Reviewerly/exHarmony huggingface dataset:
Dynamic proposal sampling for weakly supervised object detection

It is challenging to optimize object detectors with only image-level annotations because the target objects are often surrounded by a large number of background clutters. Many existing approaches tackle this problem through object proposal sampling. However, the collected positive proposals are either low in precision or lack of diversity, and the strategy of collecting negative proposals is not carefully designed, neither. In this context, the primary contribution of this work is to improve weakly supervised detection (WSD) with a dynamic proposal sampling (DPS) strategy. The proposed method collects purified positive training samples by progressively removing confident background clutters, and selects discriminative negative samples by mining class-specific hard proposals. To discover erratic number of confident proposals for different images and categories in varying training phase, we introduce class-specific probabilty accumulation score to measure the image complexity and the quality of learned object detectors, and adjust the number of sampled proposals accordingly. This proposal sampling procedure is integrated into a CNN-based WSD framework, and can be performed in each stochastic gradient descent mini-batch during training. Extensive evaluation results on PASCAL VOC 2007, VOC 2010 and VOC 2012 datasets are presented, which demonstrate that the proposed method effectively improves WSD.
Multi-party transaction framework for drone services based on alliance blockchain in smart cities

With the rapid development of drone technology, drones are frequently used in all aspects of urban safety, such as emergency disaster monitoring, public safety monitoring, traffic monitoring, violation monitoring and so on, which further speeds up the pace of smart city construction. For small and medium-sized cities, the cost of buying drone is too high, and renting drone is more cost-effective. However, the leasing transactions between government departments and drone leasing companies are faced with problems such as distrust, price disputes, and increased costs caused by third-party participation. To solve these problems, this paper proposes a multi-party secure and convenient transaction framework based on alliance blockchain, which helps to build trust between drone leasing companies and government departments without worrying about privacy leakage and transaction data tampering. In addition, an intelligent pricing strategy based on contract theory is designed, which can not only maximize the utility of government departments, but also encourage drone companies to actively participate. Simulation analysis shows that the proposed drone services transaction mechanism based on blockchain and intelligent pricing strategy is very effective.
Understanding Everyday Experiences of Reminiscence for People with Blindness: Practices, Tensions and Probing New Design Possibilities

There is growing attention in the HCI community on how technology could be designed to support experiences of reminiscence on past life experiences. Yet, this research has largely overlooked people with blindness. We present a study that aims to understand everyday experiences of reminiscence for people with blindness. We conducted a qualitative study with 9 participants with blindness to understand their personal routines, wishes and desires, and challenges and tensions regarding the experience of reminiscence. Findings are interpreted to discuss new possibilities that offer starting points for future design initiatives and openings for collaboration aimed at creating technology to better support the practices of capturing, sharing, and reflecting on significant memories of the past.
Evaluating Python, C++, JavaScript and Java Programming Languages Based on Software Complexity Calculator (Halstead Metrics)

Abstract Various software organizations used software metrics to assessing and assuring operation, maintenance, and quality of software codes. Halstead is an essential type of software complexity metrics used to measure source code complexity. We presented a comparative analysis study using this metric to benefit software testing process by showing the possibility of software metrics to measure the characteristics of the software in all its aspects. Halstead metric is used to analyse the written code in python, C, JavaScript and Java programming languages. It provides a better tool to evaluate the complexity level of the language and displays the differences levels of code complexity. The conducted experiments show that python is the simplest programming language and Java is the difficulty and more complex language than others. These results benefit the automation in software metrics computation to decide which programming language can produce high quality and the less complexity software.
Ethnographic artificial intelligence

Artificial intelligence research is seldom conducted ethnographically. Nevertheless, the field itself has a distinctive culture – an engineering imaginary within which ‘general intelligence’ is considered to be a phenomenon that can be both defined and tested by reference to some universal (probably mathematical) standard. This paper describes a research agenda that sets out to question those assumptions, through a programme of ethnographic field work, collaborating with computer scientists and educators in several countries of sub-Saharan Africa.
Assessing information security behaviour: a self-determination theory perspective

Purpose This paper outlines the development of a validated questionnaire for assessing information security behaviour. The purpose of this paper is to present data from the questionnaire validation process and the quantitative study results. Design/methodology/approach Data obtained through a quantitative survey (N = 263) at a South African university were used to validate the questionnaire. Findings Exploratory factor analysis produced 11 factors. Cronbach’s alpha for the 11 factors were all above 0.7, suggesting that the questionnaire is valid and reliable. The responses show that autonomy questions received positive perception, followed by competence questions and lastly relatedness questions. The correlation analysis results show that there was a statistically significant relationship between competence factors and autonomy factors. There was a partial significant relationship between autonomy and relatedness factors, and between competence and relatedness factors. The study results suggest that competence and autonomy could be more important than relatedness in fostering information security behaviour among employees. Research limitations/implications This study used a convenience sampling, a cross-sectional design, and was carried out in a single organisation. This could pose limitations when generalising the study results. Future studies could use random sampling and consider other universities for further validation. Practical implications Universities can use the questionnaire to identify developmental areas to improve information security from a behaviour perspective. Originality/value This paper provides a research instrument for assessing information security behaviour from the perspective of the self-determination theory.
Machine Learning Methods on COVID-19 Situation Prediction

COVID-19 has already had a devastating impact on economic and social development and people's life all over the world.Up to September 22nd, 2020, more than 30 million people have been infected.Finding out how to predict and estimate the pandemic trend precisely is of huge necessity because COVID-19 has made the world economy in a recession and deprived over 700 thousand lives.This paper is dedicated to making a comparison between conventional machine learning regression models, including ridge regression and lasso regression, and multivariate polynomial regression.Besides, we attempt to use statewide data to fit nationwide data in the US, proposing a novel aspect to forecast pandemic trend.Under the current situation, insufficient data limit the use of machine learning.To address the issue of data deficiency, a classification model based on neural network with Twitter data is applied.This gives out an alternative approach to estimate daily increase of infected people.We discovered that in a different period, specific models would outweigh another models' performance.Also, our result showed that the data of Georgia and Massachusetts could represent the whole nation data with linear transformations.And this paper verified that using alternative data that relate to the COVID-19 situation to alleviate data deficiency is feasible.
Metric-Based Attention Feature Learning for Video Action Recognition

Conventional approaches for video action recognition were designed to learn feature maps using 3D convolutional neural networks (CNNs). For better action recognition, they trained the large-scale video datasets with the representation power of 3D CNN. However, action recognition is still a challenging task. Since the previous methods rarely distinguish human body from environment, they often overfit background scenes. Note that separating human body from background allows to learn distinct representations of human action. This paper proposes a novel attention module aiming at only action part(s), while neglecting non-action part(s) such as background. First, the attention module employs triplet loss to differentiate active features from non-active or less active features. Second, two attention modules based on spatial and channel domains are proposed to enhance the feature representation ability for action recognition. The spatial attention module is to learn spatial correlation of features, and the channel attention module is to learn channel correlation. Experimental results show that the proposed method achieves state-of-the-art performance of 41.41% and 55.21% on Diving48 and Something-V1 datasets, respectively. In addition, the proposed method provides competitive performance even on UCF101 and HMDB-51 datasets, i.e., 95.83% on UCF-101 and 74.33% on HMDB-51.
A Real-Time Architecture for Pruning the Effectual Computations in Deep Neural Networks

Integrating Deep Neural Networks (DNNs) into the Internet of Thing (IoT) devices could result in the emergence of complex sensing and recognition tasks that support a new era of human interactions with surrounding environments. However, DNNs are power-hungry, performing billions of computations in terms of one inference. Spatial DNN accelerators in principle can support computation-pruning techniques compared to other common architectures such as systolic arrays. Energy-efficient DNN accelerators skip bit-wise or word-wise sparsity in the input feature maps (ifmaps) and filter weights which means ineffectual computations are skipped. However, there is still room for pruning the effectual computations without reducing the accuracy of DNNs. In this paper, we propose a novel real-time architecture and dataflow by decomposing multiplications down to the bit level and pruning identical computations in spatial designs while running benchmark networks. The proposed architecture prunes identical computations by identifying identical bit values available in both ifmaps and filter weights without changing the accuracy of benchmark networks. When compared to the reference design, our proposed design achieves an average per layer speedup of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 1.4$ </tex-math></inline-formula> and an energy efficiency of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 1.21$ </tex-math></inline-formula> per inference while maintaining the accuracy of benchmark networks.
Application of Virtual Reality Technology in Oral English Teaching for College English Majors

Abstract The purpose of this paper is to construct the application ideas of oral English Teaching for College English Majors based on virtual reality technology, so that the virtual reality technology can enable the education environment, improve the personalized learning experience, and make the oral teaching of College English major conform to the needs of information age, artificial intelligence era and 5G era.
Benchmarking RDF Metadata Representations: Reification, Singleton Property and RDF

RDF reification is a data modelling solution for writing RDF statements about RDF statements. A set of approaches exist in the literature to express statement-level metadata, or “reified” statements, in RDF. They have primarily been designed to attach additional contextual information to individual triples, such as provenance, spatio-temporal validity, or certainty. Practically, different methods have been developed to present such metadata while complying with the RDF standard and syntax. However, when effectively stored in triplestores, these various solutions produce diverse results in terms of querying performance, storage efficiency and usability. In this article, we analyse these metrics across three popular reification approaches: Standard Reification, Singleton Property and the recent RDF*. We publish our benchmark evaluation resources so that the community could expand and compare the study on different datasets, reification approaches and storage implementations.
Fuzzy C-Means Hybrid with Fuzzy Bacterial Colony Optimization

In Data mining, Fuzzy or soft clustering is one of the popular approaches proposed to solve several real-world problems. The Fuzzy C-Means (FCM) algorithm is the famous algorithm in fuzzy clustering because of its straightforwardness and short computational effort. But it has the problem of local optima. To overcome this local optima problem, many optimization algorithms have been developed and try to attain a global optimum solution. In this research work, two kinds of enhancement are proposed to solve clustering problem and overcome the above-mentioned shortcomings. First, Bacterial Colony Optimization (BCO) algorithm is integrated with fuzzy theory called Fuzzy BCO (FBCO). Second, Hybridization of FCM with FBCO is developed to obtaining good optimal clusters are called as Hybridization of Fuzzy Clustering Algorithms (HFCA). The experimental results of proposed algorithms are demonstrated using six machine learning datasets and the results produced by proposed FBCO and HFCA generates higher performance while match up with FCM, FPSO (Fuzzy Particle Swarm Optimization) and FBFO (Fuzzy Bacterial Foraging Optimization) algorithms.
Zwei: A Self-Play Reinforcement Learning Framework for Video Transmission Services

Video transmission services adopt adaptive algorithms to ensure users’ demands. Existing techniques are often optimized and evaluated by a function that linearly combines several weighted metrics. Nevertheless, we observe that the given function often fails to describe the requirement accurately, resulting in the violation of generating the required methods. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Zwei</i> , a self-play reinforcement learning framework that updates the policy by straightforwardly utilizing the actual requirement. Technically, Zwei effectively rolls out the trajectories from the same initial state, and instantly estimate the win rate w.r.t the competition outcome, where the outcome represents which trajectory is closer to the assigned requirement. We evaluate Zwei with different requirements on various video transmission tasks, including adaptive bitrate streaming, crowd-sourced live streaming scheduling, and real-time communication. Results indicate that Zwei optimizes itself according to the assigned requirement faithfully, outperforming the state-of-the-art methods under all considered scenarios. Moreover, we further propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Zwei<inline-formula><tex-math notation="LaTeX">$^+$</tex-math></inline-formula></i> , which enables Zwei to learn the policies in the vanilla no-regret reinforcement learning scenario. We validate Zwei <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^+$</tex-math></inline-formula> in the adaptive bitrate streaming task and show the superiority of the proposed method over existing state-of-the-art approaches.
Magnetometer Robust Deep Human Pose Regression With Uncertainty Prediction Using Sparse Body Worn Magnetic Inertial Measurement Units

We propose a deep learning based framework that learns data-driven temporal priors to perform 3D human pose estimation from six body worn Magnetic Inertial Measurement units sensors. Our work estimates 3D human pose with associated uncertainty from sparse body worn sensors. We derive and implement a 3D angle representation that eliminates yaw angle (or magnetometer dependence) and show that 3D human pose is still obtained from this reduced representation, but with enhanced uncertainty. We do not use kinematic acceleration as input and show that it improves the generalization to real sensor data from different subjects as well as accuracy. Our framework is based on Bi-directional recurrent autoencoder. A sliding window is used at inference time, instead of full sequence (offline mode). The major contribution of our research is that 3D human pose is predicted from sparse sensors with a well calibrated uncertainty which is correlated with ambiguity and actual errors. We have demonstrated our results on two real sensor datasets; DIP-IMU and Total capture and have come up with state-of-art accuracy. Our work confirms that the main limitation of sparse sensor based 3D human pose prediction is the lack of temporal priors. Therefore fine-tuning on a small synthetic training set of target domain, improves the accuracy.
Pashto Characters Recognition Using Multi-Class Enabled Support Vector Machine

During the last two decades signi cant work has been reported in the eld of cursive language's recognition especially, in the Arabic, the Urdu and the Persian languages.The unavailability of such work in the Pashto language is because of: the absence of a standard database and of signi cant research work that ultimately acts as a big barrier for the research community.The slight change in the Pashto characters' shape is an additional challenge for researchers.This paper presents an ef cient OCR system for the handwritten Pashto characters based on multi-class enabled support vector machine using manifold feature extraction techniques.These feature extraction techniques include, tools such as zoning feature extractor, discrete cosine transform, discrete wavelet transform, and Gabor lters and histogram of oriented gradients.A hybrid feature map is developed by combining the manifold feature maps.This research work is performed by developing a medium-sized dataset of handwritten Pashto characters that encapsulate 200 handwritten samples for each 44 characters in the Pashto language.Recognition results are generated for the proposed model based on a manifold and hybrid feature map.An overall accuracy rates of 63.30%, 65.13%, 68.55%, 68.28%, 67.02% and 83% are generated based on a zoning technique, HoGs, Gabor lter, DCT, DWT and hybrid feature maps respectively.Applicability of the proposed model is also tested by comparing its results with a convolution neural network model.The convolution neural network-based model generated an accuracy rate of 81.02% smaller than the multi-class support vector machine.The highest accuracy rate of 83% for the multi-class SVM model based on a hybrid feature map re ects the applicability of the proposed model.
Effects of Mobile Drama With Authentic Contexts on English Learning

Teaching English in a classroom setting where EFL learners were usually sitting at desks and doing English learning tasks using paper and pencil is inefficient. It has considerable potential problems that could lead to students learning in a passive state. More specifically, the teaching materials are inconsistent with students’ authentic experiences, which decreases the learning efficiency, learning motivation, and levels of interactivity. An annotatable multimedia E-reader (AME) was developed to address these problems. The provided various annotation tools to help students create dramas with authentic contexts. The study participants were 48 junior high school students and divided into the experimental group (EG) and control group (CG). Results revealed that learning English through drama with AME assistance in authentic contexts significantly improved the participants’ learning achievements. In other words, the learning achievements of the EG were superior to those of the CG. Moreover, the results indicated that different learning behaviors were significantly related to different learning achievements. Thus, the greater the diversity of learning behaviors practiced by the students in the drama activity in authentic contexts, the higher the students’ learning scores on different dimensions. Finally, the results indicated that using the AME in authentic contexts was beneficial for English learning through drama.
FPGA-based scalable and highly concurrent convolutional neural network acceleration

This article proposes an efficient, low-latency, scalable, and low-error neural network acceleration architecture. Considering the performance requirements of high efficiency and low latency, the methods of multi-channel parallel computing between layers and pipeline design are adopted to accelerate the neural network. Then, based on Xilinx zynq-7000 FPGA, the acceleration strategy is realized, and the effect of calculating 28*28 handwritten images at 25.95us at a clock frequency of 200M is investigated. Further, the flexibility and scalability of the network is improved by adding a line buffer for variable image width and designing a mechanism for selectable convolution kernel size. Since the convolutional neural networks are based on floating-point operations, if the floating-point is converted to fixed-point when implemented on FPGA, there will not only be a loss of precision, but also introduce a tedious conversion work. Thus, our neural network uses 32-bit Floating point operations. Moreover, the task of handwritten digit recognition is performed on the MNIST data set, to experimentally evaluate our solution. Experiment results show that the neural network acceleration architecture proposed in this paper achieves better performance. Compare with the literature [4],[6], the calculation speed is significantly improved, and the calculation speed is increased by 101.6 times compared with the literature [4] Compared with the literature [6], there is a speed increase of 11.88 times.