Reviewerly/exHarmony huggingface dataset:
Being Automated or Not? Risk Identification of Occupations with Graph Neural Networks

The rapid advances in automation technologies, such as artificial intelligence (AI) and robotics, pose an increasing risk of automation for occupations, with a likely significant impact on the labour market. Recent social-economic studies suggest that nearly 50% of occupations are at high risk of being automated in the next decade. However, the lack of granular data and empirically informed models have limited the accuracy of these studies and made it challenging to predict which jobs will be automated. In this paper, we study the automation risk of occupations by performing a classification task between automated and non-automated occupations. The available information is 910 occupations’ task statements, skills and interactions categorised by Standard Occupational Classification (SOC). To fully utilize this information, we propose a graph-based semi-supervised classification method named Automated Occupation Classification based on Graph Convolutional Networks (AOC-GCN) to identify the automated risk for occupations. This model integrates a heterogeneous graph to capture occupations’ local and global contexts. The results show that our proposed method outperforms the baseline models by considering the information of both internal features of occupations and their external interactions. This study could help policymakers identify potential automated occupations and support individuals’ decision-making before entering the job market.
Gamer Breakbots: Exploring Robots as a Way for Gamers to Manage Break Time and Alleviate Potential Health Issues

As more than half of the U.S. population is identified as a video gamer, a growing number of studies have investigated the health issues of gamers who spend prolonged times playing games. Potential health issues of gamers include eye fatigue, neck/back pain, wrist pain, mental stress and other issues. To alleviate potential health issues of gamers, this paper investigates how robots can help gamers better manage their health issues. We conducted three studies with gamers, including an informal interview, an online survey, and a co-design session. In the three studies with gamers, we found that gamers focused on the robot design factors-how robots get the gamers ’ attention, and how they force gamers to pause their play, while still seeming friendly. Our preliminary results indicate substantial promise regarding the role of robots in gamers ’ health management, which we will investigate in our future research.
Discriminative Distillation to Reduce Class Confusion in Continual Learning

Successful continual learning of new knowledge would enable intelligent systems to recognize more and more classes of objects. However, current intelligent systems often fail to correctly recognize previously learned classes of objects when updated to learn new classes. It is widely believed that such downgraded performance is solely due to the catastrophic forgetting of previously learned knowledge. In this study, we argue that the class confusion phenomena may also play a role in downgrading the classification performance during continual learning, i.e., the high similarity between new classes and any previously learned classes would also cause the classifier to make mistakes in recognizing these old classes, even if the knowledge of these old classes is not forgotten. To alleviate the class confusion issue, we propose a discriminative distillation strategy to help the classifier well learn discriminative features between confusing classes during continual learning. Experiments on multiple datasets support that the proposed distillation strategy, when combined with existing methods, is effective in improving continual learning.
From Inheritance to Mockito: An Automatic Refactoring Approach

Unit testing focuses on verifying the functions of individual units of a software system. It is challenging due to the high inter dependencies among software units. Developers address this by mocking—replacing the dependency by a “fake” object. Despite the existence of powerful, dedicated mocking frameworks, developers often turn to a “hand-rolled” approach—inheritance. That is, they create a subclass of the dependent class and mock its behavior through method overriding. However, this requires tedious implementation and compromises the design quality of unit tests. This work contributes a fully automated refactoring framework to identify and replace the usage of inheritance by using Mockito—a well received mocking framework. Our approach is built upon the empirical experience from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">five</i> open source projects that use inheritance for mocking. We evaluate our approach on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nine</i> other projects. Results show that our framework is efficient, generally applicable to new datasets, mostly preserves test case behaviors in detecting defects (in the form of mutants), and decouples test code from production code. The qualitative evaluation by experienced developers suggests that the auto-refactoring solutions generated by our framework improve the quality of the unit test cases in various aspects, such as making test conditions more explicit, as well as improved cohesion, readability, understandability, and maintainability with test cases. Finally, we submit 23 pull requests containing our refactoring solutions to the open source projects. It turns our that, 9 requests are accepted/merged, 6 requests are rejected, the remaining requests are pending (5 requests), with unexpected exceptions (2 requests), or undecided (1 request). In particular, among the 21 open source developers that are involved in the reviewing process, 81% give positive votes. This indicates that our refactoring solutions are quite well received by the open source projects and developers.
Most and Least Retrievable Images in Visual-Language Query Systems

This is the first work to introduce the Most Retrievable Image(MRI) and Least Retrievable Image(LRI) concepts in modern text-to-image retrieval systems. An MRI is associated with and thus can be retrieved by many unrelated texts, while an LRI is disassociated from and thus not retrievable by related texts. Both of them have important practical applications and implications. Due to their one-to-many nature, it is fundamentally challenging to construct MRI and LRI. This research addresses this nontrivial problem by developing novel and effective loss functions to craft perturbations that essentially corrupt feature correlation between visual and language spaces, thus enabling MRI and LRI. The proposed schemes are implemented based on CLIP, a state-of-the-art image and text representation model, to demonstrate MRI and LRI and their application in privacy-preserved image sharing and malicious advertisement. They are evaluated by extensive experiments based on the modern visual-language models on multiple benchmarks, including Paris, ImageNet, Flickr30k, and MSCOCO. The experimental results show the effectiveness and robustness of the proposed schemes for constructing MRI and LRI.
MTCA: A Multimodal Summarization Model Based on Two-Stream Cross Attention

The combination of vision and natural language patterns has become an important topic in the field of computer vision and natural language processing. With the rapid growth of multimodal information, multimodal summarization has received unprecedented attention. In this paper, we propose MTCA which composed of a pre-trained feature extractor, a text encoder, an image encoder, a two-stream cross attention fusion module and a summary decoder to complete the multimodal summarization task. This framework integrates three sub tasks: extractive summarization, abstractive summarization and image selection. At the same time, we introduce a residual network to alleviate the problem of modality-bias. Experiments show that the model is better than the baseline model and the performance is better than the text summarization method which ignores visual modality.
Development of Computer Intelligent Proofreading System from the Perspective of English Translation Application

This paper proposes the development of a computer intelligence calibration system from the viewpoint of English translation applications. First, we construct a semantic volume model and construct a semantic volume model. The whole system was designed. Soft hardware of the system is designed by the overall structure of the system. Hardware design primarily improves search accuracy by search modules. In the development process of the software module, it includes modules such as system application frameworks, network topology, clients, translators, and online cooperative translations, and supports multilingual translation and calibration. An English translation test was carried out on the last designed system. As a result of the English translation test, it became clear that this system fulfills the translation calibration demand of the user and raises the calibration efficiency.
CD-LwTE: Cost- and Delay-Aware Light-Weight Transcoding at the Edge

The edge computing paradigm brings cloud capabilities close to the clients. Leveraging the edge’s capabilities can improve video streaming services by employing the storage capacity and processing power at the edge for caching and transcoding tasks, respectively, resulting in video streaming services with higher quality and lower latency. In this paper, we propose, a Cost-and Delay-aware Light-weight Transcoding approach at the Edge, in the context of HTTP Adaptive Streaming (HAS). The encoding of a video segment requires computationally intensive search processes. The main idea of is to store the optimal search results as metadata for each bitrate of video segments and reuse it at the edge servers to reduce the required time and computational resources for transcoding. Aiming at minimizing the cost and delay of Video-on-Demand (VoD) services, we formulate the problem of selecting an optimal policy for serving segment requests at the edge server, including (i) storing at the edge server, (ii) transcoding from a higher bitrate at the edge server, and (iii) fetching from the origin or a CDN server, as a Binary Linear Programming (BLP) model. As a result, stores the popular video segments at the edge and serves the unpopular ones by transcoding using metadata or fetching from the origin/CDN server. In this way, in addition to the significant reduction in bandwidth and storage costs, the transcoding time of a requested segment is remarkably decreased by utilizing its corresponding metadata. Moreover, we prove the proposed BLP model is an NP-hard problem and propose two heuristic algorithms to mitigate the time complexity of . We investigate the performance of in comprehensive scenarios with various video contents, encoding software, encoding settings, and available resources at the edge. The experimental results show that our approach (i) reduces the transcoding time by up to 97%, (ii) decreases the streaming cost, including storage, computation, and bandwidth costs, by up to 75%, and (iii) reduces delay by up to 48% compared to approaches.
Towards a Taxonomy for the Opacity of AI Systems

The research program of eXplainable AI (XAI) has been developed with the aim of providing tools and methods for reducing opacity and making AI systems more humanly understandable. Unfortunately, the majority of XAI scholars actually classify a system as more or less opaque by confronting it with traditional AI systems such as linear regression models or rules-based systems, which are usually assumed to be the prototype of transparent systems. In doing so, the concept of opacity remains unexplained. To overcome this issue, we view opacity as a concept whose meaning depends on the context of application, and on the purposes and characteristics of its users. Based on this, in this work, we distinguish between access opacity, link opacity and semantic opacity, hence providing the groundwork for a taxonomy of the concept of opacity for AI systems.
Arabic Dialects Morphological Analyzers: A Survey

Morphological analysis is a crucial component in natural language processing. For the Arabic language, many attempts have been conducted in order to build morphological analyzers. Despite the increasing attention paid to Arabic dialects, the number of morphological analyzers that have been built is not important compared to Modern Standard Arabic. In addition, these tools often cover a few dialects of Arabic such as Egyptian, Levantine, and Gulf, and don’t support currently all of them. In this paper, we present a literature review of morphological analyzers supporting Arabic dialects. We classify their building approaches and propose some guidelines to adapt them to a specific Arabic dialect.
Artificial Life for Breast Ultrasound Image Segmentation

Breast cancer is one of the prominent causes of death among women worldwide. An effective way to reduce the risk of breast cancer is to diagnose it correctly at an early stage of the disease using ultrasound imaging. Breast ultrasound is a common imaging technique used to diagnose breast cancer. Ultrasound images are usually analysed by manual segmentation, which is a time-consuming process that can be inaccurate due to human error. Therefore, computer-assisted diagnosis can be a key tool to improve the accuracy of a breast cancer diagnosis. In this paper, we propose a novel method for US tumour segmentation based on artificial life active particles that interact with the edges of the image. The active particles are able to segment complex-shaped synthetic and real US tumours by grouping the feature edges. The numerical evaluation against five conventional methods shows an advantage of the proposed algorithm.
A Coalition Formation Approach for Privacy and Energy-Aware Split Deep Learning Inference in Edge Camera Network

Recently, an increasing amount of application tasks have depended on deep learning (DL) inference models on Internet of Things (IoT) based camera networks. However, it is challenging to perform the inference of such resource-hungry DL models on the computationally limited IoT system. Compared to cloud computing, edge computing deploys resources near the end-users to reduce the transmission delay and retains the raw data on the trusted servers to mitigate privacy concerns. Since resources at the edge are limited, management like user association decisions, edge resources allocation, and device configuration with DL model parameter selection becomes essential. This work proposes a coalition formation game-based algorithm to solve the association problem between IoT-based cameras and the edge nodes. Our goal is to maximize the social welfare that consists of multi-view detection enhancement, the privacy retained preference, and the power savings from the cameras. Besides, we adopt the concept of split-ML to provide more flexibility for networking and computing resources allocation at the edge. The final coalition structure is proved to converge and maintain stability. The simulation results show that each design knob, including association decisions made by coalition formation, DNN layer-level partition, and multi-view detection, is essential under different scenario settings.
An Efficient Query Architecture for Permissioned Blockchain

For the existing permissioned blockchain system, consensus nodes process not only query requests but also write requests, which make themselves in a heavy workload that reduce the system's performance. In this paper, we propose an efficient architecture which separates part of the query tasks from the consensus nodes to the secondary nodes, and present EQblockchain, a permissioned blockchain system following this architecture, to solve the problem mentioned above. EQblockchain divides transactions into two types: one is read-transactions, the other is write-transactions. We describe how query requests, i.e., read-transactions, are processed by the consensus nodes and the secondary nodes and how write requests update the blockchain state. Because the read-transactions are processed by the consensus nodes and secondary nodes, the system throughput is significantly increased. Besides, EQblockchain constructs a few inverted indexes to support range query, which also enhances the query ability. Furthermore, we conduct several experiments to show that EQblockchain not only has efficient performance in query-heavy workload but also slightly improves the throughput of the write requests, and really has good range query ability in comparison to the baseline.
Dockerized Knowledge-Oriented Multi-modal Social Event Detection System

Faced with an increasing amount of unstructured multimodal data appearing on various social platforms (e.g., Twitter/Instagram), we seek to effectively understand the complex social events portrayed on these platforms. However, conventional information extraction systems cannot understand these data because they cannot handle real-world analysis or require extensive tuning and many manually annotated examples to successfully comprehend these events. To solve this problem, this paper develops a knowledge-oriented artificial intelligence system that can identify and analyze these data and complex events and bring them to the user's attention. Our research aims to understand complex events described in multimedia inputs by developing a semi-automated system that identifies, links, and temporally sequences their subsidiary elements, the participants involved, as well as the complex event type. This project proposes a systematic analysis of world events, such as the Boston Marathon bombing, Capital Riots, Covid-19, etc. We have successfully evaluated our system on various datasets and have shown significant improvement compared to other previous methods.
A Model of E-commerce Recommender System using Enhancement Document Context Based on Attention and User Information Based on Auto Encoder and Latent Factor

Recommender system is essential tool in e-commerce application. Recommender system responsible to produce relevant information to customer. The successful adoption of recommender system influence of e-commerce company revenue. Collaborative filtering is the most successful of recommender system algorithm that adopted in many large e-commerce company. Collaborative filtering relies on rating matrix to produce product index recommendation. However, number of rating matrix very sparse due to majority customer are lazy to rate the product. In this research, we implemented product document information enhancement using attention mechanism that combined with stack denoising auto encoder (SDAE) to extract user information and latent factor based on probabilistic matrix factorization (PMF). According to our experiment report, our model success to improve the performance over previous work more than 18% in average over traditional PMF in ML.M and ML.10M.
Electromagnetic Signal Modulation Classification Based on Multimodal Features and Reinforcement Learning

The multimodal representation of signals can give a lot of information in the field of communication modulation signal recognition. Existing approaches can use some of the modes for classification tasks, but they don't take use of the multimodal characteristics of signals well. The approach of signal modulation recognition suggested in this research is based on reinforcement learning and multimodal features. The feature set is obtained via wavelet transform using the 1D features of the signal and the constellation map features, and then the feature set is filtered using reinforcement learning to get a small subset of features to obtain the training feature set. With a small number of extra channels, the accuracy of recognition can be enhanced when utilizing the filtered feature set to categorize the modulation class of the signal. Experiments on the RML2016 dataset validate the effectiveness of our proposed method and achieve good classification performance, which is a pioneering idea for the signal modulation classification problem.
Improving Persona Understanding for Persona-based Dialogue Generation with Diverse Knowledge Selection

A significant goal in an open-domain dialogue system is to make chatbots generate more persona coherent responses given a context. To achieve this goal, some researchers attempt to introduce persona information into neural dialogue models. However, these neural dialogue models describe excessively persona traits during the conversation, which still suffer from the problem of generating boring and meaningful responses. In this paper, we divide the open-domain personalized dialogue generation task into two processes, persona recognition and persona fusion. In the persona recognition process, we use the pre-training model to encode the personas and conversation history independently, which is beneficial to the persona information fusion. Then, we design a dynamic persona fusion mechanism to effectively mine the relevance of dialogue context and persona information, and dynamically predict whether to incorporate persona features in the process of the dialogues. Our model outperforms with 1.23% in Acc., 0.36% in BLEU, 0.92% in F1, and 0.036% in Distinct than baseline models. The experimental results on the ConvAI2 dataset illustrate that the proposed model is superior to baseline approaches for generating more coherent and persona consistent responses.
MDSFE: Multiscale Deep Stacking Fusion Enhancer Network for Visual Data Enhancement

With the rapid development of visual sensors and artificial intelligence (AI), video/image data has increased dramatically, especially in the era of AI-enabled intelligent transportation. In low-light imaging conditions, however, the camera only captures weak scene-reflected light. The visual data is thus inevitably affected by noise, low contrast, and poor brightness, and so on. It will have a negative influence on the development of vision-based traffic situational awareness, traffic safety management, and automatic/autonomous vehicles. To guarantee high-quality visual data, the multiscale deep stacking fusion enhancer (termed MDSFE) is proposed to enhance a low-light image. In particular, our MDSFE consists of four components, i.e., coarse extraction module (C-EM), coarse attention fusion module (C-AFM), multiscale dense enhancement module (M-DEM), and fine encoder–decoder fusion module (F-EDFM). The combination of these modules is capable of enhancing the abilities of feature mapping and expression. Experimental results on both synthetic and real-world scenarios have illustrated that the proposed method can provide superior enhancement results under different imaging conditions. It also has the capacity of improving the detection precision under low-light conditions.