Reviewerly/exHarmony huggingface dataset:
Reducing Vision-Answer Biases for Multiple-Choice VQA

Multiple-choice visual question answering (VQA) is a challenging task due to the requirement of thorough multi-modal understanding and complicated inter-modality relationship reasoning. To solve the challenge, previous approaches usually resort to different multimodal interaction modules. Despite their effectiveness, we find that existing methods may exploit a new discovered bias (vision-answer bias) to make answer prediction, leading to suboptimal VQA performances and poor generalization. To solve the issues, we propose a Causality-based Multimodal Interaction Enhancement (CMIE) method, which is model-agnostic and can be seamlessly incorporated into a wide range of VQA approaches in a plug-and-play manner. Specifically, our CMIE contains two key components: a causal intervention module and a counterfactual interaction learning module. The former devotes to removing the spurious correlation between the visual content and the answer caused by the vision-answer bias, and the latter helps capture discriminative inter-modality relationships by directly supervising multimodal interaction training via an interactive loss. Extensive experimental results on three public benchmarks and one reorganized dataset show that the proposed method can significantly improve seven representative VQA models, demonstrating the effectiveness and generalizability of the CMIE.
Efficient minimizer orders for large values of<i>k</i>using minimum decycling sets

Minimizers are ubiquitously used in data structures and algorithms for efficient searching, mapping, and indexing of high-throughput DNA sequencing data. Minimizer schemes select a minimum k -mer in every L -long subsequence of the target sequence, where minimality is with respect to a predefined k -mer order. Commonly used minimizer orders select more k -mers than necessary and therefore provide limited improvement in runtime and memory usage of downstream analysis tasks. The recently introduced universal k -mer hitting sets produce minimizer orders with fewer selected k -mers. Generating compact universal k -mer hitting sets is currently infeasible for k &gt; 13, and thus, they cannot help in the many applications that require minimizer orders for larger k . Here, we close the gap of efficient minimizer orders for large values of k by introducing decycling - set - based minimizer orders : new minimizer orders based on minimum decycling sets. We show that in practice these new minimizer orders select a number of k -mers comparable to that of minimizer orders based on universal k -mer hitting sets and can also scale to a larger k . Furthermore, we developed a method that computes the minimizers in a sequence on the fly without keeping the k -mers of a decycling set in memory. This enables the use of these minimizer orders for any value of k . We expect the new orders to improve the runtime and memory usage of algorithms and data structures in high-throughput DNA sequencing analysis.
RETRACTED: Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images

Ovarian cancer remains the leading gynecological cause of cancer mortality. Predicting the sensitivity of ovarian cancer to chemotherapy at the time of pathological diagnosis is a goal of precision medicine research that we have addressed in this study using a novel deep-learning neural network framework to analyze the histopathological images.We have developed a method based on the Inception V3 deep learning algorithm that complements other methods for predicting response to standard platinum-based therapy of the disease. For the study, we used histopathological H&E images (pre-treatment) of high-grade serous carcinoma from The Cancer Genome Atlas (TCGA) Genomic Data Commons portal to train the Inception V3 convolutional neural network system to predict whether cancers had independently been labeled as sensitive or resistant to subsequent platinum-based chemotherapy. The trained model was then tested using data from patients left out of the training process. We used receiver operating characteristic (ROC) and confusion matrix analyses to evaluate model performance and Kaplan-Meier survival analysis to correlate the predicted probability of resistance with patient outcome. Finally, occlusion sensitivity analysis was piloted as a start toward correlating histopathological features with a response.The study dataset consisted of 248 patients with stage 2 to 4 serous ovarian cancer. For a held-out test set of forty patients, the trained deep learning network model distinguished sensitive from resistant cancers with an area under the curve (AUC) of 0.846 ± 0.009 (SE). The probability of resistance calculated from the deep-learning network was also significantly correlated with patient survival and progression-free survival. In confusion matrix analysis, the network classifier achieved an overall predictive accuracy of 85% with a sensitivity of 73% and specificity of 90% for this cohort based on the Youden-J cut-off. Stage, grade, and patient age were not statistically significant for this cohort size. Occlusion sensitivity analysis suggested histopathological features learned by the network that may be associated with sensitivity or resistance to the chemotherapy, but multiple marker studies will be necessary to follow up on those preliminary results.This type of analysis has the potential, if further developed, to improve the prediction of response to therapy of high-grade serous ovarian cancer and perhaps be useful as a factor in deciding between platinum-based and other therapies. More broadly, it may increase our understanding of the histopathological variables that predict response and may be adaptable to other cancer types and imaging modalities.
Eksplorasi Penggunaan ChatGPT dalam Penulisan Artikel Pendidikan Matematika

This study aims to explore the potential of ChatGPT, an artificial intelligence platform developed by OpenAI, in assisting the process of writing research articles on mathematics education. This research is a qualitative descriptive study that collects a sample of data which is then checked using Turnitin to determine the plagiarism level of the contents of articles created by ChatGPT. Nonetheless, this study has limitations in the limited number of samples due to time and resource constraints. The purpose of this research is to find out how ChatGPT can help in the process of making math education research articles and whether it can improve the quality of the research articles that are made. The results of this research are expected to contribute to the latest studies on ChatGPT's ability to assist in the process of writing math education research articles, as well as provide input for teachers, lecturers, and other researchers in utilizing ChatGPT as a tool in the process of writing research articles.
The effect of Instagram on millennials consumer’s purchase intentions in the fashion industry

The purpose of the current research is to explore the impact of Instagram pages on consumers’ purchasing intentions among millennials in the fashion industry in Jordan. This study uses a quantitative, cause-effect, and cross-sectional approach. Online surveys were used to collect data from 212 respondents through different social media tools. The collected data was analyzed by SPSS software and Smart PLS to test the research hypothesis. Results show that bloggers’ recommendations significantly affect eWOM and engagement; usefulness information significantly affects eWOM and engagement; while trust insignificantly affects eWOM and engagement; brand familiarity insignificantly affects eWOM and engagement; participation and socialization insignificantly affect eWOM and engagement. Finally, useful information, eWOM, and engagement significantly affect consumers buying intention on Instagram. The study gives new information about the influence of Instagram pages on consumers' intentions. Therefore, this research expands the knowledge about factors that affect customers’ buying intentions. Since the study is a quantitative cross-sectional conducted on fashion industry Instagram users through an online survey in Jordan, which may limit its generalization to other industries and countries, therefore, the study suggests applying similar studies to online users of different ages, industries, and countries. Marketers can use Instagram to contact, promote, advertise, and sell their products by developing strong relationships with their customers through different social media tools. Using social media tools for marketing and selling reduces paperwork, printed advertisement, and transportation, which positively affects corporate social responsibility and reduces the consumption of energy and pollution.
Tunable optical differential operation based on graphene at a telecommunication wavelength

Optical differential operation based on the photonic spin Hall effect(SHE) has attracted extensive attention in image processing of edge detection, which has advantages of high speed, parallelism, and low power consumption. Here, we theoretically demonstrate tunable optical differential operation in a four-layered nanostructure of prism-graphene-air gap-substrate. It is shown that the spatial differentiation arises inherently from the photonic SHE. Furthermore, we find that the transverse spin-Hall shift induced by the photonic SHE changes dramatically near the Brewster angle with the incident angle increases at a telecommunication wavelength. Meanwhile, the Fermi energy of graphene and the thickness of the air gap can affect the transverse spin shift. Interestingly, we can easily adjust the Fermi energy of graphene in real time through external electrostatic field biasing, enabling fast edge imaging switching at a telecommunication wavelength. This may provide a potential way for future tunable spin-photonic devices, and open up more possible applications for artificial intelligence, such as target recognition, biomedical imaging, and edge detection.
A Holistic Review of Cyber–Physical–Social Systems: New Directions and Opportunities

A Cyber–Physical–Social System (CPSS) is an evolving subset of Cyber–Physical Systems (CPS), which involve the interlinking of the cyber, physical, and social domains within a system-of-systems mindset. CPSS is in a growing state, which combines secure digital technologies with physical systems (e.g., sensors and actuators) and incorporates social aspects (e.g., human interactions and behaviors, and societal norms) to facilitate automated and secure services to end-users and organisations. This paper reviews the field of CPSS, especially in the scope of complexity theory and cyber security to determine its impact on CPS and social media’s influence activities. The significance of CPSS lies in its potential to provide solutions to complex societal problems that are difficult to address through traditional approaches. With the integration of physical, social, and cyber components, CPSS can realize the full potential of IoT, big data analytics, and machine learning, leading to increased efficiency, improved sustainability and better decision making. CPSS presents exciting opportunities for innovation and advancement in multiple domains, improving the quality of life for people around the world. Research challenges to CPSS include the integration of hard and soft system components within all three domains, in addition to sociological metrics, data security, processing optimization and ethical implications. The findings of this paper note key research trends in the fields of CPSS, and recent novel contributions, followed by identified research gaps and future work.
Specifics of the new paradigm of higher education in the context of its digitalization

Introduction. The article examines the problem of digital transformation of higher education. The purpose of the study is to identify the specifics of the new paradigm of higher education in the context of its digitalization. Materials and Methods. The methodology of scientific research consists of phenomenological, philosophical and anthropological approaches and the principle of correlation, which allow us to consider the problem under study in the context of substantiating a new paradigm of higher education, identifying its specifics. The research methods include a comparative analysis of scientific theories and concepts, which allows to reveal the opportunities and risks of digital education in the international field, as well as synthesis and generalization. Following the paradigm approach makes it possible to explain addressing the interpretation of the new educational paradigm. Results. The authors consider the problem of digital education, defined as a change in the relationship between a person and digital technologies, on the one hand, and as the development of a relationship between a person and a cultural and communication environment, on the other hand. The specifics of the new (techno-anthropo-oriented) paradigm of higher education in the context of its digitalization are revealed. The new paradigm is a reflection that demonstrates the interaction of a person with the technological environment; its content is the cultural and communication environment where a new type of dialogue is formed on the basis of practice. Within the new paradigm, a person who creates meaning appears as a creator of themselves and the meaning of cultural communication by means of digital technologies. The techno-cultural function of the paradigm allows to act in the educational process of value-semantic and digital cultural-instrumental practices. The authors argue that in the new paradigm of education, a person surrounded by technology is focused on a new type of dialogue, strives to possess digital culture, to be its member and creator. Conclusions. The authors summarize the characteristic features of the new paradigm of higher education in the context of its digitalization.
SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis

Existing deep neural networks for histopathology image synthesis cannot generate image styles that align with different organs, and cannot produce accurate boundaries of clustered nuclei. To address these issues, we propose a style-guided instance-adaptive normalization (SIAN) approach to synthesize realistic color distributions and textures for histopathology images from different organs. SIAN contains four phases, semantization, stylization, instantiation, and modulation. The first two phases synthesize image semantics and styles by using semantic maps and learned image style vectors. The instantiation module integrates geometrical and topological information and generates accurate nuclei boundaries. We validate the proposed approach on a multiple-organ dataset, Extensive experimental results demonstrate that the proposed method generates more realistic histopathology images than four state-of-the-art approaches for five organs. By incorporating synthetic images from the proposed approach to model training, an instance segmentation network can achieve state-of-the-art performance.
A Joint Encryption and Compression Algorithm for Multiband Remote Sensing Image Transmission

Due to the increasing capabilities of cybercriminals and the vast quantity of sensitive data, it is necessary to protect remote sensing images during data transmission with “Belt and Road” countries. Joint image compression and encryption techniques exhibit reliability and cost-effectiveness for data transmission. However, the existing methods for multiband remote sensing images have limitations, such as extensive preprocessing times, incompatibility with multiple bands, and insufficient security. To address the aforementioned issues, we propose a joint encryption and compression algorithm (JECA) for multiband remote sensing images, including a preprocessing encryption stage, crypto-compression stage, and decoding stage. In the first stage, multiple bands from an input image can be spliced together in order from left to right to generate a grayscale image, which is then scrambled at the block level by a chaotic system. In the second stage, we encrypt the DC coefficient and AC coefficient. In the final stage, we first decrypt the DC coefficient and AC coefficient, and then restore the out-of-order block through the chaotic system to get the correct grayscale image. Finally, we postprocess the grayscale image and reconstruct it into a remote sensing image. The experimental results show that JECA can reduce the preprocessing time of the sender by 50% compared to existing joint encryption and compression methods. It is also compatible with multiband remote sensing images. Furthermore, JECA improves security while maintaining the same compression ratio as existing methods, especially in terms of visual security and key sensitivity.
A Smart Home Digital Twin to Support the Recognition of Activities of Daily Living

One of the challenges in the field of human activity recognition in smart homes based on IoT sensors is the variability in the recorded data. This variability arises from differences in home configurations, sensor network setups, and the number and habits of inhabitants, resulting in a lack of data that accurately represent the application environment. Although simulators have been proposed in the literature to generate data, they fail to bridge the gap between training and field data or produce diverse datasets. In this article, we propose a solution to address this issue by leveraging the concept of digital twins to reduce the disparity between training and real-world data and generate more varied datasets. We introduce the Virtual Smart Home, a simulator specifically designed for modeling daily life activities in smart homes, which is adapted from the Virtual Home simulator. To assess its realism, we compare a set of activity data recorded in a real-life smart apartment with its replication in the VirtualSmartHome simulator. Additionally, we demonstrate that an activity recognition algorithm trained on the data generated by the VirtualSmartHome simulator can be successfully validated using real-life field data.
Advancements in heat exchanger design for waste heat recovery in industrial processes

This project shows the efficiency of the heat exchanger model and how effectively the model can transmit from one medium to another medium. The heat recovery in the industrial process gets improved after using this model in the industries. The heat exchanger model has good accuracy in transmitting heat from one medium to another medium.
Development of a Metaverse Online Learning System for Undergraduate Nursing Students

An active online learning environment enables 2-way communication wherein students can engage in problem-based learning and projects, unlike fragmented lecture-style classes.This pilot study aimed to develop a metaverse-based online learning system and evaluate its usability.A rapid prototyping model and Gather.town was used to design and develop a metaverse classroom. Participants were 10 nursing students from a college in South Korea. To evaluate usability, 10 tasks were configured and 2 pilot tests were conducted. The degree of difficulty, time required to perform tasks, and students' experience were investigated.The Metaverse for Education of Nursing Students was successfully completed, incorporating student feedback and addressing identified areas for improvement.This study reflects a learner-centered educational environment through the direct participation in the development process of the instructors who conducted the lectures. The metaverse space can be widely applied in creative nursing education in the future.
DyLoRA: Parameter-Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation

With the ever-growing size of pretrained models (PMs), fine-tuning them has become more expensive and resource-hungry. As a remedy, low-rank adapters (LoRA) keep the main pretrained weights of the model frozen and just introduce some learnable truncated SVD modules (so-called LoRA blocks) to the model. While LoRA blocks are parameter-efficient, they suffer from two major problems: first, the size of these blocks is fixed and cannot be modified after training (for example, if we need to change the rank of LoRA blocks, then we need to re-train them from scratch); second, optimizing their rank requires an exhaustive search and effort. In this work, we introduce a dynamic low-rank adaptation (DyLoRA) technique to address these two problems together. Our DyLoRA method trains LoRA blocks for a range of ranks instead of a single rank by sorting the representation learned by the adapter module at different ranks during training. We evaluate our solution on different natural language understanding (GLUE benchmark) and language generation tasks (E2E, DART and WebNLG) using different pretrained models such as RoBERTa and GPT with different sizes. Our results show that we can train dynamic search-free models with DyLoRA at least 4 to 7 times (depending to the task) faster than LoRA without significantly compromising performance. Moreover, our models can perform consistently well on a much larger range of ranks compared to LoRA.
Progressive Multi-resolution Loss for Crowd Counting

Crowd counting is usually handled in a density map regression fashion, which is supervised via an L2 loss between the predicted density map and ground truth. To effectively regulate models, various improved L2 loss functions have been developed to find a better correspondence between predicted density and annotation positions. In this paper, we propose to predict the density map at one resolution but measure its quality via a derived log-formed loss at multiple resolutions. Unlike existing methods that assume density maps at different resolutions are independent, our loss is obtained by modeling the likelihood function inspired by the relationship of density maps across multi-resolutions. We find that the traditional single-resolution L2 loss is a particular case of our derived log-likelihood. We mathematically prove it is superior to a single-resolution L2 loss. Without bells and whistles, the proposed loss substantially improves several baselines and performs favorably compared to state-of-the-art methods on five crowd counting datasets: NWPU-Crowd, ShanghaiTech A & B, UCF-QNRF, and JHU-Crowd++. The source code and trained models are released at https://github.com/streamer-AP/PML_Loss.git.
The complexity of NISQ

Abstract The recent proliferation of NISQ devices has made it imperative to understand their power. In this work, we define and study the complexity class , which encapsulates problems that can be efficiently solved by a classical computer with access to noisy quantum circuits. We establish super-polynomial separations in the complexity among classical computation, , and fault-tolerant quantum computation to solve some problems based on modifications of Simon’s problems. We then consider the power of for three well-studied problems. For unstructured search, we prove that cannot achieve a Grover-like quadratic speedup over classical computers. For the Bernstein-Vazirani problem, we show that only needs a number of queries logarithmic in what is required for classical computers. Finally, for a quantum state learning problem, we prove that is exponentially weaker than classical computers with access to noiseless constant-depth quantum circuits.