Reviewerly/exHarmony huggingface dataset:
Surveying the impacts of COVID-19 on the perceived productivity of Brazilian software developers

The new coronavirus pandemic has forced companies around the world to adopt measures to stop the spread of this virus and the associated COVID-19 disease. In Brazil, this was no different with software development professionals having to work from home (WFH) due to the need of social isolation. WFH was an imposition and was a new experience for many professionals, generating unpredictable impacts. In this article, we present the results of a survey to understand the impacts of social isolation on the perceived productivity of Brazilian software developers. Data were collected during March and April/2020 with a sample of 413 respondents. The results indicate developers' perceived productivity has increased, mainly because there are fewer interruptions throughout the day. In addition, most respondents informed that they wish to continue working most of the time remotely. We contrast our results with another study published on this topic.
An Area Aware Accelerator for Elliptic Curve Point Multiplication

This work presents a hardware accelerator, for the optimization of latency and area at the same time, to improve the performance of point multiplication process in Elliptic Curve Cryptography. In order to reduce the overall computation time in the proposed 2-stage pipelined architecture, a rescheduling of point addition and point doubling instructions is performed along with an efficient use of required memory locations. Furthermore, a 41-bit multiplier is also proposed. Consequently, the FPGA and ASIC implementation results have been provided. The performance comparison with state-of-the-art implementations, in terms of latency and area, proves the significance of the proposed accelerator.
Comparing Single-modal and Multimodal Interaction in an Augmented Reality System

Multimodal interaction is expected to offer better user experience in Augmented Reality (AR), and thus becomes a recent research focus. However, due to the lack of hardware-level support, most existing works only combine two modalities at a time, e.g., gesture and speech. Gaze-based interaction techniques have been explored for the screen-based application, but rarely been used in AR systemsy configurable augmented reality system. In this paper, we propose a multimodal interactive system that integrates gaze, gesture and speech in a flexibly configurable augmented reality system. Our lightweight head-mounted device supports accurate gaze tracking, hand gesture recognition and speech recognition simultaneously. More importantly, the system can be easily configured into different modality combinations to study the effects of different interaction techniques. We evaluated the system in the table lamps scenario, and compared the performance of different interaction techniques. The experimental results show that the Gaze+Gesture+Speech is superior in terms of performance.
Deeplab_v3_plus-net for Image Semantic Segmentation with Channel Compression

The task of semantic segmentation is to correctly classify every pixel of one image. Benefit from the full convolutional neural network (FCN), the image segmentation task has step into a new stage. Since Google has shown its exploration of semantic segmentation, and proposes EncoderDecoder algorithm with Atrous Separable Convolution (Deeplab_v3_plus) method for enhancing the performance of image segmentation. Following the previous work, we will lose a great deal of detail information in the process of downsampling. Therefore, we propose to supplement the compressed channel information into the different levels feature map to compensate the details lost in the process of down sampling. This paper introduces how do we compress channel information and channel information fusion. We present a Channel Compression-Encoder-Decoder with Atrous Separable Convolution Net (CC-Deeplab_v3_plus), Eventually our CC-Deeplab_v3_plus relative to Deeplab_v3_plus in the PASCAL VOC2012 and own pest datasets get 1% Mean Intersection Over Union (MIUO) boost.
High-Precision Detection Method for Structure Parameters of Catenary Cantilever Devices Using 3-D Point Cloud Data

This article proposes an automatic high-precision detection method for structure parameters of catenary cantilever devices (SPCCDs) using 3-D point cloud data. The steps of the proposed detection method are: 1) segmenting and recognizing the components of the catenary cantilever devices, 2) extracting the detection plane and backbone component axis of catenary cantilever devices, and 3) detecting the SPCCD. The effective segmentation of components is critical for structure parameter detection. A point cloud segmentation and recognition method based on three-dimensional convolutional neural networks (3-D CNNs) is introduced to determine the different components of the catenary cantilever devices. Compared with traditional unsupervised clustering procedures for point cloud segmentation, the proposed method can improve the segmentation accuracy, does not require complex tuning procedures of parameters, and improves robustness and stability. Additionally, the segmentation method defines a recognition function, facilitating the analysis of the structural relationship between objects. Furthermore, we proposed an improved projection random sample consensus (RANSAC) method, which can effectively divide the detection plane of catenary cantilever devices to solve the multicantilever device occlusion problem. With RANSAC, it is also possible to precisely extract the backbone component axis and enhance parameter detection accuracy. The experimental results show that the structure angle and steady arm slope's error accuracy can achieve 0.1029° and 1.19%, respectively, which indicates the proposed approach can precisely detect the SPCCD.
Scalable Byzantine Reliable Broadcast

Byzantine reliable broadcast is a powerful primitive that allows a set of processes to agree on a message from a designated sender, even if some processes (including the sender) are Byzantine. Existing broadcast protocols for this setting scale poorly, as they typically build on quorum systems with strong intersection guarantees, which results in linear per-process communication and computation complexity.We generalize the Byzantine reliable broadcast abstraction to the probabilistic setting, allowing each of its properties to be violated with a fixed, arbitrarily small probability. We leverage these relaxed guarantees in a protocol where we replace quorums with stochastic samples. Compared to quorums, samples are significantly smaller in size, leading to a more scalable design. We obtain the first Byzantine reliable broadcast protocol with logarithmic per-process communication and computation complexity.We conduct a complete and thorough analysis of our protocol, deriving bounds on the probability of each of its properties being compromised. During our analysis, we introduce a novel general technique that we call adversary decorators. Adversary decorators allow us to make claims about the optimal strategy of the Byzantine adversary without imposing any additional assumptions. We also introduce Threshold Contagion, a model of message propagation through a system with Byzantine processes. To the best of our knowledge, this is the first formal analysis of a probabilistic broadcast protocol in the Byzantine fault model. We show numerically that practically negligible failure probabilities can be achieved with realistic security parameters.
Contrastive Adversarial Domain Adaptation Networks for Speaker Recognition

Domain adaptation aims to reduce the mismatch between the source and target domains. A domain adversarial network (DAN) has been recently proposed to incorporate adversarial learning into deep neural networks to create a domain-invariant space. However, DAN's major drawback is that it is difficult to find the domain-invariant space by using a single feature extractor. In this article, we propose to split the feature extractor into two contrastive branches, with one branch delegating for the class-dependence in the latent space and another branch focusing on domain-invariance. The feature extractor achieves these contrastive goals by sharing the first and last hidden layers but possessing decoupled branches in the middle hidden layers. For encouraging the feature extractor to produce class-discriminative embedded features, the label predictor is adversarially trained to produce equal posterior probabilities across all of the outputs instead of producing one-hot outputs. We refer to the resulting domain adaptation network as "contrastive adversarial domain adaptation network (CADAN)." We evaluated the embedded features' domain-invariance via a series of speaker identification experiments under both clean and noisy conditions. Results demonstrate that the embedded features produced by CADAN lead to a 33% improvement in speaker identification accuracy compared with the conventional DAN.
Secure and Efficient Image Compression-Encryption Scheme Using New Chaotic Structure and Compressive Sensing

The rapid development of the Internet leads to a surge in the amount of information transmission and brings many security problems. For multimedia information transmission, especially digital images, it is necessary to compress and encrypt at the same time. The emergence of compressive sensing solves this problem. Compressive sensing can compress and encrypt at the same time, which can not only reduce the transmission bandwidth of the network but also improve the security of the system. However, when using compressive sensing encryption, the whole measurement matrix needs to be stored, and the compressive sensing can be combined with a chaotic system, so only the generation parameters of the matrix need to be stored, and the security of the system can be further improved by using the sensitivity of the chaotic system. This paper introduces a secure and efficient image compression-encryption scheme using a new chaotic structure and compressive sensing. The chaotic map used in the scheme is generated by our new and universal chaotic structure, which not only expands the chaotic range of the chaotic system but also improves the performance of the chaotic system. After analyzing the performance comparison of traditional one-dimensional chaotic maps and some existing methods, the image compression-encryption scheme based on a new chaotic structure and compressive sensing has a good encryption effect and large keyspace, which can resist brute force attack and statistical attack.
Sequential Model Optimization for Software Effort Estimation

Many methods have been proposed to estimate how much effort is required to build and maintain software. Much of that research tries to recommend a single method – an approach that makes the dubious assumption that one method can handle the diversity of software project data. To address this drawback, we apply a configuration technique called “ROME” (Rapid Optimizing Methods for Estimation), which uses sequential model-based optimization (SMO) to find what configuration settings of effort estimation techniques work best for a particular data set. We test this method using data from 1161 traditional waterfall projects and 120 contemporary projects (from GitHub). In terms of magnitude of relative error and standardized accuracy, we find that ROME achieves better performance than the state-of-the-art methods for both traditional waterfall and contemporary projects. In addition, we conclude that we should not recommend <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">one</i> method for estimation. Rather, it is better to search through a wide range of different methods to find what works best for the local data. To the best of our knowledge, this is the largest effort estimation experiment yet attempted and the only one to test its methods on traditional waterfall and contemporary projects.
A Survey on the E-learning platforms used during COVID-19

E-learning is globalized in a short period of time due to the physical distancing requirement for curbing the spread of COVID-19. There are two major challenges imposed by the migration from face-to-face learning to E-learning: live lecturing and lab sessions. In this survey, we studied the E-learning platforms used in the COVID-19 pandemic: learning management systems or course management systems, tools for live-lecturing and tools for lab sessions. We found that this migration is quite smooth with contemporary technologies and the Internet. Prescriptive suggestions are also provided for this migration. For the future trend of education, it is expected that E-learning will become a preferable education and learning style in the future, especially for self-study and life-long learning.
Unsupervised cross-domain named entity recognition using entity-aware adversarial training

The success of neural network based methods in named entity recognition (NER) is heavily relied on abundant manual labeled data. However, these NER methods are unavailable when the data is fully-unlabeled in a new domain. To address the problem, we propose an unsupervised cross-domain model which leverages labeled data from source domain to predict entities in unlabeled target domain. To relieve the distribution divergence when transferring knowledge from source to target domain, we apply adversarial training. Furthermore, we design an entity-aware attention module to guide the adversarial training to reduce the discrepancy of entity features between different domains. Experimental results demonstrate that our model outperforms other methods and achieves state-of-the-art performance.
A Provably Secure Lightweight Subtree-Based Short Signature Scheme With Fuzzy User Data Sharing for Human-Centered IoT

Internet of Things (IoT) is made up of various smart devices for the exchange of sensed data through online services.Direct contact with people through smart devices to define parameters for healthcare and send them to a centralized repository.At the time of data exchange, messages need to be secure between a source (sender) and target (receiver) in order to confront human malicious attacks.Various signature-based schemes are presented in the literature to provide secure communication.Smart apps, however, require lightweight activities by maintaining critical security strengths.The key challenge in signature-based methods is more incurred computational expense for signing and checking process involving large numbers.In this article, a new lightweight provably secure partial discrete logarithm (DL) based subtree-based short signature with fuzzy user data sharing for human-centered IoT systems is introduced and it's security analysis is demonstrated on random oracle (RO) model.The presented scheme provides assurance of better security than other standing short-signature schemes.For low-storage, low-computation environments and low-bandwidth communication, the presented new provably secure and lightweight subtree-based short-signature scheme is needed.The results demonstrate the strength of proposed scheme, as opposed to existing works.
Iterative closest graph matching for non-rigid 3D/2D coronary arteries registration

Background and objective Fusion of the preoperative computed tomography angiography and intraoperative X-ray angiography images can considerably enhance the visual perception of physicians during percutaneous coronary interventions. This technique can provide 3D information of the arteries and reduce the uncertainty of 2D guidance images. For this purpose, 3D/2D vascular registration with high accuracy and robustness is crucial for performing accurate surgery. Methods In this study, we propose an iterative closest graph matching (ICGM) method that utilizes an alternative iteration framework including correspondence and transformation phases. A coarse-to-fine matching approach based on redundant graph matching is proposed for the correspondence phase. The transformation phase involves rigid and non-rigid transformations, in which rigid transformation is calculated using a closed-form solution, and non-rigid transformation is achieved using a statistical shape model established from a synthetic deformation dataset. Results The proposed method is evaluated and compared with nine state-of-the-art methods on simulated data and clinical datasets. Experiments demonstrate that our method is insensitive to the pose of data and robust to noise and deformation. Moreover, it outperforms other methods in terms of registering real data. Conclusions Given its high capture range, the proposed method can register 3D vessels without prior initialization in clinical practice.
Vehicle-Related Scene Segmentation Using CapsNets

Understanding of traffic scenes is a significant research problem in computer vision. In this paper, we present and implement a robust scene segmentation model by using capsule network (CapsNet) as a basic framework. We collected a large number of image samples related to Auckland traffic scenes of the motorway and labelled the data for multiple classifications. The contribution of this paper is that our model facilitates a better scene understanding based on matrix representation of pose and spatial relationship. We take a step forward to effectively solve the Picasso problem. The methods are based on deep learning and reduce human manipulation of data by completing the training process using only a small size of training data. Our model has the preliminary accuracy up to 74.61% based on our own dataset.
Toxicity Detection on Bengali Social Media Comments using Supervised Models

Social media playing an indispensable role in our daily life providing a public platform to share opinions including threats, spam and vulgar words often referred to as toxic comments. This type of expression depicts the anti-social behavior of the commentators which may hamper the online atmosphere. Filtering such toxic comments by handcrafting rules is cumbersome because they are unstructured and often include misspelled obscene words. Automated machine learning-based models to classify such toxic comments constitute a part of Sentiment Analysis and they are extensively used for the English language; showing promising results than statistical models. Though Bengali is a widely spoken language around the globe, little research works have been done to detect toxic comments in this language. Hence in this scholarly manuscript, we provide a comparative analysis of five supervised learning models (Naive Bayes, Support Vector Machines, Logistic Regression, Convolutional Neural Network, and Long Short Term Memory) to detect toxic Bengali comments from an annotated publicly available dataset. As our research finding, we demonstrate that both the deep learning-based models have outperformed other classifiers by 10% margin where Convolutional Neural Network achieved the highest accuracy of 95.30%.
Non-revisiting stochastic search revisited: Results, perspectives, and future directions

The non-revisiting genetic algorithm (NrGA) uses the entire search history with parameter-less adaptive mutation to significantly enhance search performance. In the past decade, a family of non-revisiting stochastic search (NrSS) methods has been developed. Using the entire (or partial) search history to assist evolutionary computation can achieve not only the goal of duplicated solution prevention, but also other utilizations such as adaptive parameter-less local search and fitness landscape estimation. In this survey, the focus is on the memory-assisted stochastic search techniques that store the search history in a binary space partitioning (BSP) tree. First, the basic NrGA is reviewed. Then, the development of the family of NrSS is reviewed from three aspects: 1) the basic NrSS algorithms, 2) conceptual extension of non-revisit and usage of the search history as novel operators and estimators, and 3) application of NrSS to different problem types, such as multi-objective problems, dynamic problems, and multi-modal problems. A comprehensive classification of search history-assisted algorithms suggests that the cumulative historical search information can be developed in a more functional manner; for example, a BSP tree can be simultaneously used for revisit prevention, fitness landscape estimation, and adaptive operation. Both the application on real-world problems and the theoretical analysis of NrSS are reviewed. Possible future work suggestions include a deeper understanding of the non-revisiting scheme in theory, a more comprehensive functional usage of search history, and a closer cooperation with data-driven techniques.
Abusive Comments Detection in Bangla-English Code-mixed and Transliterated Text

The comment section in public websites, while reflecting public opinion and enabling people to provide constructive criticism or to show appreciation, can be viewed by some people as a stage to use vulgar and offensive words without any consequences. With the rising popularity of micro-blogging websites like Facebook, Twitter etc., Bangla Language speakers' tendency to use code-mixing and transliteration is increasing as well. Manually checking and removing abusive comments from public websites can get tedious, which is undesirable in the present day of technological automation. In this paper, we propose a method to detect abusive comments using Machine Learning algorithms. This paper works not only with Bangla text but also with Bangla-English code-mixed text and transliterated Bangla text. The proposed method involves great amount of preprocessing as a result of people's disregard for correct spelling, grammar and punctuation when it comes to writing comments on the internet. For the dataset, we collected comments from public Facebook pages along with the number of likes they got. For features, we used Unigrams, Bigrams, number of likes, emojis along with their categories, sentiment scores, offensive and threatening words used in the comments, detected using our proposed algorithm, and the number of abusive words in each comment. The aforementioned algorithm can detect profanitypes too. After experimenting with three Machine Learning algorithms, namely Support Vector Machine, Random Forest, and Adaboost, the proposed method achieved a highest accuracy of 72.14%.
From Arithmetic to Logic based AI: A Comparative Analysis of Neural Networks and Tsetlin Machine

Neural networks constitute a well-established design method for current and future generations of artificial intelligence. They depends on regressed arithmetic between perceptrons organized in multiple layers to derive a set of weights that can be used for classification or prediction. Over the past few decades, significant progress has been made in low-complexity designs enabled by powerful hardware/software ecosystems. Built on the foundations of finite-state automata and game theory, Tsetlin Machine is increasingly gaining momentum as an emerging artificial intelligence design method. It is fundamentally based on propositional logic based formulation using booleanized input features. Recently developed Tsetlin Machine hardware architecture has demonstrated competitive performance and accuracy as well as opportunities for by-design energy efficiency and explainability. In this paper, we investigate these two architectures closely and perform a comprehensive, comparative analysis considering their architectural subtleties implemented in low-level C language and ignoring any specialized implementations. We study the impact of hyperparameters on both arithmetic and logic basis of learning in terms of performance, accuracy and energy efficiency. We show that Tsetln Machine consistently outperforms artificial neural network in terms of learning convergence and energy efficiency by up to 15×at the cost of higher energy consumption per epoch.