analysis tools (ASATs) have become a major part of the software development workflow. Acting on the generated warnings, i.e., changing the code indicated in the warning, should be part of, at latest, the code review phase. Despite this being a best practice in software development, there is still a lack of empirical research regarding the usage of ASATs in the wild. In this work, we want to study ASAT warning trends in software via the example of PMD as an ASAT and its usage in open source projects. We analyzed the commit history of 54 projects (with 112,266 commits in total), taking into account 193 PMD rules and 61 PMD releases. We investigate trends of ASAT warnings over up to 17 years for the selected study subjects regarding changes of warning types, short and long term impact of ASAT use, and changes in warning severities. We found that large global changes in ASAT warnings are mostly due to coding style changes regarding braces and naming conventions. We also found that, surprisingly, the influence of the presence of PMD in the build process of the project on warning removal trends for the number of warnings per lines of code is small and not statistically significant. Regardless, if we consider defect density as a proxy for external quality, we see a positive effect if PMD is present in the build configuration of our study subjects.