Reviewerly/exHarmony huggingface dataset:
PAXQA: Generating Cross-lingual Question Answering Examples at Training Scale

Existing question answering (QA) systems owe much of their success to large, high-quality training data. Such annotation efforts are costly, and the difficulty compounds in the cross-lingual setting. Therefore, prior cross-lingual QA work has focused on releasing evaluation datasets, and then applying zero-shot methods as baselines. This work proposes a synthetic data generation method for cross-lingual QA which leverages indirect supervision from existing parallel corpora. Our method termed PAXQA (Projecting annotations for cross-lingual (x) QA) decomposes cross-lingual QA into two stages. First, we apply a question generation (QG) model to the English side. Second, we apply annotation projection to translate both the questions and answers. To better translate questions, we propose a novel use of lexically-constrained machine translation, in which constrained entities are extracted from the parallel bitexts. We apply PAXQA to generate cross-lingual QA examples in 4 languages (662K examples total), and perform human evaluation on a subset to create validation and test splits. We then show that models fine-tuned on these datasets outperform prior synthetic data generation models over several extractive QA datasets. The largest performance gains are for directions with non-English questions and English contexts. Ablation studies show that our dataset generation method is relatively robust to noise from automatic word alignments, showing the sufficient quality of our generations. To facilitate follow-up work, we release our code and datasets at https://github.com/manestay/paxqa .
Enriching Source Code with Contextual Data for Code Completion Models: An Empirical Study

Transformer-based pre-trained models have recently achieved great results in solving many software engineering tasks including automatic code completion which is a staple in a developer's toolkit. While many have striven to improve the code-understanding abilities of such models, the opposite -- making the code easier to understand -- has not been properly investigated. In this study, we aim to answer whether making code easier to understand through using contextual data improves the performance of pre-trained code language models for the task of code completion. We consider type annotations and comments as two common forms of additional contextual information that often help developers understand code better. For the experiments, we study code completion in two granularity levels; token and line completion and take three recent and large-scale language models for source code: UniXcoder, CodeGPT, and InCoder with five evaluation metrics. Finally, we perform the Wilcoxon Signed Rank test to gauge significance and measure the effect size. Contrary to our expectations, all models perform better if type annotations are removed (albeit the effect sizes are small). For comments, we find that the models perform better in the presence of multi-line comments (again with small effect sizes). Based on our observations, we recommend making proper design choices when training, fine-tuning, or simply selecting such models given the intended data and application. Better evaluations and multi-modal techniques can also be further investigated to improve the practicality and accuracy of auto-completions.
Construction of Permutation Polynomials of Certain Specific Cycle Structure over Finite Fields

For a finite field of odd number of elements we construct families of permutation binomials and permutation trinomials with one fixed-point (namely zero) and remaining elements being permuted as disjoint cycles of same length. Binomials and trinomials providing permutations with cycles of many lengths with certain frequency are also constructed.
Total-Recon: Deformable Scene Reconstruction for Embodied View Synthesis

We explore the task of embodied view synthesis from monocular videos of deformable scenes. Given a minute-long RGBD video of people interacting with their pets, we render the scene from novel camera trajectories derived from the in-scene motion of actors: (1) egocentric cameras that simulate the point of view of a target actor and (2) 3rd-person cameras that follow the actor. Building such a system requires reconstructing the root-body and articulated motion of every actor, as well as a scene representation that supports free-viewpoint synthesis. Longer videos are more likely to capture the scene from diverse viewpoints (which helps reconstruction) but are also more likely to contain larger motions (which complicates reconstruction). To address these challenges, we present Total-Recon, the first method to photorealistically reconstruct deformable scenes from long monocular RGBD videos. Crucially, to scale to long videos, our method hierarchically decomposes the scene into the background and objects, whose motion is decomposed into carefully initialized root-body motion and local articulations. To quantify such "in-the-wild" reconstruction and view synthesis, we collect ground-truth data from a specialized stereo RGBD capture rig for 11 challenging videos, significantly outperforming prior methods. Our code, model, and data can be found at https://andrewsonga.github.io/totalrecon .
Distilling from Similar Tasks for Transfer Learning on a Budget

We address the challenge of getting efficient yet accurate recognition systems with limited labels. While recognition models improve with model size and amount of data, many specialized applications of computer vision have severe resource constraints both during training and inference. Transfer learning is an effective solution for training with few labels, however often at the expense of a computationally costly fine-tuning of large base models. We propose to mitigate this unpleasant trade-off between compute and accuracy via semi-supervised cross-domain distillation from a set of diverse source models. Initially, we show how to use task similarity metrics to select a single suitable source model to distill from, and that a good selection process is imperative for good downstream performance of a target model. We dub this approach DistillNearest. Though effective, DistillNearest assumes a single source model matches the target task, which is not always the case. To alleviate this, we propose a weighted multi-source distillation method to distill multiple source models trained on different domains weighted by their relevance for the target task into a single efficient model (named DistillWeighted). Our methods need no access to source data, and merely need features and pseudo-labels of the source models. When the goal is accurate recognition under computational constraints, both DistillNearest and DistillWeighted approaches outperform both transfer learning from strong ImageNet initializations as well as state-of-the-art semi-supervised techniques such as FixMatch. Averaged over 8 diverse target tasks our multi-source method outperforms the baselines by 5.6%-points and 4.5%-points, respectively.
Rhythm, Language, Theatre and Film - A Synaesthetic Proposal Ritmo, Linguaggio, Teatro e Cinema - Una proposta sinestetica

This personal narrative describes the author's experience of struggling with stuttering during their teenage years.Despite the challenges and embarrassment caused by the condition, the author never imagined that twenty years later they would be using the same sounds and phonics they once struggled with to teach percussion instruments to students.In this context, the author has found that the same consonants and syllables that used to cause difficulty in speaking are now essential in teaching a unique type of music to children.The narrative highlights the transformative power of music and the unexpected ways in which personal challenges can be turned into strengths.Ugo Rodolico ubr77
An Unstructured Mammogram Analysis for Feasible Classification and Detection of Breast Cancer Using a Convolutional Approach

Currently, different methods are available for the purpose of breast cancer classification and detection. Most of these techniques are well appreciated by society and in response to the demand of society, almost every year the different techniques are introduced by different researchers, but it does not satisfy the demand of current requirements. Under such a situation, we are going to propose a new breast cancer classification and detection algorithm using a convolutional approach. This technique starts with the mammogram preprocessing step. It is followed by the convolutional model architecture design step. In the next step, the segregation of the dataset into the training and testing phase is performed. Then, the convolutional model architecture is trained using the training dataset and pre-masked images. After that our proposed algorithm predicts, the breast cancer detection and classification result. We have found that our proposed algorithm can be used for breast tumor detection and classification from mammogram images with the average approximate accuracy of 98.5% and the average approximate F1 score of 0.98. Novel preprocessing steps and modifications in the convolutional architecture make the proposed methodology unique. Due to high performance, novelty, ease of use, our proposed method is useful to develop any mobile or web application in the future.
Artificial Intelligence with Graph Neural Networks Applied to a Risk-like Board Game

While AI has been successfully applied to many board games, such as chess and Go, most research is confined to a single board and is inflexible to topological changes. Contrarily, this research develops an AI agent, referred to as GG-net, to play an online strategy game based on the classic board game Risk, which is played on a wide variety of irregularly shaped maps. Prior research has struggled to create an effective AI for Risk-like games due to the immense branching factor. The most successful attempts tended to rely on manually restricting the AI's set of actions and providing the AI with handcrafted features. GG-net uses no human knowledge, instead, it relies on a genetic algorithm combined with a graph neural network. Together, these methods allow GG-net to overcome the high branching factor and generalize across a multitude of maps. GG-net appears to be a strong opponent on both small and medium maps, however, on large maps with hundreds of territories, inefficiencies become more significant and GG-net struggles against the rule-based agents.
Thoreau’s Philosophy of Simplicity Applied to Love, Relationships, and Heartbreak

This paper explores the relevance of Henry David Thoreau’s philosophy of simplicity to individuals at any point in their life, not only fortunate individuals who can live alone. Although Thoreau seems to argue that the kind of enlightenment or transcendence he obtains is only available to the genuinely unencumbered and free, his principles can be applied in measured amounts to anyone’s circumstances. This paper looks at Thoreau’s writings in Walden, “Walking,” and “Civil Disobedience” and analyzes Thoreau’s recommendations for simplicity, love, human relationships, and the role of a personal mission in one’s life.
A systematic review of homomorphic encryption applications in Internet of Things

Internet of Things (IoT) is becoming one of the most growing technology in the current century. Cloud computing and IoT have become dependent on each other. Cloud services providers ensure the availability of customer data and protect it from external threats and attacks. However, the IoT providers must guarantee that the collected data should hold its secrecy and confidentiality from cloud service providers. Therefore, many IoT providers began setting various controls and regulations on how data should be processed and shared, leading to a higher adaptation of homomorphic encryption(HE). This paper provides an introduction to homomorphic encryption with its different types. It will go through some of the different adaptations of homomorphic encryption. It concludes with a comparison between the different ways of implementing homomorphic encryption within IoT and each method&apos;s strengths and weaknesses.
Breast cancer: deep transfer learning techniques for breast tumor detection in mammography

Among women, breast cancer become the second leading cause of death. Several techniques exist for breast cancer detection, yet mammogram imaging is currently the most often-used method. To avoid overfitting challenges with images, we were able to meet the goal of our study, which was to construct an accurate Convolutional Neural Network (CNN) model that classifies mammography images into normal and abnormal using deep transfer learning and data augmentation techniques. Xception, ResNet50V2, and VGG16 were used with, then without trainable layers on the Mammographic Image Analyses Society MiniMammographic Database (MiniMIAS), in order to determine which one of them is suitable for our case. But due to the shortage of images in the abnormal class and to avoid unbalanced data, we managed to balance the data using images from the Chinese Mammography Database (CMMD) which only contains abnormal images. The dataset was pre-processed using various filters to extract the Region Of Interest (ROI) and remove any noises, resulting in better images for the training process. The evaluation results show that using this dataset the pre-trained models Xception and VGG16 with trainable layers are more suitable than ResNet50V2 for our situation with an accuracy of 99.9% ,95.23%, and 94.04% respectively, which proves that the pre-processing filters employed are extremely effective in breast cancer detection. With this dataset, we were also able to demonstrate that activating the layers of a pre-trained model to train will produce more positive results than not doing so for breast tumor detection.
Drivers, the missed attack surface of connected vehicles

The purpose of this paper is to analyse the human factors in cyberattacks targeting connected vehicles. To achieve this purpose, we utilised qualitative research methods and analysed multiple attack cases against connected vehicles. These cases demonstrated a number of different attack vectors and vulnerabilities. Our paper demonstrated that the most widely used Risk Assessment standards are flawed in this case; each assumes that certain considerations are either managed by other standards or that they are common knowledge and do not necessitate standardisation. This problem results in unresolved voids in the overall standards structure. The main gap identified was around Human Factors; none of the regulations or standards considered the impact of the owner/user/driver on the connected elements of vehicle safety and cybersecurity.
Recent advances in artificial intelligence techniques for identifying fake accounts: a review

Online social networks are still growing; as a result, more people are opening accounts every day. However, not all of those accounts are for actual persons. The creation of fake accounts and bots is terrifying, and it has a negative impact on society. Because of this, numerous researchers have worked to develop efficient systems that identify false accounts on Facebook, Twitter, Instagram, etc. We will investigate this through analysis of recent research in this field.
Effective comparison of meta-heuristics algorithm in cloud computing

Cloud Computing is an innovative model that is changing the industry procedure of inventing, developing, deploying, order, updating, maintaining, and budget for applications and the stock on which they run. It is a technique to increase the resource facility or proficiencies without financing in a new hardware or licensing new software. Due to its attractive features, organizations and individuals are getting involved in cloud technology paradigms. However, there are still a few obstacles in total deployment of cloud technology commercially, which is a challenging task. The complexness of the scheduling job, the resource parameter should be augmented by decreasing the execution time. Provisioning the resource and mapping the workload is a crucial role in improving the performance of the complete system. Mapping/Executing the cloudlet should be customer satisfaction and economical to the provider. Cloud technology is facing different challenging issues for allocating the job in an efficient manner to requested user in the peak time. This thesis is composed of most important works like Provisioning and scheduling the required job, RFD metaheuristics algorithm, Performance metrics calculation for response time, execution time, and cost. The current research investigation is the job Scheduling problem of cloud scheduling paradigm and to propose a new approach for provisioning and allocating the resources using RFD algorithm-based technique. The aim of implementing the RFD algorithm in the cloud is to efficiently allocate the resources and better utilization of cloud resources. The proposed effective approach of resource provisioning and scheduling algorithm is to solve the provision of resource and to execute workload efficiently in the cloud paradigm. The theme of research contains the proposed River formation dynamics algorithm which gives a better optimal solution for allocating the resources in the cloud in Comparing with “Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO).” The implementation results show decreasing response time, cost, and execution time.
Classification of Benign and Malignant Breast Tumor Using Machine Learning and Feature Selection Algorithms

Breast cancer is among the most frequent kinds of cancer that may be detected early and treated with a high probability of complete recovery before the disease&apos;s progression. The only way to save and decrease breast cancer mortality is by early detection, identification, and efficient treatment. The proper categorization of breast tumors is critical in the practice of medical diagnosis. This study builds different breast tumor classification models based on machine learning algorithms. Support Vector Machine (SVM), k-Nearest Neighbours (KNN), and Random Forest (RF) classifiers are used to build a set of breast tumor classification models. Each classifier is evaluated individually before and after applying a set of feature selection methods to a public breast tumor dataset. Moreover, a set of hybrid machine learning models are created using stacking approach. Results show that machine learning algorithms with feature selection techniques can be effectively used to build breast tumor classification models. The highest f-measure value is 97.30%, which obtained by combining SVM classifier with CLAE as feature selection.
Indian sign language detection using tensor flow and deep learning

It is necessary for persons with hearing impairments to communicate using Sign Language, and each region has its method of sign language that allows them to do so. To allow two people to communicate, they must both be fluent in their language, and the same is true for those conversing in Indian Sign Language. Hearing Impaired people, except close family members, find it difficult to converse with others. As a result, for Sign Language users and those who aren&apos;t well acquainted with the language, having a visual translator capable of understanding letters and digits might be advantageous.
Technology triumphs in track and synthetic skin reveals robot boo-boos

New tech for the Olympics When some Olympic athletes benefit from developments in materials science, it can kick up a heap of controversy, as it leads some to question if this gives an advantage to athletes who have enough money or sponsors to afford the new technology. Think Australian sprinter Cathy Freeman in 2000 wearing her air-parting polyurethane-coated hooded bodysuit, US swimmer Michael Phelps and others in 2008 who wore go-faster polyurethane shark skin–patterned swimsuits and collected 98% of the indoor swimming medals, and all three male marathon medalists in 2016 who wore Nike Vaporfly 4% road-running shoes featuring a carbon-fiber plated sole that cut the metabolic energy they expended by 4%. This summer’s Tokyo Olympics promises to be no different. This Newscriptster’s hunch is that the latest tech leap will involve novel designs and materials for track shoes with spikes on the bottom. Until now spikes were designed to
Data Characteristics Stability Index Integrated with Random Forest for Credit Card Fraud Detection

As more people use card-based and online payment options, the market for digital payments is growing. Along with such a shift, more urgent problems of fraud and cybersecurity. All financial institutions and banks are concerned about improving their ability to detect fraud. Machine learning methods for detecting fraud are predicated on the notion that fraudulent transactions differ from genuine ones in a number of keyways. Machine learning algorithms discover trends in financial activities and decide whether a particular transaction is legitimate based on this presumption. In order to prevent fraudulent transactions with credit cards, it is critical to choose optimized features that are successful in detecting fraud while developing predictive models. This is where machine learning comes into play. This research paper proposes an optimized feature selection method based on Data Characteristic Stability Index (DCS) along with resampling technique for balancing the dataset with Synthetic Minority Oversampling Technique (SMOTE) which prevents the Model Bias towards the Majority class in dataset. Finally, the Random Forest algorithm is chosen to train the predictive model with selected optimized features through the DCS index. The proposed framework provides the Model Stability and delay the Model Degradation and helps in capturing the Fraud effectively. Therefore, the data characteristics stability-based feature selected model will perform better with a model that is built by using traditional feature selection methods.
Empc: Efficient Multi-View Parallel Co-Learning for Semi-Supervised Action Recognition

Semi-supervised learning (SSL) is an effective approach to address the challenge of limited labeled data in action recognition. Existing methods have explored temporal augmentation and consistent learning, which have received widespread attention. However, these methods come with an exponential increase in computational effort, and neglect the potential for divergence and collaboration between modalities. Additionally, the models may exhibit randomness during pseudo-label evaluation and inconsistency between training and inference. To address these challenges, we propose an efficient multi-view parallel co-learning (EMPC) method for semi-supervised action recognition. First, we explore the temporal gradient (TG) and create a new view that contains rich motion history information, called the historical temporal gradient (HTG). Second, inspired by the working mechanism of Dropout, we assemble a low-computational multi-functional committee (MFC) and perform pseudo-label editing based on two evaluation criteria: confidence and consistency. We further design a new regularization strategy based on MFC, called mean regularized dropout (MR-Drop), which measures and reduces the output distribution’s uncertainty between sub-models to improve the model’s performance. Finally, based on the complementary information between RGB and HTG views, we build an efficient parallel network with multi-view feature sharing and pseudo-label collaboration. We evaluate EMPC on three public datasets: UCF-101, HMDB-51, and Kinetics-100. The experimental results demonstrate that EMPC achieves better classification performance with a limited amount of labeled data and a large amount of unlabeled data.