Reviewerly/exHarmony huggingface dataset:
Overview of EXIST 2023: sEXism Identification in Social NeTworks

The paper describes the lab on Sexism identification in social networks (EXIST 2023) that will be hosted as a lab at the CLEF 2023 conference. The lab consists of three tasks, two of which are continuation of EXIST 2022 (sexism detection and sexism categorization) and a third and novel one on source intention identification. For this edition new test and training data will be provided and some novelties are introduced in order to tackle two central problems of Natural Language Processing (NLP): bias and fairness. Firstly, the sampling and data gathering process will take into account different sources of bias in data: seed, temporal and user bias. During the annotation process we will also consider some sources of "label bias" that come from the social and demographic characteristics of the annotators. Secondly, we will adopt the "learning with disagreements" paradigm by providing datasets containing also pre-aggregated annotations, so that systems can make use of this information to learn from different perspectives. The general goal of the EXIST shared tasks is to advance the state of the art in online sexism detection and categorization, as well as investigating to what extent bias can be characterized in data and whether systems may take fairness decisions when learning from multiple annotations.
Scene-Centric vs. Object-Centric Image-Text Cross-Modal Retrieval: A Reproducibility Study

Most approaches to (CMR) focus either on object-centric datasets, meaning that each document depicts or describes a single object, or on scene-centric datasets, meaning that each image depicts or describes a complex scene that involves multiple objects and relations between them. We posit that a robust CMR model should generalize well across both dataset types. Despite recent advances in CMR, the reproducibility of the results and their generalizability across different dataset types has not been studied before. We address this gap and focus on the reproducibility of the state-of-the-art CMR results when evaluated on object-centric and scene-centric datasets. We select two state-of-the-art CMR models with different architectures: (i) CLIP; and (ii) X-VLM. Additionally, we select two scene-centric datasets, and three object-centric datasets, and determine the relative performance of the selected models on these datasets. We focus on reproducibility, replicability, and generalizability of the outcomes of previously published CMR experiments. We discover that the experiments are not fully reproducible and replicable. Besides, the relative performance results partially generalize across object-centric and scene-centric datasets. On top of that, the scores obtained on object-centric datasets are much lower than the scores obtained on scene-centric datasets. For reproducibility and transparency we make our source code and the trained models publicly available.
Byzantine-Resilient Federated Learning at Edge

Both Byzantine resilience and communication efficiency have attracted tremendous attention recently for their significance in edge federated learning. However, most existing algorithms may fail when dealing with real-world irregular data that behaves in a heavy-tailed manner. To address this issue, we study the stochastic convex and non-convex optimization problem for federated learning at edge and show how to handle heavy-tailed data while retaining the Byzantine resilience, communication efficiency and the optimal statistical error rates simultaneously. Specifically, we first present a Byzantine-resilient distributed gradient descent algorithm that can handle the heavy-tailed data and meanwhile converge under the standard assumptions. To reduce the communication overhead, we further propose another algorithm that incorporates gradient compression techniques to save communication costs during the learning process. Theoretical analysis shows that our algorithms achieve order-optimal statistical error rate in presence of Byzantine devices. Finally, we conduct extensive experiments on both synthetic and real-world datasets to verify the efficacy of our algorithms.
Adoption of Blockchain Technology in Supply Chain

Supply chain management processes and systems in different industries include multiple variables. This study examines the adoption of blockchain technology in supply chain in Malaysia. Data were collected through questionnaire designed as open ended question through 300 respondents while only 256 is used according to completed surveys, statistical analysis of the data obtained in this study was carried out by one sample t-test using the statistical software package (SPSS). we find that, perceived ease, Inter-Organizational Trust, Perceived Usefulness, Data transparency and confidentiality have significant impact on adoption Blockchain in supply chain, while Blockchain technology simplifies inventory financing. The paper use open ended questions so in future can use different kind of scale and different variables which can affect the adoption decisions.
A Framework for Constructing a Technology -Enhanced Education Metaverse: Learner Engagement With Human–Machine Collaboration

The education metaverse (Edu-Metaverse), as a simulated extension of the real world, is an infinite virtual space where learners can build their relationships with others and create interactive content. However, preparing learners to engage fully with Edu-Metaverse remains challenging. As technologies on Edu-Metaverse are new to learners, there is a lack of studies on how to enhance learner engagement with human–machine collaboration. Therefore, this study proposes a technology-enhanced Edu-Metaverse framework for facilitating learner engagement with human–machine interactions. The framework consists of two major components: technology enhancement of Edu-Metaverse and interactions among learners and avatars in Edu-Metaverse contexts. In the proposed framework, Edu-Metaverse reshapes the relationships between humans and machines. With the support of human–machine collaboration, learner engagement could be concluded in three patterns: training engagement guided by Edu-Metaverse; collaboration engagement supported by Edu-Metaverse; and creative engagement empowered by Edu-Metaverse. In addition, a case analysis is conducted to validate the Edu-Metaverse theoretical framework and engagement patterns. The proposed framework can provide a more holistic understanding of the Edu-Metaverse implications as well as learning designs that are aimed at enhancing learner engagement with the support of human–machine collaboration.
A Survey of MulVAL Extensions and Their Attack Scenarios Coverage

Organizations employ various adversary models to assess the risk and potential impact of attacks on their networks. A popular method of visually representing cyber risks is the attack graph. Attack graphs represent vulnerabilities and actions an attacker can take to identify and compromise an organization’s assets. Attack graphs facilitate the visual presentation and algorithmic analysis of attack scenarios in the form of attack paths. MulVAL is a generic open-source framework for constructing logical attack graphs, which has been widely used by researchers and practitioners and extended by them with additional attack scenarios. This paper surveys all of the existing MulVAL extensions and maps all MulVAL interaction rules to MITRE ATT&CK Techniques to estimate their attack scenarios coverage. This survey aligns current MulVAL extensions along unified ontological concepts and highlights the existing gaps. It paves the way for the systematic improvement of MulVAL and the comprehensive modeling of the entire landscape of adversarial behaviors captured in MITRE ATT&CK.
Underwater Image Enhancement Using Deep Transfer Learning Based on a Color Restoration Model

In ocean engineering, an underwater vehicle is widely used as an important equipment to explore the ocean. However, due to the reflection and attenuation of light when propagating in water, the images captured by the visual system of an underwater vehicle in the complex underwater environment usually suffer from low visibility, blurred details, and color distortion. To solve this problem, in this article, we present an underwater image enhancement framework based on transfer learning, which consists of a domain transformation module and an image enhancement module. The two modules, respectively, perform color correction and image enhancement, effectively transferring in-air image dehazing to underwater image enhancement. To maintain the physical properties of an underwater image, we embed the physical model into the domain transformation module which ensures that the transformed image complies with the physical model. To effectively remove the color deviation, a coarse-grained similarity calculation is added to the domain transformation module to improve the model performance. The experimental results on real-world underwater images of different scenes show that the presented method is superior to some advanced underwater image enhancement algorithms both qualitatively and quantitatively. Furthermore, we conduct ablation experiments to indicate the contribution of each component and further validate the effectiveness of the presented method through application tests.
Latency considerations for stochastic optimizers in variational quantum algorithms

Variational quantum algorithms, which have risen to prominence in the noisy intermediate-scale quantum setting, require the implementation of a stochastic optimizer on classical hardware. To date, most research has employed algorithms based on the stochastic gradient iteration as the stochastic classical optimizer. In this work we propose instead using stochastic optimization algorithms that yield stochastic processes emulating the dynamics of classical deterministic algorithms. This approach results in methods with theoretically superior worst-case iteration complexities, at the expense of greater per-iteration sample (shot) complexities. We investigate this trade-off both theoretically and empirically and conclude that preferences for a choice of stochastic optimizer should explicitly depend on a function of both latency and shot execution times.
MjSO: A modified differential evolution with a probability selection mechanism and a directed mutation strategy

To further enhance the performance of jSO, the most representative variant of differential evolution, a modified differential devolution with a probability selection mechanism and a directed mutation strategy named MjSO is developed in this study. There are three main modifications for jSO. Firstly, a novel probability selection mechanism is utilized in mutation process to fully take advantage of dominant individuals. Specifically, the better dominant individual has a higher probability of being selected. Then, a directed mutation strategy is adopted to ensure the evolution towards a better direction during the optimization. Moreover, the archive scheme is no longer adopted in MjSO to avoid the excessive use of historically dominant individuals. MjSO is evaluated using the CEC 2018 test suite in comparison with six state-of-the-art DE variants, called jSO, mL-SHADE, ELSHADE-SPACMA, EB-LSHADE, iL-SHADE, and MadDE. The numerical experimental results certify that MjSO outperforms the six comparison algorithms. Moreover, MjSO significantly enhances the performance of jSO while reducing its time complexity. In conclusion, the probability selection mechanism and directed mutation strategy proposed in this study efficiently enhance the performance of jSO, and MjSO is a promising algorithm.
Surrogate-based optimization for variational quantum algorithms

Variational quantum algorithms are a class of techniques intended to be used on near-term quantum computers. The goal of these algorithms is to perform large quantum computations by breaking the problem down into a large number of shallow quantum circuits, complemented by classical optimization and feedback between each circuit execution. One path for improving the performance of these algorithms is to enhance the classical optimization technique. Given the relative ease and abundance of classical computing resources, there is ample opportunity to do so. In this work, we introduce the idea of learning surrogate models for variational circuits using few experimental measurements, and then performing parameter optimization using these models as opposed to the original data. We demonstrate this idea using a surrogate model based on kernel approximations, through which we reconstruct local patches of variational cost functions using batches of noisy quantum circuit results. Through application to the quantum approximate optimization algorithm and preparation of ground states for molecules, we demonstrate the superiority of surrogate-based optimization over commonly-used optimization techniques for variational algorithms.
The effect of digital literacy on technology acceptance: An evaluation on administrative staff in higher education

Digital literacy is a critical skill that administrative staff must acquire to continue their business activities at higher education. However, digital literacy studies for administrative staff seem to be neglected in the current literature. This article examines the impact of higher education administrative staff’s digital literacy on their intention to use digital technologies while performing their tasks. For this purpose, a conceptual model consisting of effort expectancy and performance expectancy structures based on the unified theory of acceptance and use of technology and expanded with the digital literacy dimension has been created. Data were collected from 158 participants who were administrative staff of two higher education institutions in Türkiye to evaluate the theoretical model. The data were analysed using the structural equation modelling technique. The findings revealed the relationship between the digital literacy skills of higher education administrative staff and their intention to use digital technology. According to the results, digital literacy has a direct effect on effort expectancy but not on performance expectancy. Also, contrary to our expectations, digital literacy does not directly affect the intention to use. However, digital literacy affects the intention to use digital technology through effort expectancy and performance expectancy. In higher education, personnel with low digital literacy skills should be identified and in-service training should be provided. This is one of the first studies to address the impact of digital literacy on technology acceptance by administrative staff working in higher education.
Bilevel Topic Model‐Based Multitask Learning for Constructed‐Responses Multidimensional Automated Scoring and Interpretation

Abstract Multidimensional scoring evaluates each constructed‐response answer from more than one rating dimension and/or trait such as lexicon, organization, and supporting ideas instead of only one holistic score, to help students distinguish between various dimensions of writing quality. In this work, we present a bilevel learning model for combining two objectives, the multidimensional automated scoring, and the students’ writing structure analysis and interpretation. The dual objectives are enabled by a supervised model, called Latent Dirichlet Allocation Multitask Learning (LDAMTL), integrating a topic model and a multitask learning model with an attention mechanism. Two empirical data sets were employed to indicate LDAMTL model performance. On one hand, results suggested that LDAMTL owns better scoring and QW‐ κ values than two other competitor models, the supervised latent Dirichlet allocation, and Bidirectional Encoder Representations from Transformers at the 5% significance level. On the other hand, extracted topic structures revealed that students with a higher language score tended to employ more compelling words to support the argument in their answers. This study suggested that LDAMTL not only demonstrates the model performance by conjugating the underlying shared representation of each topic and learned representation from the neural networks but also helps understand students’ writing.
Pedestrian detection model based on Tiny-Yolov3 architecture for wearable devices to visually impaired assistance

Introduction: Wearable assistive devices for the visually impaired whose technology is based on video camera devices represent a challenge in rapid evolution, where one of the main problems is to find computer vision algorithms that can be implemented in low-cost embedded devices. Objectives and Methods: This work presents a Tiny You Only Look Once architecture for pedestrian detection, which can be implemented in low-cost wearable devices as an alternative for the development of assistive technologies for the visually impaired. Results: The recall results of the proposed refined model represent an improvement of 71% working with four anchor boxes and 66% with six anchor boxes compared to the original model. The accuracy achieved on the same data set shows an increase of 14% and 25%, respectively. The F1 calculation shows a refinement of 57% and 55%. The average accuracy of the models achieved an improvement of 87% and 99%. The number of correctly detected objects was 3098 and 2892 for four and six anchor boxes, respectively, whose performance is better by 77% and 65% compared to the original, which correctly detected 1743 objects. Discussion: Finally, the model was optimized for the Jetson Nano embedded system, a case study for low-power embedded devices, and in a desktop computer. In both cases, the graphics processing unit (GPU) and central processing unit were tested, and a documented comparison of solutions aimed at serving visually impaired people was performed. Conclusion: We performed the desktop tests with a RTX 2070S graphics card, and the image processing took about 2.8 ms. The Jetson Nano board could process an image in about 110 ms, offering the opportunity to generate alert notification procedures in support of visually impaired mobility.
Smart Cities and Homes: Current Status and Future Possibilities

The advancement of sensing technologies, embedded systems, wireless communication technologies, nanomaterials, miniaturization, vision sensing and processing speed have made it possible to develop smart technologies that can generate data seamlessly [...]
Multi-Method Diagnosis of Histopathological Images for Early Detection of Breast Cancer Based on Hybrid and Deep Learning

Breast cancer (BC) is a type of cancer suffered by adult females worldwide. A late diagnosis of BC leads to death, so early diagnosis is essential for saving lives. There are many methods of diagnosing BC, including surgical open biopsy (SOB), which however constitutes an intense workload for pathologists to follow SOB and additionally takes a long time. Therefore, artificial intelligence systems can help by accurately diagnosing BC earlier; it is a tool that can assist doctors in making sound diagnostic decisions. In this study, two proposed approaches were applied, each with two systems, to diagnose BC in a dataset with magnification factors (MF): 40×, 100×, 200×, and 400×. The first proposed method is a hybrid technology between CNN (AlexNet and GoogLeNet) models that extracts features and classify them using the support vector machine (SVM). Thus, all BC datasets were diagnosed using AlexNet + SVM and GoogLeNet + SVM. The second proposed method diagnoses all BC datasets by ANN based on combining CNN features with handcrafted features extracted using the fuzzy color histogram (FCH), local binary pattern (LBP), and gray level co-occurrence matrix (GLCM), which collectively is called fusion features. Finally, the fusion features were fed into an artificial neural network (ANN) for classification. This method has proven its superior ability to diagnose histopathological images (HI) of BC accurately. The ANN algorithm based on fusion features achieved results of 100% for all metrics with the 400× dataset.
Analyzing the Performance of Sentiment Analysis using BERT, DistilBERT, and RoBERTa

Sentiment analysis or opinion mining is a natural language processing (NLP) technique to identify, extract, and quantify the emotional tone behind a body of text. It helps to capture public opinion and user interests on various topics based on comments on social events, product reviews, film reviews, etc. Linear Regression, Support Vector Machines, Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), LSTM (Long Short Term Memory), and other machine learning and deep learning algorithms can be used to analyze the sentiment behind a text. This work analyses the sentiments behind movie reviews and tweets using the Coronavirus tweets NLP dataset and Sentiment140 dataset. Three advanced transformer-based deep learning models like BERT, DistilBERT, and RoBERTa are experimented with to perform the sentiment analysis. Finally, the performance obtained using these models on these two different datasets is compared using the accuracy as the performance evaluation matrix. On analyzing the performance, it can be seen that the BERT model outperforms the other two models.
Spanish Pre-Trained CaTrBETO Model for Sentiment Classification in Twitter

The classification and analysis of sentiments allow us to study the behavior of human beings concerning a specific topic. Our work proposes using a multimodal trained model for sentiment classification in Spanish, incorporating tweets. Our model is called CaTrBETO, an approach based on a caption transformer (CaTr) and BETO (BERT model trained on an extensive Spanish corpus). The proposed model allows us to understand the sentiments in tweets through the combination of images and text. We test our approach using the Ecuadorian prison crisis 2021–2022, obtaining good results without extensive data. Furthermore, we demonstrate that our model exceeds the accuracy of BETO's previous work.
Improving Video Retrieval Using Multilingual Knowledge Transfer

Video retrieval has seen tremendous progress with the development of vision-language models. However, further improving these models require additional labelled data which is a huge manual effort. In this paper, we propose a framework MKTVR, that utilizes knowledge transfer from a multilingual model to boost the performance of video retrieval. We first use state-of-the-art machine translation models to construct pseudo ground-truth multilingual video-text pairs. We then use this data to learn a video-text representation where English and non-English text queries are represented in a common embedding space based on pretrained multilingual models. We evaluate our proposed approach on four English video retrieval datasets such as MSRVTT, MSVD, DiDeMo and Charades. Experimental results demonstrate that our approach achieves state-of-the-art results on all datasets outperforming previous models. Finally, we also evaluate our model on a multilingual video-retrieval dataset encompassing six languages and show that our model outperforms previous multilingual video retrieval models in a zero-shot setting.
Seeing microplastic clouds: Using ecomedia literacy for digital technology in environmental education

Connecting environmental and digital media education with the guiding metaphor of water, this article explores ecomedia literacy, a methodology that prompts students to perform a holistic analysis and systems thinking of gadgets using four zones of inquiry: ecoculture, political ecology, ecomateriality, and lifeworld. Ecoculture involves studying the discourses, symbols, and stories associated with gadgets. Political ecology relates to the ideological aspects of gadgets and how their production chain is facilitated by social structures. Materiality corresponds with how the gadget is made and what materials it's composed of. Lifeworld is the phenomenological experience of the gadget and how it impacts sensory, cognitive, and emotional experience. This article reflects on student research performed utilizing ecomedia literacy and the insights they generated about the environmental impacts of their personal gadgets.