Reviewerly/exHarmony huggingface dataset:
Effective Rating Prediction Using an Attention-Based User Review Sentiment Model

We propose a new sentiment information-based attention mechanism that helps to identify user reviews that are more likely to enhance the accuracy of a rating prediction model. We hypothesis that highly polarised reviews (strongly positive or negative) are better indicators of the users' preferences and that this sentiment polarity information helps to identify the usefulness of reviews. Hence, we introduce a novel neural network rating prediction model, called SentiAttn, which includes both the proposed sentiment attention mechanism as well as a global attention mechanism that captures the importance of different parts of the reviews. We show how the concatenation of the positive and negative users' and items' reviews as input to SentiAttn, results in different architectures with various channels. We investigate if we can improve the performance of SentiAttn by fine-tuning different channel setups. We examine the performance of SentiAttn on two well-known datasets from Yelp and Amazon. Our results show that SentiAttn significantly outperforms a classical approach and four state-of-the-art rating prediction models. Moreover, we show the advantages of using the sentiment attention mechanism in the rating prediction task and its effectiveness in addressing the cold-start problem.
Conditions for designing effective professional educational programs at a modern university

The author examines the aspects of the formation of professional and key personal competencies and their features in the modern digital society and university education, reflected in the design of professional educational programs. The author actualizes the need to update educational professional programs, examines the essence and features of the formation of professional competencies in universities of modern Russia at the digital stage of the development of civilization, features of the individualization of the educational process in the era of digitalization and the spread of distance technologies. The article prioritizes the impact of personnel, logistical and substantive problems. The subject of this article is the conditions for the development of effective professional educational programs in university education. The object is the design of professional educational programs. В В In the situation of the current stage of the digital development of society and the stage of the development of vocational education, the problem of the formation of professional competencies in university education is particularly relevant.The methodological basis of the article is a competence-based approach that considers the results of students' educational actions through competencies. The main problem of the article is the consideration of the conditions for designing effective professional educational programs and technologies for their practical implementation at the university for the formation of a complex of professional and personal competencies of students in the conditions of digitalization of university education and the spread of distance education. The need to update educational professional programs is determined by the peculiarities of modern vocational education, the level of competence and preparedness of subjects in it, taking into account the personnel, material, technical and content-technological conditions of the modern personality-oriented professional educational route of each student, which allows to coordinate and harmonize the disparate personal and axiological determinants of the digital stage of the development of university education in society.
ePose: Let's Make EfficientPose More Generally Applicable

EfficientPose is an impressive 3D object detection model. It has been demonstrated to be quick, scalable, and accurate, especially when considering that it uses only RGB inputs. In this paper we try to improve on EfficientPose by giving it the ability to infer an object's size, and by simplifying both the data collection and loss calculations. We evaluated ePose using the Linemod dataset and a new subset of it called "Occlusion 1-class". We also outline our current progress and thoughts about using ePose with the NuScenes and the 2017 KITTI 3D Object Detection datasets. The source code is available at https://github.com/tbd-clip/EfficientPose.
Towards Full-to-Empty Room Generation with Structure-Aware Feature  Encoding and Soft Semantic Region-Adaptive Normalization

The task of transforming a furnished room image into a background-only is extremely challenging since it requires making large changes regarding the scene context while still preserving the overall layout and style. In order to acquire photo-realistic and structural consistent background, existing deep learning methods either employ image inpainting approaches or incorporate the learning of the scene layout as an individual task and leverage it later in a not fully differentiable semantic region-adaptive normalization module. To tackle these drawbacks, we treat scene layout generation as a feature linear transformation problem and propose a simple yet effective adjusted fully differentiable soft semantic region-adaptive normalization module (softSEAN) block. We showcase the applicability in diminished reality and depth estimation tasks, where our approach besides the advantages of mitigating training complexity and non-differentiability issues, surpasses the compared methods both quantitatively and qualitatively. Our softSEAN block can be used as a drop-in module for existing discriminative and generative models. Implementation is available on vcl3d.github.io/PanoDR/.
Efektivitas Penggunaan Model Pembelajaran TPS Berbantuan Media Komik Terhadap Hasil Belajar Ekonomi Siswa Kelas XI Di SMA Bina Putra Tangerang

This study examined the effectiveness of the use of the TPS learning model with the help of comic media on the economics subject learning outcomes of XI grade students at SMA Bina Putra. The technique used in this exploration was Quasi Experiment using Non Equivalent Control Group Design. The population in this study were students of class XI SMA Bina Putra first semester in the Academic Year 2021/2022 with a total sample of 45 students as respondents. The research method used in this research was a quasi-experimental method. Experimental quasi-experimental research was a process of scientific investigation to find the effect of a certain treatment by comparing the conditions before and after being given the treatment or by comparing the samples that were given the treatment and those who were not given the treatment (the control sample). The results showed that the cognitive learning outcomes after using the Think Pair Share learning model assisted by the Media Comics Experimental class were normally distributed with the content value of the experimental class learning outcomes 0.80 &gt; 0.05, meaning that Hᴏ was accepted with an average value of 76.13; (2) classical mastery as a result of the student's economics study in class XI Experiment at SMA Bina Putra was 75%; and (3) the average result of the Experiment and Control class was higher than the Experiment class, where the experimental class was 76.13 while the Control class was 71.36.
Towards Rich, Portable, and Large-Scale Pedestrian Data Collection

Recently, pedestrian behavior research has shifted towards machine learning based methods and converged on the topic of modeling pedestrian interactions. For this, a large-scale dataset that contains rich information is needed. We propose a data collection system that is portable, which facilitates accessible large-scale data collection in diverse environments. We also couple the system with a semi-autonomous labeling pipeline for fast trajectory label production. We further introduce the first batch of dataset from the ongoing data collection effort -- the TBD pedestrian dataset. Compared with existing pedestrian datasets, our dataset contains three components: human verified labels grounded in the metric space, a combination of top-down and perspective views, and naturalistic human behavior in the presence of a socially appropriate "robot".
Semi-supervised Learning using Robust Loss

The amount of manually labeled data is limited in medical applications, so semi-supervised learning and automatic labeling strategies can be an asset for training deep neural networks. However, the quality of the automatically generated labels can be uneven and inferior to manual labels. In this paper, we suggest a semi-supervised training strategy for leveraging both manually labeled data and extra unlabeled data. In contrast to the existing approaches, we apply robust loss for the automated labeled data to automatically compensate for the uneven data quality using a teacher-student framework. First, we generate pseudo-labels for unlabeled data using a teacher model pre-trained on labeled data. These pseudo-labels are noisy, and using them along with labeled data for training a deep neural network can severely degrade learned feature representations and the generalization of the network. Here we mitigate the effect of these pseudo-labels by using robust loss functions. Specifically, we use three robust loss functions, namely beta cross-entropy, symmetric cross-entropy, and generalized cross-entropy. We show that our proposed strategy improves the model performance by compensating for the uneven quality of labels in image classification as well as segmentation applications.
The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.5M Screening and Diagnostic Mammograms

Developing and validating artificial intelligence models in medical imaging requires datasets that are large, granular, and diverse. To date, the majority of publicly available breast imaging datasets lack in one or more of these areas. Models trained on these data may therefore underperform on patient populations or pathologies that have not previously been encountered. The EMory BrEast imaging Dataset (EMBED) addresses these gaps by providing 3650,000 2D and DBT screening and diagnostic mammograms for 116,000 women divided equally between White and African American patients. The dataset also contains 40,000 annotated lesions linked to structured imaging descriptors and 61 ground truth pathologic outcomes grouped into six severity classes. Our goal is to share this dataset with research partners to aid in development and validation of breast AI models that will serve all patients fairly and help decrease bias in medical AI.
A quantum advantage for Density Functional Theory ?

The technological revolution brought about by quantum computers promises to solve problems with high economical and societal impact that remain intractable on classical computers. While several quantum algorithms have been devoted to solve the many-body problem in quantum chemistry, the focus is on wavefunction theory that is limited to relatively small systems, even for quantum computers, i.e., the size of tractable systems being roughly limited by the number of qubits available. Computations on large systems rely mainly on mean-field-type approaches such as density functional theory, for which no quantum advantage has been envisioned so far. In this work, we question this a priori by investigating the benefit of quantum computers to scale up not only many-body wavefunction methods, but also mean-field-type methods, and consequently the all range of application of quantum chemistry.
Smoothing Advantage Learning

Advantage learning (AL) aims to improve the robustness of value-based reinforcement learning against estimation errors with action-gap-based regularization. Unfortunately, the method tends to be unstable in the case of function approximation. In this paper, we propose a simple variant of AL, named smoothing advantage learning (SAL), to alleviate this problem. The key to our method is to replace the original Bellman Optimal operator in AL with a smooth one so as to obtain more reliable estimation of the temporal difference target. We give a detailed account of the resulting action gap and the performance bound for approximate SAL. Further theoretical analysis reveals that the proposed value smoothing technique not only helps to stabilize the training procedure of AL by controlling the trade-off between convergence rate and the upper bound of the approximation errors, but is beneficial to increase the action gap between the optimal and sub-optimal action value as well.
DEEP: DEnoising Entity Pre-training for Neural Machine Translation

It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus. Earlier named entity translation methods mainly focus on phonetic transliteration, which ignores the sentence context for translation and is limited in domain and language coverage. To address this limitation, we propose DEEP, a DEnoising Entity Pre-training method that leverages large amounts of monolingual data and a knowledge base to improve named entity translation accuracy within sentences. Besides, we investigate a multi-task learning strategy that finetunes a pre-trained neural machine translation model on both entity-augmented monolingual data and parallel data to further improve entity translation. Experimental results on three language pairs demonstrate that \method results in significant improvements over strong denoising auto-encoding baselines, with a gain of up to 1.3 BLEU and up to 9.2 entity accuracy points for English-Russian translation.
Decentralized Privacy Protection of IoTs Using Blockchain-Enabled Federated Learning

Internet of Things (IoT), as the extension of cloud computing and a foundation of edge computing, is fast booming due to its abilities to easy the bothersome issues of networks like high latency, congestion, etc. However, the privacy issues attract increasing concerns which result in the “data isolated islands” while dragging down the performances of IoT.
Copy, Right? A Testing Framework for Copyright Protection of Deep  Learning Models

Deep learning (DL) models, especially those large-scale and high-performance ones, can be very costly to train, demanding a great amount of data and computational resources. Unauthorized reproduction of DL models can lead to copyright infringement and cause huge economic losses to model owners. Existing copyright protection techniques are mostly based on watermarking, which embeds an owner-specified watermark into the model. While being able to provide exact ownership verification, these techniques are 1) invasive, as they need to tamper with the training process, which may affect the utility or introduce new security risks; 2) prone to adaptive attacks that attempt to remove the watermark; and 3) not robust to the emerging model extraction attacks. Latest fingerprinting work, though being non-invasive, also falls short when facing the diverse and ever-growing attack scenarios. In this paper, we propose a novel testing framework for DL copyright protection: DEEPJUDGE. DEEPJUDGE quantitatively tests the similarities between two DL models: a victim model and a suspect model. It leverages a diverse set of testing metrics and test case generation methods to produce a chain of supporting evidence to help determine whether a suspect model is a copy of the victim model. Advantages of DEEPJUDGE include: 1) non-invasive, as it works directly on the model and does not tamper with the training process; 2) efficient, as it only needs a small set of test cases and a quick scan of models; 3) flexible, as it can easily incorporate new metrics or generation methods to obtain more confident judgement; and 4) fairly robust to model extraction and adaptive attacks. We verify the effectiveness of DEEPJUDGE under typical copyright infringement scenarios, including model finetuning, pruning and extraction, via extensive experiments on both image and speech datasets with a variety of model architectures.
iGraphMatch: an R Package for the Analysis of Graph Matching

iGraphMatch is an R package for finding corresponding vertices between two graphs, also known as graph matching. The package implements three categories of prevalent graph matching algorithms including relaxation-based, percolation-based, and spectral-based, which are applicable to matching graphs under general settings: weighted directed graphs of different order and graphs of multiple layers. We provide versatile options to incorporate prior information in the form of seeds with or without noise and similarity scores. In addition, iGraphMatch provides functions to summarize the graph matching results in terms of several evaluation measures and visualize the matching performance. Finally, the package enables users to sample correlated random graph pairs from classic random graph models to generate data for simulations. This paper illustrates the practical applications of the package to the analysis of graph matching by detailed examples using real data from communication, neuron, and transportation networks.
Unsupervised Matching of Data and Text

Entity resolution is a widely studied problem with several proposals to match records across relations. Matching textual content is a widespread task in many applications, such as question answering and search. While recent methods achieve promising results for these two tasks, there is no clear solution for the more general problem of matching textual content and structured data. We introduce a framework that supports this new task in an unsupervised setting for any pair of corpora, being relational tables or text documents. Our method builds a fine-grained graph over the content of the corpora and derives word embeddings to represent the objects to match in a low dimensional space. The learned representation enables effective and efficient matching at different granularity, from relational tuples to text sentences and paragraphs. Our flexible framework can exploit pre-trained resources, but it does not depends on their existence and achieves better quality performance in matching content when the vocabulary is domain specific. We also introduce optimizations in the graph creation process with an "expand and compress" approach that first identifies new valid relationships across elements, to improve matching, and then prunes nodes and edges, to reduce the graph size. Experiments on real use cases and public datasets show that our framework produces embeddings that outperform word embeddings and fine-tuned language models both in results' quality and in execution times.
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data

The detection of lesions in magnetic resonance imaging (MRI)-scans of human brains remains challenging, time-consuming and error-prone. Recently, unsupervised anomaly detection (UAD) methods have shown promising results for this task. These methods rely on training data sets that solely contain healthy samples. Compared to supervised approaches, this significantly reduces the need for an extensive amount of labeled training data. However, data labelling remains error-prone. We study how unhealthy samples within the training data affect anomaly detection performance for brain MRI-scans. For our evaluations, we consider three publicly available data sets and use autoencoders (AE) as a well-established baseline method for UAD. We systematically evaluate the effect of impured training data by injecting different quantities of unhealthy samples to our training set of healthy samples from T1-weighted MRI-scans. We evaluate a method to identify falsely labeled samples directly during training based on the reconstruction error of the AE. Our results show that training with impured data decreases the UAD performance notably even with few falsely labeled samples. By performing outlier removal directly during training based on the reconstruction-loss, we demonstrate that falsely labeled data can be detected and removed to mitigate the effect of falsely labeled data. Overall, we highlight the importance of clean data sets for UAD in brain MRI and demonstrate an approach for detecting falsely labeled data directly during training.
ViCE: Improving Dense Representation Learning by Superpixelization and Contrasting Cluster Assignment

Recent self-supervised models have demonstrated equal or better performance than supervised methods, opening for AI systems to learn visual representations from practically unlimited data. However, these methods are typically classification-based and thus ineffective for learning high-resolution feature maps that preserve precise spatial information. This work introduces superpixels to improve self-supervised learning of dense semantically rich visual concept embeddings. Decomposing images into a small set of visually coherent regions reduces the computational complexity by $\mathcal{O}(1000)$ while preserving detail. We experimentally show that contrasting over regions improves the effectiveness of contrastive learning methods, extends their applicability to high-resolution images, improves overclustering performance, superpixels are better than grids, and regional masking improves performance. The expressiveness of our dense embeddings is demonstrated by improving the SOTA unsupervised semantic segmentation benchmark on Cityscapes, and for convolutional models on COCO.
ADEVA: A Decentralized Electronic Voting Application Using Blockchain Technology

&lt;div&gt;Abstract—Despite the various advantages inherent in the use of blockchain technology, blockchain application platforms are characterized by bottlenecks, latency, energy inefficiency, time inefficiency, low transaction throughput, vulnerability to 51% attacks, and a lack of fairness in the sharing of mining proceeds. These challenges are currently affecting the performance of this technology, hindering its rapid development and adoption for real-time applications such as Electronic Voting Applications (EVAs). It is well known that the consensus mechanism responsible for the security and consistency features of a blockchain, is also responsible for most of the above challenges.&lt;/div&gt;&lt;div&gt;In this paper, a new consensus mechanism, Proof of Vote (PoV) is proposed, a lightweight consensus mechanism specifically adapted for EVA, as a replacement for the heavyweight Proof of Work (PoW) consensus algorithm. We use the Ethereum blockchain as a decentralized application platform, in which we develop a decentralized electronic voting application (ADEVA), replacing the Ethereum PoW consensus algorithm with the proposed PoV consensus algorithm. Thus we implemented ADEVA with PoV and compared it to the two major consensus mechanisms: Proof of work (PoW) and Proof of Stake (PoS). We found that PoV enhanced the performance of ADEVA over and above that achieved using PoW and PoS. The low resource utilization and high transaction throughput of ADEVA with PoV makes it a scalable solution for real-world EVAs.&lt;/div&gt;
Discretely Indexed Flows

In this paper we propose Discretely Indexed flows (DIF) as a new tool for solving variational estimation problems. Roughly speaking, DIF are built as an extension of Normalizing Flows (NF), in which the deterministic transport becomes stochastic, and more precisely discretely indexed. Due to the discrete nature of the underlying additional latent variable, DIF inherit the good computational behavior of NF: they benefit from both a tractable density as well as a straightforward sampling scheme, and can thus be used for the dual problems of Variational Inference (VI) and of Variational density estimation (VDE). On the other hand, DIF can also be understood as an extension of mixture density models, in which the constant mixture weights are replaced by flexible functions. As a consequence, DIF are better suited for capturing distributions with discontinuities, sharp edges and fine details, which is a main advantage of this construction. Finally we propose a methodology for constructiong DIF in practice, and see that DIF can be sequentially cascaded, and cascaded with NF.