Reviewerly/exHarmony huggingface dataset:
Fine-Grained Image Recognition by Integrating Transformer Encoder Blocks in a Robust Single Stage Object Detector

Fine-grained image classification remains an ongoing challenge in the computer vision field, which is particularly intended to identify objects within sub-categories. It is a difficult task since there is a minimal and substantial intra-class variance. The current methods address the issue by first locating selective regions with Region Proposal Networks (RPN), object localization, or part localization, followed by implementing a CNN Network or SVM classifier to those selective regions. This approach, however, makes the process simple by implementing a single-stage end-to-end feature encoding with a localization method, which leads to improved feature representations of individual tokens/regions by integrating the transformer encoder blocks into the Yolov5 backbone structure. These Transformer Encoder Blocks, with their self-attention mechanism, effectively captured the global dependencies and enabled the model to learn relationships between distant regions. This improved the model ability to understand context and captured long-range spatial relationships in the image. We also replaced the Yolov5 detection heads with three transformer heads at the output for object recognition using the discriminative and informative features maps from transformer encoder blocks. We established the potential of the single stage detector for the fine-grained image recognition task, by achieving state of the art 93.4% accuracy, as well as outperforming the existing Yolov5 model. The effectiveness of our approach is assessed using the Stanford car dataset, which includes 16,185 images of 196 different classes of vehicles with significantly identical visual appearances.
Parasitic Egg detection from Microscopic images using Convolutional Neural Networks

The most common test for parasitic infection diagnosis is stool parasitological testing. The use of the Kato-Katz method in the preparation of slides for the development of the image bank discussed here was extremely important. Other authors' studies on the same topic were discussed. Various parasite eggs of various species were created. Then the binary and multiclass classifier architectures were empirically defined, and each model was implemented. The performance of the classifiers was evaluated using metrics recommended in the literature for both empirically defined and transfer learning architectures. Finally, experiments were conducted to improve the system's performance by allowing binary and multiclass models to communicate. This data was used to build a database of 66 parasite egg photos from various species. Using data augmentation techniques, a total of 48,000 photos were collected. The examined measures all reached 99.9%. Despite some species' eggs sharing morphological similarities, the second method correctly classified each egg with a 99.9% hit rate. The problems addressed received a 99.9% rating on the evaluation measures used to assess them. The method can also be applied to a larger number of helminth species and detection technologies using the same procedures.&#x0D;
Hate Speech Detection in Social Networks using Machine Learning and Deep Learning Methods

Hate speech on social media platforms like Twitter is a growing concern that poses challenges to maintaining a healthy online environment and fostering constructive communication.Effective detection and monitoring of hate speech are crucial for mitigating its adverse impact on individuals and communities.In this paper, we propose a comprehensive approach for hate speech detection on Twitter using both traditional machine learning and deep learning techniques.Our research encompasses a thorough comparison of these techniques to determine their effectiveness in identifying hate speech on Twitter.We construct a robust dataset, gathered from diverse sources and annotated by experts, to ensure the reliability of our models.The dataset consists of tweets labeled as hate speech, offensive language, or neutral, providing a more nuanced representation of online discourse.We evaluate the performance of LSTM, BiLSTM, and CNN models against traditional shallow learning methods to establish a baseline for comparison.Our findings reveal that deep learning techniques outperform shallow learning methods, with BiLSTM emerging as the most accurate model for hate speech detection.The BiLSTM model demonstrates improved sensitivity to context, semantic nuances, and sequential patterns in tweets, making it adept at capturing the intricate nature of hate speech.Furthermore, we explore the integration of word embeddings, such as Word2Vec and GloVe, to enhance the performance of our models.The incorporation of these embeddings significantly improves the models' ability to discern between hate speech and other forms of online communication.This paper presents a comprehensive analysis of various machine learning methods for hate speech detection on Twitter, ultimately demonstrating the superiority of deep learning techniques, particularly BiLSTM, in addressing this critical issue.Our findings pave the way for further research into advanced methods of tackling hate speech and facilitating healthier online interactions.
“I Wish You Had Died on Utøya”: Terrorist Attack Survivors’ Personal Experiences with Hate Speech and Threats

We aimed to explore how many of the politically active, highly media-exposed, young survivors of the terrorist attack on Utøya island, Norway, have had personal experiences with hate speech and threats post-terror. Further, we aimed to examine the nature of these experiences, and the impact and consequences they have had for the directly affected. Approximately 8.5 years after the politically motivated terrorist attack on Utøya island in 2011, 289 survivors (51.2 percent females, M age = 27.7, SD = 4.6) were interviewed about their personal experiences with hate speech and threats. Responses were systematically analyzed using thematic analysis. In total, ninety-nine participants (34 percent) had experienced hate speech and/or threats post-terror, of whom a majority believed that this was related to their status as a survivor of the attack. Thematically, the hate/threats mainly revolved around wishes that the survivors had died in the attack, death threats, support for the terrorist, severe accusations related to the attack, and conspiracy theories. This had affected the survivors both emotionally (e.g., inducing fear and anger, but also robustness) and behaviorally (e.g., social and political withdrawal). Immediate measures and long-term strategies need to be developed to mitigate the personal harm and threat to democracy that experiences with hate speech/threats may cause.
Language Report Catalan

Abstract Despite its vulnerable position as a minoritised language, the presence of Catalan in the digital sphere is relatively strong, thanks to an active online community with a high technological profile. Technological support for Catalan is slowly growing, following the recent advances in AI and increased awareness of the value of language data and technologies among public and private bodies. However, more effort is needed to promote the creation of open-source solutions and resources so as to lower the investment barrier for companies to build technology for Catalan.
Hybrid Deep Convolutional Neural Network Approach for Detecting Breast Cancer in Mammography Images

Breast cancer is among the top causes of fatalities related to cancer in females.Radiologists commonly use mammogram images to detect breast tumors in their early stages.However, mammography can produce low-contrast images, making it difficult and time-consuming to segment abnormal regions.Deep convolutional neural networks (CNNs) are commonly used for image eval.This study used deep CNN models to develop a computer-aided diagnostic (CAD) system for feature extraction and classification.The proposed approach consists of three phases.In the first phase, a shallow, deep CNN model comprising five convolutional layers, five max-pooling layers, one batch normalization layer, and one dropout layer was developed and used to extract recombined images and novel features.In the second phase, the Inception-v3 model was used for label smoothing and classification due to its multiple filters with different sizes.In the third phase, features were extracted using shallow, deep CNN and Inception-v3 models.The Infallible Euclidean distance-based nonlinear dimensionality reduction approach was used to minimize dimensionality.Finally, the Gini-index-based C4.5 decision tree was used for the binary classification of mammogram images from the Digital Database for Screening Mammography (DDSM) + Curated Breast Imaging Subset of DDSM (CBIS-DDSM) and Mammographic Image Analysis Society (MIAS) datasets.The proposed hybrid shallow, deep CNN and Inception-v3 model achieved 99.52% accuracy, a 96% AUC on the DDSM + CBIS-DDSM dataset, and an accuracy of 97.53% and an AUC value of 97% on the MIAS dataset.Compared with other cutting-edge CAD systems, the proposed hybrid approach achieved higher accuracy by combining in-depth features across both datasets.
Language Report Basque

Abstract Since 1968 Basque has been immersed in a process of revitalisation that has faced formidable obstacles. Nonetheless, significant progress has been made in numerous areas. The Language Technology community widely accepts the standardised language and constructs efficacious LT tools. After thirty years of collaborative work, research has resulted in state-of-the-art technology and robust, broad-coverage NLP for Basque. However, a dramatic difference remains between Basque and other European languages in terms of both the maturity of research and the state of readiness with respect to language technology solutions.
Study of the Applicability Level of Merge Cube Augmented Reality Media on Junior Hight School Science Teachers

Science learning media based on Merge Cube Augmented Reality (AR) has been developed. This research aims to determine the level of applicability of science learning media based on Merge Cube AR to users, especially junior high school science teachers. The research method was carried out through a descriptive survey after testing the use of science learning media based on Merge Cube Augmented Reality. The respondents of this research were 41 teachers from 13 different junior hight schools. Based on the analysis data showed that respondents were very confident of being able to apply Merge Cube AR media in science learning and the percentage of applicability level in this description reach 87.8%, those indicate that the media is in very good criteria. Teachers are very confident that they can achieve learning goals by implementing Merge Cube AR learning media. The percentage of applicability level in this description reach 82.9%, those indicate that the media is in very good criteria. The results of the analysis also show that teachers are strongly believe that the Merge Cube AR media helps them in a more effective way to deliver science learning activity. The percentage of applicability level in this description reach 90.3%, those also indicate that the media is in very good criteria. The average percentage of applicability level reach 87.0 %, it is indicated that the applicability level of science learning media based on merge cube ar in the very good criteria. It can be concluded that science learning media based on Merge Cube AR has a very good level of applicability and can be used in science learning.
Graph Transformer for Recommendation

This paper presents a novel approach to representation learning in recommender systems by integrating generative self-supervised learning with graph transformer architecture. We highlight the importance of high-quality data augmentation with relevant self-supervised pretext tasks for improving performance. Towards this end, we propose a new approach that automates the self-supervision augmentation process through a rationale-aware generative SSL that distills informative user-item interaction patterns. The proposed recommender with Graph Transformer (GFormer) that offers parameterized collaborative rationale discovery for selective augmentation while preserving global-aware user-item relationships. In GFormer, we allow the rationale-aware SSL to inspire graph collaborative filtering with task-adaptive invariant rationalization in graph transformer. The experimental results reveal that our GFormer has the capability to consistently improve the performance over baselines on different datasets. Several in-depth experiments further investigate the invariant rationale-aware augmentation from various aspects. The source code for this work is publicly available at: https://github.com/HKUDS/GFormer.
Amplification of cascaded down-conversion by reusing photons with a switchable cavity

The ability to efficiently produce and manipulate nonclassical states of light is a critical requirement for the development of quantum optical technologies. In recent years, experiments have demonstrated that cascaded spontaneous parametric down-conversion is a promising approach to implement photon precertification, providing a way to overcome photon transmission losses for quantum communication, as well as to directly produce entangled three-photon states and heralded Bell pairs. However, the low efficiency of this process has so far limited its applicability beyond basic experiments. Here, we propose a scheme to amplify triplet production rates by using a fast switch and a delay loop to reuse photons that fail to convert on the first pass through the cascade's second nonlinear crystal. We construct a theoretical model to predict amplification rates and verify them in an experimental implementation. Our proof-of-concept device increases the rate of detected photon triplets as predicted, demonstrating that the method has the potential to dramatically improve the usefulness of cascaded down-conversion for device-independent quantum communication and entangled state generation.
Word square english learning media design assisted by the Canva application

Students in learning English "difficult, vocabulary, boring, do not understand". This is due to the lack of English vocabulary that students have regarding pronunciation and pronunciation. Lack of interesting and not boring learning media to help students expand their vocabulary when learning English. The purpose of this research is to make a Word Square media product supported by the Canva application in learning English that is valid as an experiment to expand the vocabulary of seventh grade junior high school students. The method used is 4D model development. This research is only limited to development, namely the third stage only. The results of the verification score were 4.47 from both media and 4.81 from both material expert verifiers in the very valid category. From the evaluation results, it was found that the Word Square media supported by the Canva application is suitable for learning English.
On Quantum Steering and Wigner Negativity

Quantum correlations and Wigner negativity are two important signatures of nonclassicality in continuous-variable quantum systems. In this work, we investigate how both are intertwined in the context of the conditional generation of Wigner negativity. It was previously shown that when Alice and Bob share a Gaussian state, Bob can perform some measurement on his system to create Wigner negativity on Alice's side if and only if there is Gaussian steering from Alice to Bob. In this work, we attempt to generalise these findings to a much broader class of scenarios on which Alice and Bob share a non-Gaussian state. We show that if Alice can initially steer Bob's system with Wigner-positive measurements, Bob can remotely create Wigner negativity in Alice's subsystem. Even though this shows that quantum steering is sufficient, we also show that quantum correlations are in general not necessary for the conditional generation of Wigner negativity.
Deep Learning- and Expert Knowledge-Based Feature Extraction and Performance Evaluation in Breast Histopathology Images

Cancer develops when a single or a group of cells grows and spreads uncontrollably. Histopathology images are used in cancer diagnosis since they show tissue and cell structures under a microscope. Knowledge-based and deep learning-based computer-aided detection is an ongoing research field in cancer diagnosis using histopathology images. Feature extraction is vital in both approaches since the feature set is fed to a classifier and determines the performance. This paper evaluates three feature extraction methods and their performance in breast cancer diagnosis. Features are extracted by (1) a Convolutional Neural Network, (2) a transfer learning architecture VGG16, and (3) a knowledge-based system. The feature sets are tested by seven classifiers, including Neural Network (64 units), Random Forest, Multilayer Perceptron, Decision Tree, Support Vector Machines, K-Nearest Neighbors, and Narrow Neural Network (10 units) on the BreakHis 400× image dataset. The CNN achieved up to 85% for the Neural Network and Random Forest, the VGG16 method achieved up to 86% for the Neural Network, and the knowledge-based features achieved up to 98% for Neural Network, Random Forest, Multilayer Perceptron classifiers.
How missing value imputation is confounded with batch effects and what you can do about it

In data-processing pipelines, upstream steps can influence downstream processes because of their sequential nature. Among these data-processing steps, batch effect (BE) correction (BEC) and missing value imputation (MVI) are crucial for ensuring data suitability for advanced modeling and reducing the likelihood of false discoveries. Although BEC–MVI interactions are not well studied, they are ultimately interdependent. Batch sensitization can improve the quality of MVI. Conversely, accounting for missingness also improves proper BE estimation in BEC. Here, we discuss how BEC and MVI are interconnected and interdependent. We show how batch sensitization can improve any MVI and bring attention to the idea of BE-associated missing values (BEAMs). Finally, we discuss how batch-class imbalance problems can be mitigated by borrowing ideas from machine learning.
High-Round QAOA for MAX $k$-SAT on Trapped Ion NISQ Devices

The Quantum Alternating Operator Ansatz (QAOA) is a hybrid classical-quantum algorithm that aims to sample the optimal solution(s) of discrete combinatorial optimization problems. We present optimized QAOA circuit constructions for sampling MAX $k$-SAT problems, specifically for $k=3$ and $k=4$. The novel $4$-SAT QAOA circuit construction we present uses measurement based uncomputation, followed by classical feed forward conditional operations. The QAOA circuit parameters for $3$-SAT are optimized via exact classical (noise-free) simulation, using HPC resources to simulate up to $20$ rounds on $10$ qubits. In order to explore the limits of current NISQ devices we execute these optimized QAOA circuits for random $3$-SAT test instances with clause-to-variable ratio $4$ on four trapped ion quantum computers: Quantinuum H1-1 (20 qubits), IonQ Harmony (11 qubits), IonQ Aria 1 (25 qubits), and IonQ Forte (30 qubits). The QAOA circuits that are executed include $n=10$ up to $p=20$, and $n=22$ for $p=1$ and $p=2$. The high round circuits use upwards of 9,000 individual gate instructions, making these some of the largest QAOA circuits executed on NISQ devices. Our main finding is that current NISQ devices perform best at low round counts (i.e., $p = 1,\ldots, 5$) and then -- as expected due to noise -- gradually start returning satisfiability truth assignments that are no better than randomly picked solutions as the number of QAOA rounds are further increased.
Modeling Singlet Fission on a Quantum Computer

We demonstrate a practical application of quantum computing by using it to investigate the linear H4 molecule as a simple model for singlet fission. We use the Peeters-Devreese-Soldatov energy functional to calculate the necessary energetics based on the moments of the Hamiltonian estimated on the quantum computer. To reduce the number of required measurements, we use several independent strategies: 1) reduction of the size of the relevant Hilbert space by tapering off qubits; 2) measurement optimization via rotations to eigenbases shared by groups of qubit-wise commuting Pauli strings; and 3) parallel execution of multiple state preparation and measurement operations using all 20 qubits available on the Quantinuum H1-1 quantum hardware. Our results meet the energetic requirements for singlet fission, are in excellent agreement with exact transition energies (for the chosen one-particle basis), and outperform classical methods considered computationally feasible for singlet fission candidates.
Immersive Horizons: Exploring the Transformative Power of Virtual Reality Across Economic Sectors

The scholarly discourse surrounding the manifold advantages, applications, and limitations of implementing Virtual Reality (VR) in the contemporary milieu has burgeoned over time. VR holds immense potential, attracting fervent interest from governmental and private entities alike. Nevertheless, the existing body of literature pertaining to the expanding utilization of VR in diverse economic sectors remains scant. Therefore, the primary objective of this study is to furnish a comprehensive literature review encompassing VR applications across various economic domains while elucidating concerns surrounding its integration within engineering education. A total of 108 publications were extracted from prominent databases such as Scopus, Elsevier, Science Direct, and Google Scholar, with a subsequent review of 51 relevant works. These scrutinized journals were published between 2015 and 2022 and were predominantly authored in English. The reviewed publications encompassed VR applications in education, robotics, healthcare, transportation, sports, agriculture, governance, security, and media. The study’s findings unveiled significant advancements in VR implementation within engineering education, medical training, cognitive augmentation, aircraft assembly, governance, and diverse other spheres. Notwithstanding these achievements, impediments to VR deployment were identified, stemming from financial exigencies, cultural and conventional norms, with scant evidence of VR’s prevalence in underdeveloped nations, given that all the assessed research originated from developed economies. Additionally, the limitations of this review encompassed a small sample size and a narrowly focused demographic in the examined articles. Nevertheless, despite these constraints, the research highlights substantial progress in VR utilization over the preceding decade.
MyVote - Blockchain Based Online Voting System

The application of blockchain technology has drawn a lot of interest as a potential solution for secure and open online voting systems. In order to guarantee the validity and reliability of the voting process, this research paper focuses on analyzing the design and implementation of an online voting system built on blockchain technology. The architecture of the system is covered in the paper, including how smart contracts are used to control the voting process and how a face detection system and cryptographic techniques are integrated to guarantee the confidentiality and privacy of voter data. Furthermore, the contribution evaluates the usability and perceived security of the system through the presentation of the results of a user study. The findings suggest that a blockchain-based online voting system is a promising approach to enable secure and transparent voting. In addition, the study provides insight into the challenges and opportunities associated with using blockchain technology for online voting systems.