Reviewerly/exHarmony huggingface dataset:
An Experimental Analysis of Data Annotation Methodologies for Emotion Detection in Short Text Posted on Social Media

Opinion mining techniques, investigating if text is expressing a positive or negative opinion, continuously gain in popularity, attracting the attention of many scientists from different disciplines. Specific use cases, however, where the expressed opinion is indisputably positive or negative, render such solutions obsolete and emphasize the need for a more in-depth analysis of the available text. Emotion analysis is a solution to this problem, but the multi-dimensional elements of the expressed emotions in text along with the complexity of the features that allow their identification pose a significant challenge. Machine learning solutions fail to achieve a high accuracy, mainly due to the limited availability of annotated training datasets, and the bias introduced to the annotations by the personal interpretations of emotions from individuals. A hybrid rule-based algorithm that allows the acquisition of a dataset that is annotated with regard to the Plutchik’s eight basic emotions is proposed in this paper. Emoji, keywords and semantic relationships are used in order to identify in an objective and unbiased way the emotion expressed in a short phrase or text. The acquired datasets are used to train machine learning classification models. The accuracy of the models and the parameters that affect it are presented in length through an experimental analysis. The most accurate model is selected and offered through an API to tackle the emotion detection in social media posts.
Privacy-preserving hierarchical deterministic key generation based on a lattice of rings in public blockchain

Blockchain has revolutionized numerous fields, which include financial services, health care, the Internet of things, academia and supply chain management. Blockchain technology enables us to have an immutable, distributed ledger for managing the transactions of untrusted users. However, the technology has many open challenges, such as privacy leaks, scalability, and energy consumption. User identity can be easily tracked using network analysis, as transactions are accessible to everyone, which is a serious concern of blockchain. In this paper, we propose a new efficient, privacy-preserving, and quantum-resistant key generation algorithm, namely, lattice-based hierarchical deterministic key generation (LB-HDKG), for maintaining user privacy in the public blockchain. The LB-HDKG scheme generates many cryptographic keys in a tree-like structure from a single seed to hide the links between transactions of the same user. Our proposal uses the lattice NTRU cryptosystem, the security of which relies on the shortest vector problem (SVP) and closest vector problem (CVP) over the polynomial ring. Operations on the lattice NTRU cryptosystem are efficient and secure against classical computers and quantum computers. Security and performance analyses of our scheme show that the model is more secure and efficient and should replace current models to safeguard data from quantum computers.
Liver segmentation with 2.5D perpendicular UNets

Liver and hepatic tumor segmentation is a crucial yet challenging step during the screening and diagnosis of liver illnesses. Currently, accurate 3D segmentation deep learning models are large, while the smaller 2D ones are generally less accurate due to their small receptive fields. To reduce model sizes and increase segmentation accuracy, we propose 2.5D Perpendicular-UNet to fuse the segmentation results of three perpendicular 2.5D Res-UNets in the task of liver and hepatic tumor segmentation. Data augmentation, loss functions, and post-processing steps are customizable with our model. With a larger receptive field in three dimensions, our model outperforms 2D UNet models in accuracy, achieving 0.962 and 0.735 Dice scores for liver and tumor segmentation on the liver tumor segmentation dataset. Being smaller than 3D models, our 2.5D P-UNet trains using less data and GPU memory. This enables it to be deployed on low-configuration hardware, expanding its potential use.
Short/medium term solar power forecasting of Chhattisgarh state of India using modified TLBO optimized ELM

The solar power generation (SPG) prediction is indispensable to establish a reliable and secure power grid. The intelligent and knowledgeable techniques are required to forecast the most nonlinear and volatile SPG due to its dependency on fluctuating weather conditions (solar irradiance and temperature). In this work, an optimized Extreme Learning Machine (ELM) is employed to forecast real-time SPG of Chhattisgarh state of India by conceding weather conditions. The performance of ELM approach is enhanced by exploring relevant parameters such as weights, biases, and numbers of hidden layers. It requires computational techniques which are proficient enough to deal with high dimensional and complex problems. Teaching Learning Based Optimization (TLBO) technique is modified with two novel approaches to enhance the exploration and exploitation proficiency of TLBO algorithm. The collaboration of modified TLBO (MTLBO) and optimizable ELM technique is implemented to forecast SPG for four different case studies such as an hour ahead, a day ahead, a month ahead and three months ahead forecasting. The performance measures such as mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), mean arctangent absolute percentage error (MAAPE), and correlation of determination (R2) are used to demonstrate the performance of proposed approach. Diebold-Mariano (DM) test and forecasting effectiveness are employed to hypothetically corroborate the capability of MTLBO based ELM model to outperform different optimization based ELM, ELM (with randomly fixed weights and biases) and ANN models. The simulation results contribute the evidence of excel performance of proposed approach for SPG forecasting.
A Survey of Automatic Contact Tracing Approaches Using Bluetooth Low Energy

To combat the ongoing Covid-19 pandemic, many new ways have been proposed on how to automate the process of finding infected people, also called contact tracing . A special focus was put on preserving the privacy of users. Bluetooth Low Energy as base technology has the most promising properties, so this survey focuses on automated contact tracing techniques using Bluetooth Low Energy. We define multiple classes of methods and identify two major groups: systems that rely on a server for finding new infections and systems that distribute this process. Existing approaches are systematically classified regarding security and privacy criteria.
Security reinforcement for Ethereum virtual machine

Smart contracts are more sensitive from a security perspective than other software due to several reasons. First, smart contracts are immutable thus cannot be easily patched once deployed. Second, smart contracts are directly tied to payments and can hold millions of dollars’ worth of digital currencies. Third, smart contracts are still a new practice thus do not have best coding practices and development lifecycles tailored for decentralized apps yet. Even though several testing and verification tools have been developed, smart contract vulnerabilities remain a clear and present danger. In this paper, we present an approach that is different from existing ones that attempt to eliminate vulnerabilities from smart contracts. Instead, we fortify Ethereum virtual machines (EVM) to stop dangerous transactions once vulnerabilities are detected in real-time. Since proving programs written in Turing-complete languages is undecidable, our approach complements current approaches by catching vulnerabilities and interrupts their executions during runtime. We have implemented our reinforcement on two widely used EVMs (js-evm and FISCO-BCOS-evm). The reinforced EVMs detects and interrupts all the vulnerabilities, 20% of them missed by testing tools, in 100 real smart contracts. Our approach is practical with less than 34% overhead. In fact, the reinforced FISCO-BCOS-evm has been integrated into the official release of FISCO-BCOS adopted by a large Chinese bank — WeBank.
Isolated Handwritten Pashto Character Recognition Using a K-NN Classification Tool based on Zoning and HOG Feature Extraction Techniques

Handwritten text recognition is considered as the most challenging task for the research community due to slight change in different characters’ shape in handwritten documents. The unavailability of a standard dataset makes it vaguer in nature for the researchers to work on. To address these problems, this paper presents an optical character recognition system for the recognition of offline Pashto characters. The problem of the unavailability of a standard handwritten Pashto characters database is addressed by developing a medium-sized database of offline Pashto characters. This database consists of 11352 character images (258 samples for each 44 characters in a Pashto script). Enriched feature extraction techniques of histogram of oriented gradients and zoning-based density features are used for feature extraction of carved Pashto characters. K-nearest neighbors is considered as a classification tool for the proposed algorithm based on the proposed feature sets. A resultant accuracy of 80.34% is calculated for the histogram of oriented gradients, while for zoning-based density features, 76.42% is achieved using 10-fold cross validation.
HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset

High dynamic range (HDR) video reconstruction from sequences captured with alternating exposures is a very challenging problem. Existing methods often align low dynamic range (LDR) input sequence in the image space using optical flow, and then merge the aligned images to produce HDR output. However, accurate alignment and fusion in the image space are difficult due to the missing details in the over-exposed regions and noise in the under-exposed regions, resulting in unpleasing ghosting artifacts. To enable more accurate alignment and HDR fusion, we introduce a coarse-to-fine deep learning framework for HDR video reconstruction. Firstly, we perform coarse alignment and pixel blending in the image space to estimate the coarse HDR video. Secondly, we conduct more sophisticated alignment and temporal fusion in the feature space of the coarse HDR video to produce better reconstruction. Considering the fact that there is no publicly available dataset for quantitative and comprehensive evaluation of HDR video reconstruction methods, we collect such a benchmark dataset, which contains 97 sequences of static scenes and 184 testing pairs of dynamic scenes. Extensive experiments show that our method outperforms previous state-of-the-art methods. Our code and dataset can be found at https://guanyingc.github.io/DeepHDRVideo.
UTF: Upgrade transfer function for binary meta-heuristic algorithms

In the real world, many optimization problems are discrete and very complex to solve. Some of them are in the class of NP-hard problems and their search spaces grow exponentially with the problem size. As a result, an exhaustive search will be impractical using exact algorithms. In the last decades, meta-heuristic algorithms as approximate algorithms have shown superior performance in solving these problems. The majority of these algorithms have been designed for continuous search spaces and are not able to solve binary optimization problems. Therefore, a transfer function is applied to convert the continuous search space to the binary one. The performance of such binary algorithms depends on their ability of exploration, exploitation and transfer function. Several transfer functions have been introduced so far but they have shown poor exploration and exploitation in solving some problems. In this study, a novel adaptive transfer function, based on two linear functions, is proposed to overcome the shortcomings of existing transfer functions. The proposed method called upgrade transfer function (UTF) adapts itself during running the algorithm to switch from exploration to exploitation. This capability also covers disadvantages of metaheuristic algorithms in terms of poor exploration and exploitation. The performance of UTF has been evaluated by three discrete optimization problems: function optimization, feature selection and the 0–1 multi-knapsack problem (MKP). The results of binary particle swarm optimization (BPSO), binary artificial bee colony (BABC) and several improved BPSO and BABC have been compared with those of UTF–BPSO and UTF–BABC using function optimization problems. Also, the efficiency of UTF–BPSO and UTF–BABC and some binary meta-heuristic algorithms such as binary salp swarm algorithm (BSSA) and binary gray wolf optimization (bGWO), binary dragon algorithm (BDA), binary multi-neighborhood artificial bee colony (BMNABC), binary hybrid topology particle swarm optimization quadratic interpolation (BHTPSO-QI), binary ant lion optimizer (bALO) and binary gravitational search algorithm (BGSA) have been evaluated by feature selection problems. Moreover, UTF and seventeen transfer functions have been applied in original PSO, ABC, SSA and GWO algorithms to solve low and high dimensions 0–1 MKP benchmark instances. The results showed that the new transfer function significantly enhances the performance of algorithms to achieve the best solution in the binary search space.
Convolution Neural Network based lossy compression of hyperspectral images

The large size of hyperspectral imaging poses a significant threat to its potential use in real life due to the abundant information stored in it. The use of deep learning for such data processing is visible in recent applications. In this work, we propose a lossy hyperspectral image compression algorithm based on the concept of autoencoders. It uses a combination of the convolution layer and max-pooling layer to reduce the dimensions of the input image and generate a compressed image. The original image with some loss of information is reconstructed using transpose convolution layer that uses reverse of the procedure used by the encoder. The compressed image has been entropy coded using an adaptive arithmetic coder for transmission or storage application. The method provides an improvement of 28% in PSNR with 21 times increment in the compression ratio. The effect of compression on classification has also been evaluated in the experiment using state of art classification algorithm. Negligible difference in classification accuracy was obtained that proves the effectiveness of the proposed algorithm.
Conceptualizing dimensions and a model for digital pedagogy

The global COVID-19 pandemic has caused a rising interest in the use of digital pedagogies and the need to teach remotely. This article aims to conceptualize the dimensions described below and offer a model for digital pedagogy to provide tools for using digital technologies in teaching. The model for digital pedagogy is discussed in terms of three dimensions: 1) pedagogical orientation; 2) pedagogical practices; and 3) the digital pedagogical competencies it provides for the teacher. This study examines how the dimensions of digital pedagogy are presented in the current research literature. The research is conducted through a systematic literature review surveying articles published in the years 2014 to 2019; a total of 12 articles are included in the review. The findings suggest that, first, in many cases, pedagogical orientation is labeled as socio-constructivist and student-centered. Second, pedagogical practices are the methods used to promote students’ learning; they involve, for example, collaboration and social knowledge construction. Finally, in addition to technological, pedagogical, and content knowledge, teachers’ success in blending digital technologies into their teaching is improved by high self-efficacy and strong peer-collaboration skills.
Automated Software Vulnerability Detection Based on Hybrid Neural Network

Vulnerabilities threaten the security of information systems. It is crucial to detect and patch vulnerabilities before attacks happen. However, existing vulnerability detection methods suffer from long-term dependency, out of vocabulary, bias towards global features or local features, and coarse detection granularity. This paper proposes an automatic vulnerability detection framework in source code based on a hybrid neural network. First, the inputs are transformed into an intermediate representation with explicit structure information using lower level virtual machine intermediate representation (LLVM IR) and backward program slicing. After the transformation, the size of samples and the size of vocabulary are significantly reduced. A hybrid neural network model is then applied to extract high-level features of vulnerability, which learns features both from convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The former is applied to learn local vulnerability features, such as buffer size. Furthermore, the latter is utilized to learn global features, such as data dependency. The extracted features are made up of concatenated outputs of CNN and RNN. Experiments are performed to validate our vulnerability detection method. The results show that our proposed method achieves excellent results with F1-scores of 98.6% and accuracy of 99.0% on the SARD dataset. It outperforms state-of-the-art methods.
Synthesis of Mammogram From Digital Breast Tomosynthesis Using Deep Convolutional Neural Network With Gradient Guided cGANs

Synthetic digital mammography (SDM), a 2D image generated from digital breast tomosynthesis (DBT), is used as a potential substitute for full-field digital mammography (FFDM) in clinic to reduce the radiation dose for breast cancer screening. Previous studies exploited projection geometry and fused projection data and DBT volume, with different post-processing techniques applied on re-projection data which may generate different image appearance compared to FFDM. To alleviate this issue, one possible solution to generate an SDM image is using a learning-based method to model the transformation from the DBT volume to the FFDM image using current DBT/FFDM combo images. In this study, we proposed to use a deep convolutional neural network (DCNN) to learn the transformation to generate SDM using current DBT/FFDM combo images. Gradient guided conditional generative adversarial networks (GGGAN) objective function was designed to preserve subtle MCs and the perceptual loss was exploited to improve the performance of the proposed DCNN on perceptual quality. We used various image quality criteria for evaluation, including preserving masses and MCs which are important in mammogram. Experiment results demonstrated progressive performance improvement of network using different objective functions in terms of those image quality criteria. The methodology we exploited in the SDM generation task to analyze and progressively improve image quality by designing objective functions may be helpful to other image generation tasks.
Investigation of End-to-End Speaker-Attributed ASR for Continuous Multi-Talker Recordings

Recently, an end-to-end (E2E) speaker-attributed automatic speech recognition (SA-ASR) model was proposed as a joint model of speaker counting, speech recognition and speaker identification for monaural overlapped speech. It showed promising results for simulated speech mixtures consisting of various numbers of speakers. However, the model required prior knowledge of speaker profiles to perform speaker identification, which significantly limited the application of the model. In this paper, we extend the prior work by addressing the case where no speaker profile is available. Specifically, we perform speaker counting and clustering by using the internal speaker representations of the E2E SA-ASR model to diarize the utterances of the speakers whose profiles are missing from the speaker inventory. We also propose a simple modification to the reference labels of the E2E SA-ASR training which helps handle continuous multi-talker recordings well. We conduct a comprehensive investigation of the original E2E SA-ASR and the proposed method on the monaural LibriCSS dataset. Compared to the original E2E SA-ASR with relevant speaker profiles, the proposed method achieves a close performance without any prior speaker knowledge. We also show that the source-target attention in the E2E SA-ASR model provides information about the start and end times of the hypotheses.
Considerations about learning Word2Vec

Abstract Despite the large diffusion and use of embedding generated through Word2Vec, there are still many open questions about the reasons for its results and about its real capabilities. In particular, to our knowledge, no author seems to have analysed in detail how learning may be affected by the various choices of hyperparameters. In this work, we try to shed some light on various issues focusing on a typical dataset. It is shown that the learning rate prevents the exact mapping of the co-occurrence matrix, that Word2Vec is unable to learn syntactic relationships, and that it does not suffer from the problem of overfitting. Furthermore, through the creation of an ad-hoc network, it is also shown how it is possible to improve Word2Vec directly on the analogies, obtaining very high accuracy without damaging the pre-existing embedding. This analogy-enhanced Word2Vec may be convenient in various NLP scenarios, but it is used here as an optimal starting point to evaluate the limits of Word2Vec.
Participation Is not a Design Fix for Machine Learning

This paper critiques popular modes of participation in design practice and machine learning. It examines three existing kinds of participation in design practice and machine learning participation as work, participation as consultation, and as participation as justice – to argue that the machine learning community must become attuned to possibly exploitative and extractive forms of community involvement and shift away from the prerogatives of context independent scalability. Cautioning against “participation washing”, it argues that the notion of “participation” should be expanded to acknowledge more subtle, and possibly exploitative, forms of community involvement in participatory machine learning design. Specifically, it suggests that it is imperative to recognize design participation as work; to ensure that participation as consultation is context-specific; and that participation as justice must be genuine and long term. The paper argues that such a development can only be scaffolded by a new epistemology around design harms, including, but not limited to, in machine learning. To facilitate such a development, the paper suggests developing we argue that developing a cross-sectoral database of design participation failures that is cross-referenced with socio-structural dimensions and highlights “edge cases” that can and must be learned from.
Simplified Self-Attention for Transformer-Based end-to-end Speech Recognition

Transformer models have been introduced into end-to-end speech recognition with state-of-the-art performance on various tasks owing to their superiority in modeling long-term dependencies. However, such improvements are usually obtained through the use of very large neural networks. Transformer models mainly include two submodules - position-wise feedforward layers and self-attention (SAN) layers. In this paper, to reduce the model complexity while maintaining good performance, we propose a simplified self-attention (SSAN) layer which employs FSMN memory blocks instead of projection layers to form query and key vectors for transformer-based end-to-end speech recognition. We evaluate the SSAN-based and the conventional SAN-based transformers on the public AISHELL-1, internal 1000-hour and 20,000-hour large-scale Mandarin tasks. Results show that our proposed SSAN-based transformer model can achieve over 20% reduction in model parameters and 6.7% relative CER reduction on the AISHELL-1 task. With impressively 20% parameter reduction, our model shows no loss of recognition performance on the 20,000-hour large-scale task.