Reviewerly/exHarmony huggingface dataset:
Making Data Tangible: A Cross-disciplinary Design Space for Data Physicalization

Designing a data physicalization requires a myriad of different considerations. Despite the cross-disciplinary nature of these considerations, research currently lacks a synthesis across the different communities data physicalization sits upon, including their approaches, theories, and even terminologies. To bridge these communities synergistically, we present a design space that describes and analyzes physicalizations according to three facets: context (end-user considerations), structure (the physical structure of the artifact), and interactions (interactions with both the artifact and data). We construct this design space through a systematic review of 47 physicalizations and analyze the interrelationships of key factors when designing a physicalization. This design space cross-pollinates knowledge from relevant HCI communities, providing a cohesive overview of what designers should consider when creating a data physicalization while suggesting new design possibilities. We analyze the design decisions present in current physicalizations, discuss emerging trends, and identify underlying open challenges.
Robust Table Detection and Structure Recognition from Heterogeneous Document Images

We introduce a new table detection and structure recognition approach named RobusTabNet to detect the boundaries of tables and reconstruct the cellular structure of each table from heterogeneous document images. For table detection, we propose to use CornerNet as a new region proposal network to generate higher quality table proposals for Faster R-CNN, which has significantly improved the localization accuracy of Faster R-CNN for table detection. Consequently, our table detection approach achieves state-of-the-art performance on three public table detection benchmarks, namely cTDaR TrackA, PubLayNet and IIIT-AR-13K, by only using a lightweight ResNet-18 backbone network. Furthermore, we propose a new split-and-merge based table structure recognition approach, in which a novel spatial CNN based separation line prediction module is proposed to split each detected table into a grid of cells, and a Grid CNN based cell merging module is applied to recover the spanning cells. As the spatial CNN module can effectively propagate contextual information across the whole table image, our table structure recognizer can robustly recognize tables with large blank spaces and geometrically distorted (even curved) tables. Thanks to these two techniques, our table structure recognition approach achieves state-of-the-art performance on three public benchmarks, including SciTSR, PubTabNet and cTDaR TrackB2-Modern. Moreover, we have further demonstrated the advantages of our approach in recognizing tables with complex structures, large blank spaces, as well as geometrically distorted or even curved shapes on a more challenging in-house dataset.
Blockchain-Oriented Privacy Protection of Sensitive Data in the Internet of Vehicles

The Internet of Vehicles is the specific instantiation of the Internet of Things in the field of transportation. Vehicle and driving data are often used to mine information about people’s lifestyles and preferences. Without advanced data encryption and strict authorization measures, personal privacy and security are at great risk. To solve the problem that the relatively low security of vehicles connected to the Internet may threaten the privacy of users, a blockchain-based sensitive data privacy-protection scheme for vehicles connected to the Internet is proposed. First, association rules are used to mine Big Data in the Internet of Vehicles. Then, a data security aggregation protocol is established. Finally, an end-to-end encryption mechanism is created. The privacy protection of Big Data in the Internet of Vehicles is achieved through static and dynamic strategies. The experimental results show that the scheme can improve the hiding rate of sensitive data, the key generation time and encryption time are shorter than those of typical technologies, and the encryption ability is better. Therefore, in protecting the privacy of Big Data in the Internet of Vehicles, the blockchain proposed in this paper has better privacy protection and encryption abilities and is applicable to the protection of sensitive data in the Internet of Vehicles.
Pale Transformer: A General Vision Transformer Backbone with Pale-Shaped Attention

Recently, Transformers have shown promising performance in various vision tasks. To reduce the quadratic computation complexity caused by the global self-attention, various methods constrain the range of attention within a local region to improve its efficiency. Consequently, their receptive fields in a single attention layer are not large enough, resulting in insufficient context modeling. To address this issue, we propose a Pale-Shaped self-Attention (PS-Attention), which performs self-attention within a pale-shaped region. Compared to the global self-attention, PS-Attention can reduce the computation and memory costs significantly. Meanwhile, it can capture richer contextual information under the similar computation complexity with previous local self-attention mechanisms. Based on the PS-Attention, we develop a general Vision Transformer backbone with a hierarchical architecture, named Pale Transformer, which achieves 83.4%, 84.3%, and 84.9% Top-1 accuracy with the model size of 22M, 48M, and 85M respectively for 224x224 ImageNet-1K classification, outperforming the previous Vision Transformer backbones. For downstream tasks, our Pale Transformer backbone performs better than the recent state-of-the-art CSWin Transformer by a large margin on ADE20K semantic segmentation and COCO object detection &amp; instance segmentation. The code will be released on https://github.com/BR-IDL/PaddleViT.
Measuring the Effect of Service Quality and Corporate Image on Student Satisfaction and Loyalty in Higher Learning Institutes of Technical and Vocational Education and Training

The 11th Malaysia Plan has emphasized that TVET HLIs is expected to increase the number of enrolment, improve quantity and quality of graduate to meet industry demand. However, in a competitive higher learning industry, previous researches revealed that TVET HLIs are facing challenges such as service quality and corporate image that potentially effect student satisfaction and loyalty. The main objective of this research is to measure the effects of service quality and corporate image on student satisfaction and loyalty. Stimulus- Organism- Response (SOR) model was adapted in conceptualizing the relationship between the constructs. The scope of study is focus on student from TVET HLIs which is Universiti Kuala Lumpur (UniKL). This study adapted quantitative method whereby questionnaires was used as instrument for data collection. 398 data were collected and analyzed using PLS-SEM. The measurement model indicated that the measurement items are reliable and valid. The structural model indicates that there is a significance and direct effects of service quality and corporate image on student satisfaction and student loyalty. In addition, student satisfaction also has significance and direct effects on student loyalty. In the future research, it is suggest to extend the scope to other TVET HLIs and public HLIs.
A Research on DevOps Maturity Models

Despite the big number of software process models currently available which have been used and practiced for many years, we could not till now totally solve the problem of projects’ late submissions! Meanwhile Software have constantly become bigger, more complex, and require high quality. A recently developed model, called DevOps, aims at producing fast and high-quality releases by bringing the development and operation team to work together. Unfortunately, DevOps is still lacking a clear definition as well as empirical studies that document the experience in implementing and enhancing it. Maturity models are used as a tool to assess the effectiveness of an organizational processes on adopting certain practices and identify what capabilities they need to acquire next in order to improve their performance and reach higher maturity level. However, there are few DevOps maturity models which have been emerged as means to assess DevOps adopted practices. This research aims to identify and benchmark the DevOps maturity models available in literature. We were able to identify several maturity models and compare among them.
PEIGEN – a Platform for Evaluation, Implementation, and Generation of S-boxes

In this paper, a platform named PEIGEN is presented to evaluate security, find efficient software/hardware implementations, and generate cryptographic S-boxes. Continuously developed for decades, S-boxes are constantly evolving in terms of the design criteria for both security requirements and software/hardware performances. PEIGEN is aimed to be a platform covering a comprehensive check-list of design criteria of S-boxes appearing in the literature. To do so, the security requirements are first intensively surveyed, existing tools of S-boxes are then comprehensively compared, and finally our platform PEIGEN is presented. The survey part is aimed to be a systematic reference for the theoretical study of S-boxes. The platform is aimed to be an assistant tool for the experimental study and practical use of S-boxes. PEIGEN not only integrates most of the features in existing tools, but also equips with functionalities to evaluate new security-related properties, improves the efficiency of the search algorithms for optimized implementations in several aspects. With the help of this powerful platform, many interesting observations are made in-between the security notations, as well as on the S-boxes used in the existing symmetrickey cryptographic primitives. PEIGEN will become an open platform and welcomes contributions from all parties to help the community to facilitate the research and use of S-boxes.
Development and validation of students’ digital competence scale (SDiCoS)

Abstract Towards the transition to blended and remote education, evaluating the levels of students’ digital competence and designing educational programs to advance them is of paramount importance. Existing validated digital competence scales usually ignore either important digital skills needed or new socio-technological innovations. This study proposes and validates a comprehensive digital competence scale for students in higher education. The suggested instrument includes skills of online learning and collaboration, social media, smart and mobile devices, safety, and data protection. The scale was evaluated on a sample of 156 undergraduate and postgraduate students just before and at the beginning of the COVID-19 crisis. The final scale is composed of 28 items and six digital competence components. The evaluation study revealed valid results in terms of model fit criteria, factor loadings, internal validity, and reliability. Individual factors like the students’ field of study, computer experience and age revealed significant associations to the scale components, while gender revealed no significant differences. The suggested scale can be useful to the design of new actions and policies towards remote education and the digital skills’ development of adult learners.
ICT and economic growth in Sub-Saharan Africa: Transmission channels and effects

Despite significant progress in the empirical analysis of the impact of information and communication technology (ICT) on economic growth, previous studies have not empirically examined the mechanisms by which ICT hinders or expands economic growth. The specific aim of the present study was to identify the transmission channels through which ICT contributes to economic growth. The examined channels included; openness, FDI inflows, education, domestic investment, political institution, and inflation. The study employed the panel-corrected standard errors (PCSE) and system two step system GMM techniques on a sample of 44 countries in Sub-Saharan Africa (SSA) over the period 2004–2020. Using an ICT composite index, the causal mediation analysis identified that ICT contributed directly to growth, as proposed by the growth theories. Furthermore, the same analysis identified; domestic investment, openness, and education as crucial variables through which ICT penetration indirectly promoted per capita growth in the SSA region. The robustness of this result was verified using a variety of tools. The present findings suggested that the current efforts to expand ICTs in Sub-Saharan Africa should be continued since this sector has a considerable indirect impact on promoting economic growth.
Limitations of Variational Quantum Algorithms: A Quantum Optimal Transport Approach

The impressive progress in quantum hardware in the last years has raised the interest of the quantum computing community in harvesting the computational power of such devices. However, in the absence of error correction, these devices can only reliably implement very shallow circuits or comparatively deeper circuits at the expense of a nontrivial density of errors. In this work, we obtain extremely tight limitation bounds for standard NISQ proposals in both the noisy and noiseless regimes, with or without error-mitigation tools. The bounds limit the performance of both circuit model algorithms, such as QAOA, and also continuous-time algorithms, such as quantum annealing. In the noisy regime with local depolarizing noise $p$, we prove that at depths $L=\mathcal{O}(p^{-1})$ it is exponentially unlikely that the outcome of a noisy quantum circuit outperforms efficient classical algorithms for combinatorial optimization problems like Max-Cut. Although previous results already showed that classical algorithms outperform noisy quantum circuits at constant depth, these results only held for the expectation value of the output. Our results are based on newly developed quantum entropic and concentration inequalities, which constitute a homogeneous toolkit of theoretical methods from the quantum theory of optimal mass transport whose potential usefulness goes beyond the study of variational quantum algorithms.
Dicke Superradiance in Ordered Lattices: Dimensionality Matters

Dicke superradiance in ordered atomic arrays is a phenomenon where atomic synchronization gives rise to a burst in photon emission. This superradiant burst only occurs if there is one -- or just a few -- dominant decay channels. For a fixed atom number, this happens only below a critical interatomic distance. Here we show that array dimensionality is the determinant factor that drives superradiance. In 2D and 3D arrays, superradiance occurs due to constructive interference, which grows stronger with atom number. This leads to a critical distance that scales sublogarithmically with atom number in 2D, and as a power law in 3D. In 1D arrays, superradiance occurs due to destructive interference that effectively switches off certain decay channels, yielding a critical distance that saturates with atom number. Our results provide a guide to explore many-body decay in state-of-the art experimental setups.
Collective Data Harms at the Crossroads of Data Protection and Competition Law: Moving Beyond Individual Empowerment

In an era of big data, harms caused by data technologies can no longer be effectively addressed under the predominant regulatory paradigm of individual empowerment. Even a sophisticated consumer cannot fully protect herself against collective harms triggered by others’ privacy choices or by technologies creating competitive harm without processing personal data or targeting individuals. While data protection and competition law can be applied more proactively to address such harms, difficulties are likely to remain. We therefore submit that stronger regulatory interventions are required to target collective, and sometimes competitive, harm from technologies like pervasive advertising, facial recognition, deepfakes and spyproducts. Big data, individual rights, societal harms, exploitative abuse, enforcement priorities, blacklists, pervasive advertising, facial recognition, deepfakes, spyproducts
ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification

Few-shot text classification has recently been promoted by the meta-learning paradigm which aims to identify target classes with knowledge transferred from source classes with sets of small tasks named episodes. Despite their success, existing works building their meta-learner based on Prototypical Networks are unsatisfactory in learning discriminative text representations between similar classes, which may lead to contradictions during label prediction. In addition, the task-level and instance-level overfitting problems in few-shot text classification caused by a few training examples are not sufficiently tackled. In this work, we propose a contrastive learning framework named ContrastNet to tackle both discriminative representation and overfitting problems in few-shot text classification. ContrastNet learns to pull closer text representations belonging to the same class and push away text representations belonging to different classes, while simultaneously introducing unsupervised contrastive regularization at both task-level and instance-level to prevent overfitting. Experiments on 8 few-shot text classification datasets show that ContrastNet outperforms the current state-of-the-art models.
End-to-End generation of Multiple-Choice questions using Text-to-Text transfer Transformer models

The increasing worldwide adoption of e-learning tools and widespread increase of online education has brought multiple challenges, including the ability of generating assessments at the scale and speed demanded by this environment. In this sense, recent advances in language models and architectures like the Transformer, provide opportunities to explore how to assist educators in these tasks. This study focuses on using neural language models for the generation of questionnaires composed of multiple-choice questions, based on English Wikipedia articles as input. The problem is addressed using three dimensions: Question Generation (QG), Question Answering (QA), and Distractor Generation (DG). A processing pipeline based on pre-trained T5 language models is designed and a REST API is implemented for its use. The DG task is defined using a Text-To-Text format and a T5 model is fine-tuned on the DG-RACE dataset, showing an improvement to ROUGE-L metric compared to the reference for the dataset. A discussion about the lack of an adequate metric for DG is presented and the cosine similarity using word embeddings is considered as a complement. Questionnaires are evaluated by human experts reporting that questions and options are generally well formed, however, they are more oriented to measuring retention than comprehension.
An Application to Detect Cyberbullying Using Machine Learning and Deep Learning Techniques

Nowadays, a lot of people indulge themselves in the world of social media. With the current pandemic scenario, this engagement has only increased as people often rely on social media platforms to express their emotions, find comfort, find like-minded individuals, and form communities. With this extensive use of social media comes many downsides and one of the downsides is cyberbully. Cyberbullying is a form of online harassment that is both unsettling and troubling. It can take many forms, but the most common is a textual format. Cyberbullying is common on social media, and people often end up in a mental breakdown state instead of taking action against the bully. On the majority of social networks, automated detection of these situations necessitates the use of intelligent systems. We have proposed a cyberbullying detection system to address this issue. In this work, we proposed a deep learning framework that will evaluate real-time twitter tweets or social media posts as well as correctly identify any cyberbullying content in them. Recent studies has shown that deep neural network-based approaches are more effective than conventional techniques at detecting cyberbullying texts. Additionally, our application can recognise cyberbullying posts which were written in English, Hindi, and Hinglish (Multilingual data).
Scalable Generation of Multiphoton Entangled States by Active Feed-Forward and Multiplexing

Multiphoton entangled quantum states are key to advancing quantum technologies such as multiparty quantum communications, quantum sensing, or quantum computation. Their scalable generation, however, remains an experimental challenge. Current methods for generating these states rely on stitching together photons from probabilistic sources, and state generation rates drop exponentially in the number of photons. Here, we implement a system based on active feed-forward and multiplexing that addresses this challenge. We demonstrate the scalable generation of four-photon and six-photon Greenberger-Horne-Zeilinger states, increasing generation rates by factors of 9 and 35, respectively. This is consistent with the exponential enhancement compared to the standard nonmultiplexed approach that is predicted by our theory. These results facilitate the realization of practical multiphoton protocols for photonic quantum technologies.
Large-Scale Pre-training for Person Re-identification with Noisy Labels

This paper aims to address the problem of pretraining for person re-identification (Re-ID) with noisy labels. To setup the pretraining task, we apply a simple online multi-object tracking system on raw videos of an existing un-labeled Re-ID dataset “LUPerson” and build the Noisy Labeled variant called “LUPerson-NL”. Since theses ID labels automatically derived from tracklets inevitably con-tain noises, we develop a large-scale Pre-training frame-work utilizing Noisy Labels (PNL), which consists of three learning modules: supervised Re-ID learning, prototype-based contrastive learning, and label-guided contrastive learning. In principle, joint learning of these three mod-ules not only clusters similar examples to one prototype, but also rectifies noisy labels based on the prototype as-signment. We demonstrate that learning directly from raw videos is a promising alternative for pre-training, which utilizes spatial and temporal correlations as weak super-vision. This simple pre-training task provides a scalable way to learn SOTA Re-ID representations from scratch on “LUPerson-NL” without bells and whistles. For example, by applying on the same supervised Re-ID method MGN, our pre-trained model improves the mAP over the unsu-pervised pre-training counterpart by 5.7%, 2.2%, 2.3% on CUHK03, DukeMTMC, and MSMT17 respectively. Under the small-scale or few-shot setting, the performance gain is even more significant, suggesting a better transferability of the learned representation. Code is available at https://github.com/DengpanFu/LUPerson-NL.