Reviewerly/exHarmony huggingface dataset:
Assessing the role of information and communication technology in reducing the gap between rich and poor: the case of South Asia

Purpose The widening income gap between rich and poor has gained worldwide recognition in recent decades. This income gap between rich and poor is defined as the extent of income unevenly distributed in a host country. This study provides an empirical view of the association between information and communication technology and the widening of the income gap. Design/methodology/approach The study used panel data from 2005 to 2019. To detect unit root issues, Levin and Lin (LL) and Im, Pesaran and Shin (IPS) tests were first employed. The pooled mean group and mean group estimators were employed to investigate the short and long -term impact of information and communication technology and other control factors on reducing the gap between rich and poor in South Asia. Findings The results showed that the Pooled mean group's findings are more efficient and consistent as compared to mean group estimators. The results of the paper showed that the greater penetration of information and communication technologies in the economy negatively and significantly affects income inequality. Moreover, the information and communication technology, foreign remittances and foreign direct investment (FDI) significantly reduce the gap between rich and poor in the long run. Practical implications At last, the findings of the study serve as an excellent roadmap for policymakers seeking to address the issue of growing income inequality in the South Asian regions and worldwide. Originality/value Based on the findings of this study, South Asia can reduce the gap between rich and poor by investing more in the information and communication technology sector. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2021-0638
Learning With Mobile Augmented Reality- and Automatic Speech Recognition-Based Materials for English Listening and Speaking Skills: Effectiveness and Perceptions of Non-English Major English as a Foreign Language Students

Due to the outbreak of COVID 19, an online bilingual curriculum was conducted via Google Meet. The learning material was developed and implemented by using a smartphone application, STEMUP, based on augmented reality (AR) and automatic speech recognition (ASR) technologies. This study investigated the oral performance and perceptions of learning with STEMUP of ninety non-English major students from several colleges at a technical university in Taiwan. Data were collected from pre- and post-tests and a questionnaire survey. Results indicated that students significantly improved their oral performance and recorded their positive perceptions. Students’ oral performance significantly depended on their English proficiency. Their perceptions were not significant related to their English proficiency, gender, or college. Instant feedback and evaluation provided by ASR technology and online Google text-to-speech service both embedded in STEMUP helped students notice, modify and improve their listening and speaking skills. They were satisfied with the bilingual curriculum, which helped them increase understanding about content knowledge by the teacher’s explanation in Chinese, and improve English listening and speaking skills by learning with STEMUP. This study is a good start in creating an interactive and communicative learning environment where translanguaging is effectively integrated with innovative technologies.
The role of sustainable development goals, financial knowledge and investment strategies on the organizational profitability: Moderating impact of government support

Recently, sustainable development goals (SDG) and investment strategies and knowledge has become the foremost factors for the high organizational profitability and capture the focus of recent studies and policymakers. Therefore, the current study aims to examine the impact of SDG, investment strategies and financial knowledge on the organizational profitability of manufacturing firms in China. Furthermore, the study examines the role of government support in the interplay between investment plans, financial understanding, and the profitability of organisations. Survey questionnaires and smart-PLS were used to collect data and analyse reliability and correlations. The findings show that SDGs, investment strategies, and financial knowledge all play a substantial role in a company's profitability.The results also revealed that government support moderates significantly among investment strategies, financial knowledge, and organizational profitability. This study guides the regulators while developing policies regarding SDG and investment strategies with respect to organizational profitability.
Towards practical robustness analysis for DNNs based on PAC-model learning

To analyse local robustness properties of deep neural networks (DNNs), we present a practical framework from a model learning perspective. Based on black-box model learning with scenario optimisation, we abstract the local behaviour of a DNN via an affine model with the probably approximately correct (PAC) guarantee. From the learned model, we can infer the corresponding PAC-model robustness property. The innovation of our work is the integration of model learning into PAC robustness analysis: that is, we construct a PAC guarantee on the model level instead of sample distribution, which induces a more faithful and accurate robustness evaluation. This is in contrast to existing statistical methods without model learning. We implement our method in a prototypical tool named DeepPAC. As a black-box method, DeepPAC is scalable and efficient, especially when DNNs have complex structures or high-dimensional inputs. We extensively evaluate DeepPAC, with 4 baselines (using formal verification, statistical methods, testing and adversarial attack) and 20 DNN models across 3 datasets, including MNIST, CIFAR-10, and ImageNet. It is shown that DeepPAC outperforms the state-of-the-art statistical method PROVERO, and it achieves more practical robustness analysis than the formal verification tool ERAN. Also, its results are consistent with existing DNN testing work like DeepGini.
Ethics Sheets for AI Tasks

Several high-profile events, such as the mass testing of emotion recognition systems on vulnerable sub-populations and using question answering systems to make moral judgments, have highlighted how technology will often lead to more adverse outcomes for those that are already marginalized.At issue here are not just individual systems and datasets, but also the AI tasks themselves.In this position paper, I make a case for thinking about ethical considerations not just at the level of individual models and datasets, but also at the level of AI tasks.I will present a new form of such an effort, Ethics Sheets for AI Tasks, dedicated to fleshing out the assumptions and ethical considerations hidden in how a task is commonly framed and in the choices we make regarding the data, method, and evaluation.I will also present a template for ethics sheets with 50 ethical considerations, using the task of emotion recognition as a running example.Ethics sheets are a mechanism to engage with and document ethical considerations before building datasets and systems.Similar to survey articles, a small number of carefully created ethics sheets can serve numerous researchers and developers.1 The Case: Importance of Ethics Considerations at the Level of AI TasksGood design helps everyone.It is well established, for example, that designing for accessibility helps society at large. 1 As Artificial Intelligence (AI), Machine Learning (ML), and Natural language Processing (NLP) systems become more ubiquitous, their broad societal impacts are receiving more scrutiny than ever before.However, several high-profile instances such as face-recognition systems that perform poorly for people with dark skin tones (Buolamwini and Gebru, 2018), machine translation systems that are biased against
Ising-CIM: A Reconfigurable and Scalable Compute Within Memory Analog Ising Accelerator for Solving Combinatorial Optimization Problems

Combinatorial optimization problems (COPs) find applications in real-world scientific, industrial, and societal scenarios. Such COPs are computationally NP-hard, and performing an exhaustive brute force search for the optimal solution becomes untenable as the COP size increases. To expedite the COP computation, the Ising model formalism is used, which abstracts spin dynamics in a ferromagnet. The spins are orientated to reach the minimum energy state, representing the optimum COP solution. Previous Ising engine designs utilized dedicated annealing processors or additional digital arithmetic circuits next to the memory bitcells. These custom circuits or processors cannot be repurposed for other applications, incurring significant area and power overhead. In contrast to the prior approaches, this work presents a reconfigurable and scalable compute-within-memory analog approach for Ising computation (called Ising-CIM). This area-efficient approach repurposes existing embedded memory bitcell columns and peripheral circuits to perform analog domain Hamiltonian calculations on the bitlines minimizing area and power overhead significantly. A 13.18-Kb silicon prototype, implemented in a 65-nm CMOS process, demonstrates the Ising-CIM concept and functionality using a 100 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 64 pixel image in a max-cut COP. The Ising-CIM design achieves 48- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}~^{\mathrm{ 2}}$ </tex-math></inline-formula> /spin unit spin area and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1091\times $ </tex-math></inline-formula> speedup in annealing time compared to the CPU.
XLM-EMO: Multilingual Emotion Prediction in Social Media Text

Detecting emotion in text allows social and computational scientists to study how people behave and react to online events.However, developing these tools for different languages requires data that is not always available.This paper collects the available emotion detection datasets across 19 languages.We train a multilingual emotion prediction model for social media data, XLM-EMO.The model shows competitive performance in a zero-shot setting, suggesting it is helpful in the context of lowresource languages.We release our model to the community so that interested researchers can directly use it.
Exploring Design smells for smell-based defect prediction

Defect prediction is commonly used to reduce the effort from the testing phase of software development. A promising strategy is to use machine learning techniques to predict which software components may be defective. Features are key factors to the prediction’s success, and thus extracting significant features can improve the model’s accuracy. In particular, code smells are a category of those features that have been shown to improve the prediction performance significantly. However, Design code smells, a state-of-the-art collection of code smells based on the violations of the object-oriented programming principles, have not been studied in the context of defect prediction. In this paper, we study the performance of defect prediction models by training multiple classifiers for 97 real projects. We compare using Design code smells as features and using other Traditional smells from the literature and both. Moreover, we cluster and analyze the models’ performance based on the categories of Design code smells. We conclude that the models trained with both the Design code smells and the smells from the literature performed the best, with an improvement of 4.1% for the AUC score, compared to models trained with only Traditional smells. Consequently, Design smells are a good addition to the smells commonly studied in the literature for defect prediction.
Criteria Comparative Learning for Real-Scene Image Super-Resolution

Real-scene image super-resolution aims to restore real-world low-resolution images into their high-quality versions. A typical RealSR framework usually includes the optimization of multiple criteria which are designed for different image properties, by making the implicit assumption that the ground-truth images can provide a good trade-off between different criteria. However, this assumption could be easily violated in practice due to the inherent contrastive relationship between different image properties. Contrastive learning (CL) provides a promising recipe to relieve this problem by learning discriminative features using the triplet contrastive losses. Though CL has achieved significant success in many computer vision tasks, it is non-trivial to introduce CL to RealSR due to the difficulty in defining valid positive image pairs in this case. Inspired by the observation that the contrastive relationship could also exist between the criteria, in this work, we propose a novel training paradigm for RealSR, named Criteria Comparative Learning (Cria-CL), by developing contrastive losses defined on criteria instead of image patches. In addition, a spatial projector is proposed to obtain a good view for Cria-CL in RealSR. Our experiments demonstrate that compared with the typical weighted regression strategy, our method achieves a significant improvement under similar parameter settings.
A learning method for AUV collision avoidance through deep reinforcement learning

This paper investigates the collision avoidance problem for autonomous underwater vehicle (AUV) with limited detection range via deep reinforcement learning method in an unknown underwater environment. Firstly, to deal with both unknown static and dynamic obstacles, we introduce two different event-triggered mechanism to generate obstacle-related states and reward the safe collision avoidance action of AUV, respectively. Secondly, the complete state space is customized by combining the state of AUV itself and the state related to a target area. In addition, considering the task requirement of reaching a target area with the shortest path, the complete reward function is designed. Then, we propose a novel event-triggered soft actor–critic (ET-SAC) algorithm for AUV collision avoidance, and give its detailed pseudo code and implementation architecture. Finally, training and evaluation are carried out in the simulation platform we designed, including unknown static obstacle environment and unknown dynamic obstacle environment in 2D/3D space, and the results confirm that the proposed algorithm of this paper is feasible and effective for AUV to avoid collision safely.
Encrypted video search

Video-based services have become popular. Clients often outsource their videos to the cloud to relieve local maintenance. However, privacy has emerged as a major concern since many videos contain sensitive information. While retrieving (unencrypted) videos has been widely studied, encrypted multimedia retrieval receives rare attention, at best in a limited form of similarity searches on images.
Human Skeleton Feature Optimizer and Adaptive Structure Enhancement Graph Convolution Network for Action Recognition

Human action recognition based on the graph convolution network (GCN) is a hot topic in computer vision. Existing GCN-based methods fail to capture internal implicit information when extracting action features, thereby leading to over-smoothing in the training stage. These issues result in poor performance and inaccurate extraction of action features. To address these problems, a new GCN is constructed. In this paper, a human skeleton feature optimizer (SFO) and adaptive structure enhancement graph convolution network (ASE-GCN) for action recognition are proposed in an end-to-end manner. To obtain discriminative features, the SFO is proposed to construct a new skeleton representation for action recognition through the connection criterion, which extracts the internal implicit information of action. The action feature of the joint coordinates is extracted by graph structure mask (GSM), directed graph mapping (DGM), and adaptive pooling operation (APO) in the proposed ASE-GCN network. The GSM acts as the regularizer of skeleton structure information to strengthen the representation of the graph structure. The DGM correlates the directed graph with human motion information through kinematic principle, and the APO strengthens the global high-frequency features to alleviate over-smoothing. The proposed method achieves comparable or superior results over state-of-the-art methods when used in experiments on two large public-scale datasets, NTU-RGB+D and Kinetics.
Automatic pointer meters recognition system based on line scan vision

Abstract The automatic recognition of pointers is of great significance for efficiently collecting measurements from industrial instruments. In this paper, an automatic pointer meter recognition system based on line scan vision is developed. A line scan camera is used to capture images of the pointer of a pointer meter. Light-spot centroid algorithm is implemented to determine the centroid position of the pointer. The captured images can record the dynamic movement of the pointer, thereby enabling condition monitoring in industrial processes. Experimental results show that the proposed pointer meter extraction method is robust against interference and that a characteristic segmentation classifier produces more accurate detection results than other approaches.
A multiple association-based unsupervised feature selection algorithm for mixed data sets

Companies have an increasing access to very large datasets within their domain. Analysing these datasets often requires the application of feature selection techniques in order to reduce the dimensionality of the data and prioritize features for downstream knowledge generation tasks. Effective feature selection is a key part of clustering, regression and classification. It presents a myriad of opportunities to improve the machine learning pipeline: eliminating redundant and irrelevant features, reducing model over-fitting, faster model training times and more explainable models. By contrast, and despite the widespread availability and use of categorical data in practice, feature selection for categorical and/or mixed data has received relatively little attention in comparison to numerical data. Furthermore, existing feature selection methods for mixed data are sensitive to number of objects by having nonlinear time complexities with respect to number of objects. In this work, we propose a generic multiple association measure for mixed datasets and a novel feature selection algorithm that uses multiple association across features. Our algorithm is based upon the belief that the most representative chosen set of features should be as diverse and minimally dependent on each other as possible. The proposed algorithm formulates the problem of feature selection as an optimization problem, searching for the set of features that have minimum association amongst them. We present a generic multiple association measure and two associated feature selection algorithms: Naive and Greedy Feature Selection Algorithms called NFSA and GFSA, respectively. Our proposed GFSA algorithm is evaluated on 15 benchmark datasets, and compared to four existing state of the art feature selection techniques. We demonstrate that our approach provides comparable downstream classification performance outperforming other leading techniques on several datasets. Both time complexity analysis and experimental results show that our proposed algorithm significantly reduces the processing time required for unsupervised feature selection algorithms especially for long datasets which have a huge number of objects, whilst also yielding comparable clustering and classification performance. On the other hand, we do not recommend our approach for wide datasets where the number of features is huge with respect to the number of objects e.g., image, text and genome datasets.
DramaQA: Character-Centered Video Story Understanding with Hierarchical QA

Despite recent progress on computer vision and natural language processing, developing a machine that can understand video story is still hard to achieve due to the intrinsic difficulty of video story. Moreover, researches on how to evaluate the degree of video understanding based on human cognitive process have not progressed as yet. In this paper, we propose a novel video question answering (Video QA) task, DramaQA, for a comprehensive understanding of the video story. The DramaQA focuses on two perspectives: 1) Hierarchical QAs as an evaluation metric based on the cognitive developmental stages of human intelligence. 2) Character-centered video annotations to model local coherence of the story. Our dataset is built upon the TV drama "Another Miss Oh" and it contains 17,983 QA pairs from 23,928 various length video clips, with each QA pair belonging to one of four difficulty levels. We provide 217,308 annotated images with rich character-centered annotations, including visual bounding boxes, behaviors and emotions of main characters, and coreference resolved scripts. Additionally, we suggest Multi-level Context Matching model which hierarchically understands character-centered representations of video to answer questions. We release our dataset and model publicly for research purposes, and we expect our work to provide a new perspective on video story understanding research.
Subspace analysis based machine learning method for automated defect detection from fringe patterns

We propose a reliable and robust defect detection method from the noisy fringe patterns obtained in optical interferometry. The proposed method relies on a naive Bayes classifier based machine learning model. The model utilizes the phase derivatives computed using fringe signal subspace analysis as feature vectors. This allows for automated defect identification without the requirement of selection of manual threshold parameters. The simulation analysis of various types of defects corroborates the utility of the proposed method. Further, the method is tested on experimental fringes obtained in diffraction phase microscopy to validate its practical applicability.
Closing the Digital Gender Gap among Foreign University Students: The Challenges Ahead

In today’s world, new and advanced forms of technology are increasingly providing great changes in universities, thus generating new possibilities and impacting pedagogy and learning methodology. Unfortunately, not all students can use these tools in the same way and with the same ability. Not only are there digital gender gaps that limit women from enjoying these learning opportunities, but there are also digital gaps between foreign and natives’ students who have been trained in these technologies in their countries of origin, which impedes the achievement of the sustainable development goals planned for 2030. This study addresses theoretical foundations on the digital gender gap in university studies and provides an econometric analysis, through a simple linear regression, on the existence of a correlation between this digital gender gap and the university study gap by gender. A more specific analysis is also presented on the digital gender gap in the case of foreign students from four groups of countries, according to their income. The results show, on one hand, that differences in the access and use of technologies represent one of the factors that affects the percentage of graduates in higher education by gender; on the other, that there is a highly visible digital divide between countries with high income, compared to low- and lower-middle-income countries.