Reviewerly/exHarmony huggingface dataset:
CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation

Acquiring the most representative examples via active learning (AL) can benefit many data-dependent computer vision tasks by minimizing efforts of image-level or pixel-wise annotations. In this paper, we propose a novel Collaborative Panoptic-Regional Active Learning framework (CPRAL) to address the semantic segmentation task. For a small batch of images initially sampled with pixel-wise annotations, we employ panoptic information to initially select unlabeled samples. Considering the class imbalance in the segmentation dataset, we import a Regional Gaussian Attention module (RGA) to achieve semantics-biased selection. The subset is highlighted by vote entropy and then attended by Gaussian kernels to maximize the biased regions. We also propose a Contextual Labels Extension (CLE) to boost regional annotations with contextual attention guidance. With the collaboration of semantics-agnostic panoptic matching and region-biased selection and extension, our CPRAL can strike a balance between labeling efforts and performance and compromise the semantics distribution. We perform extensive experiments on Cityscapes and BDD10K datasets and show that CPRAL outperforms the cutting-edge methods with impressive results and less labeling proportion.
Boosting Contrastive Learning with Relation Knowledge Distillation

While self-supervised representation learning (SSL) has proved to be effective in the large model, there is still a huge gap between the SSL and supervised method in the lightweight model when following the same solution. We delve into this problem and find that the lightweight model is prone to collapse in semantic space when simply performing instance-wise contrast. To address this issue, we propose a relation-wise contrastive paradigm with Relation Knowledge Distillation (ReKD). We introduce a heterogeneous teacher to explicitly mine the semantic information and transferring a novel relation knowledge to the student (lightweight model). The theoretical analysis supports our main concern about instance-wise contrast and verify the effectiveness of our relation-wise contrastive learning. Extensive experimental results also demonstrate that our method achieves significant improvements on multiple lightweight models. Particularly, the linear evaluation on AlexNet obviously improves the current state-of-art from 44.7% to 50.1% , which is the first work to get close to the supervised (50.5%). Code will be made available.
ValueNet: A New Dataset for Human Value Driven Dialogue System

Building a socially intelligent agent involves many challenges, one of which is to teach the agent to speak guided by its value like a human. However, value-driven chatbots are still understudied in the area of dialogue systems. Most existing datasets focus on commonsense reasoning or social norm modeling. In this work, we present a new large-scale human value dataset called ValueNet, which contains human attitudes on 21,374 text scenarios. The dataset is organized in ten dimensions that conform to the basic human value theory in intercultural research. We further develop a Transformer-based value regression model on ValueNet to learn the utility distribution. Comprehensive empirical results show that the learned value model could benefit a wide range of dialogue tasks. For example, by teaching a generative agent with reinforcement learning and the rewards from the value model, our method attains state-of-the-art performance on the personalized dialog generation dataset: Persona-Chat. With values as additional features, existing emotion recognition models enable capturing rich human emotions in the context, which further improves the empathetic response generation performance in the EmpatheticDialogues dataset. To the best of our knowledge, ValueNet is the first large-scale text dataset for human value modeling, and we are the first one trying to incorporate a value model into emotionally intelligent dialogue systems. The dataset is available at https://liang-qiu.github.io/ValueNet/.
Evaluating Explanations of Relational Graph Convolutional Network Link Predictions on Knowledge Graphs

Recently, explanation methods have been proposed to evaluate the predictions of Graph Neural Networks on the task of link prediction. Evaluating explanation quality is difficult without ground truth explanations. This thesis is focused on providing a method, including datasets and scoring metrics, to quantitatively evaluate explanation methods on link prediction on Knowledge Graphs.
PCBMS: A Model to Select an Optimal Lossless Image Compression Technique

This article presents a parameter combination-based method selection (PCBMS) approach to select an optimal lossless data compression technique and provides an analysis based on experimental results to show its effectiveness. There are different types of data such as image, audio, video, and text. These data are classified based on the number of bits. Many algorithms have been developed to compress data over the past few decades, but no developed algorithm works well on all types of data. Lossless data compression techniques are mainly evaluated based on the compression ratio, encoding, and decoding time. While a higher compression ratio is more important for some applications, others may require faster encoding or decoding, or both. Alternatively, each of the three parameters can be equally significant. Choosing an optimal algorithm from many algorithms based on an application’s requirements is a significant challenge. By analyzing the data from each perspective, this model recommends an algorithm as the best for each type of data. Based on the proposed model, an analysis is provided. For some sets of data, it has been demonstrated that the proposed method gives a better prediction to select an algorithm according to the needs of an application.
Universally-Optimal Distributed Shortest Paths and Transshipment via Graph-Based ℓ<sub>1</sub>-Oblivious Routing

We provide universally-optimal distributed graph algorithms for (1+ε)-approximate shortest path problems including shortest-path-tree and transshipment.The universal optimality of our algorithms guarantees that, on any n-node network G, our algorithm completes in T •n o(1) rounds whenever a T -round algorithm exists for G.This includes D • n o(1) -round algorithms for any planar or excluded-minor network.Our algorithms never require more than ( √ n + D) • n o(1) rounds, resulting in the first sub-linear-round distributed algorithm for transshipment.
Show issue Year 6/2021 Issue 35 Sensitivity of numerate individuals to large asymmetry in outcomes: A registered replication of Traczyk et al. (2018)

The main aim of this study is to replicate the effect shown by Traczyk et al. (2018), where individuals with higher statistical numeracy, compared to individuals with lower statistical numeracy, employed a more effortful choice strategy when outcomes were meaningful. I hypothesize that participants with higher numeracy will be more likely to make choices predicted by Cumulative Prospect Theory and Expected Value theory (CPT/EV) in high-payoff problems than in low-payoff problems. Data collection was done online by appointing 73 participants. Participants’ preference, fluid intelligence, objective and subjective numeracy were measured using thirteen high and eleven low payoff choice problems, International Cognitive Ability Resource (ICAR), Berlin Numeracy Test (BNT), and Subjective Numeracy Scale (SNS), respectively. All the measures mentioned above were presented randomly. Results showed that all participants, in high-payoff condition, on average maximized EV; however, participants with high BNT scores were more likely to make choices consistent with CPT/EV predictions than individuals with low BNT scores. Furthermore, compared to less numerate participants, highly numerate participants were less likely to make choices consistent with CPT/EV predictions in low-payoff condition. Highly numerate individuals adjusted their choice strategy by modulating their response time, indicating their discernible sensitivity towards large asymmetry in payoff. In conclusion, the effect shown by Traczyk et al. (2018) was successfully replicated.
Current Situation and Strategy Formulation of College Sports Psychology Teaching Following Adaptive Learning and Deep Learning Under Information Education

This study aims to explore the current situation and strategy formulation of sports psychology teaching in colleges and universities following adaptive learning and deep learning under information education. The informatization in physical education, teaching methods, and teaching processes make psychological education more scientific and efficient. First, the relevant theories of adaptive learning and deep learning are introduced, and an adaptive learning analysis model is implemented. Second, based on the deep learning automatic encoder, college students' sports psychology is investigated and the test results are predicted. Finally, the current situation and development strategy of physical education in colleges and universities are analyzed. The results show that when the learning rate is 1, 0.1, and 0.01, there is no significant change in the analysis factors of recall, ndcg, item_coverage, and sps. When the learning rate is 1, their analysis factors change obviously, and it is calculated that the optimal learning rate of the model is 1. And the difficulty of the recommended test questions by using the sports psychology teaching method based on adaptive learning and deep learning is relatively stable. The test questions include various language points of sports psychology. Compared with others methods, adaptive learning and deep learning can provide comprehensive test questions for sports psychology teaching. This study provides technical support for the reform of sports psychology teaching in colleges and universities and contributes to optimizing the information-based teaching mode.
CAIT: A Predictive Tool for Supporting the Book Market Operation Using Social Networks

A new predictive support tool for the publishing industry is presented in this note. It consists of a combined model of Artificial Intelligence techniques (CAIT) that seeks the most optimal prediction of the number of book copies, finding out which is the best segmentation of the book market, using data from the networks social and the web. Predicted sales appear to be more accurate, applying machine learning techniques such as clustering (in this specific case, KMeans) rather than using current publishing industry expert’s segmentation. This identification has important implications for the publishing sector since the forecast will adjust more to the behavior of the stakeholders than to the skills or knowledge acquired by the experts, which is a certain way that may not be sufficient and/or variable throughout the period.
Blockchain Beyond Cryptocurrency: Non-Fungible Tokens

Blockchain technology is a disruptive innovation with the potential to replace existing business models that rely on centralized systems and third parties for trust. Even if there are a lot of application areas, blockchain used primarily for cryptocurrencies. Satoshi Nakamoto implemented the first blockchain application and invented the world’s first digital currency which is named as Bitcoin in 2008. Fundementally Bitcoin relies on cryptographic “proof of work” mechanism, digital signatures, and peer to peer distributed networking layer in order to provide a distributed ledger holding transactions. In 2014, a second generation of blockchains allow to program and execute them over distributed networks such as Ethereum project. The code to program any asset stored in blockchain’s peer-to-peer network is called as "smart contract" and smart contracts gives a powerful tool to developers for decentralized applications. There are various types of tokens that anyone can built on top of Ethereum and by combining smart contracts and new tokens, this paved the way of possibility to build a wide range of decentralized projects. One of the disruptive blockchain based innovation impacting intellectual property is called non-fungible-tokens or NFTs firstly introcuced in late 2017 on Ethereum network. This research contends that blockchain and non-fungible tokens (NFTs) which are cryptographically unique, scarce, non-replicable digital assets created through smart contracts and provably digital collectible assets. Our objective is to give NFT taxonomy, review NFT platforms and discuss technical challenges as well as recent advances in tackling the challenges. Moreover, this paper also aims to point out the future directions for NFT technology.
Aligning Actions Across Recipe Graphs

Recipe texts are an idiosyncratic form of instructional language that pose unique challenges for automatic understanding.One challenge is that a cooking step in one recipe can be explained in another recipe in different words, at a different level of abstraction, or not at all.Previous work has annotated correspondences between recipe instructions at the sentence level, often glossing over important correspondences between cooking steps across recipes.We present a novel and fully-parsed English recipe corpus, ARA (Aligned Recipe Actions), which annotates correspondences between individual actions across similar recipes with the goal of capturing information implicit for accurate recipe understanding.We represent this information in the form of recipe graphs, and we train a neural model for predicting correspondences on ARA.We find that substantial gains in accuracy can be obtained by taking fine-grained structural information about the recipes into account.
Definition Modelling for Appropriate Specificity

Definition generation techniques aim to generate a definition of a target word or phrase given a context.In previous studies, researchers have faced various issues such as the out-of-vocabulary problem and over/underspecificity problems.Over-specific definitions present narrow word meanings, whereas under-specific definitions present general and context-insensitive meanings.Herein, we propose a method for definition generation with appropriate specificity.The proposed method addresses the aforementioned problems by leveraging a pre-trained encoderdecoder model, namely Text-to-Text Transfer Transformer, and introducing a re-ranking mechanism to model specificity in definitions. 1 Experimental results on standard evaluation datasets indicate that our method significantly outperforms the previous state-of-theart method.Moreover, manual evaluation confirms that our method effectively addresses the over/under-specificity problems.
A New Active Contour Medical Image Segmentation Method Based on Fractional Varying-Order Differential

Image segmentation technology is dedicated to the segmentation of intensity inhomogeneous at present. In this paper, we propose a new method that incorporates fractional varying-order differential and local fitting energy to construct a new variational level set active contour model. The energy functions in this paper mainly include three parts: the local term, the regular term and the penalty term. The local term combined with fractional varying-order differential can obtain more details of the image. The regular term is used to regularize the image contour length. The penalty term is used to keep the evolution curve smooth. True positive (TP) rate, false positive (FP) rate, precision (P) rate, Jaccard similarity coefficient (JSC), and Dice similarity coefficient (DSC) are employed as the comparative measures for the segmentation results. Experimental results for both synthetic and real images show that our method has more accurate segmentation results than other models, and it is robust to intensity inhomogeneous or noises.
Training Data Subset Search With Ensemble Active Learning

Deep Neural Networks (DNNs) often rely on vast datasets for training. Given the large size of such datasets, it is conceivable that they contain specific samples that either do not contribute or negatively impact the DNN’s optimization. Modifying the training distribution to exclude such samples could provide an effective solution to improve performance and reduce training time. This paper proposes to scale up ensemble Active Learning (AL) methods to perform acquisition at a large scale (10k to 500k samples at a time). We do this with ensembles of hundreds of models, obtained at a minimal computational cost by reusing intermediate training checkpoints. This allows us to automatically and efficiently perform a training data subset search for large labeled datasets. We observe that our approach obtains favorable subsets of training data, which can be used to train more accurate DNNs than training with the entire dataset. We perform an extensive experimental study of this phenomenon on three image classification benchmarks (CIFAR-10, CIFAR-100, and ImageNet), as well as an internal object detection benchmark for prototyping perception models for autonomous driving. Unlike existing studies, our experiments on object detection are at the scale required for production-ready autonomous driving systems. We provide insights on the impact of different initialization schemes, acquisition functions, and ensemble configurations at this scale. Our results provide strong empirical evidence that optimizing the training data distribution can significantly benefit large-scale vision tasks.
Measurement-based multipartite entanglement inflation

Generating entanglement between more parties is one of the central tasks and challenges in the backdrop of building quantum technologies. Here we propose a measurement-based protocol for producing multipartite entangled states which can be later fed into some network for realizing suitable quantum protocols. We consider weak entangling measurement on two parties as the basic unit of operation to create entanglement between more parties starting from an entangled state with a lesser number of parties and auxiliary systems in the form of a single-qubit or entangled state itself. We call the introduced expansion procedure, ``multipartite entanglement inflation.'' In the context of inflating bipartite entanglement to more number of parties, surprisingly, maximally entangled states as inputs turn out to be worse than that of the nonmaximally entangled states, Haar uniformly generated pure states having a moderate amount of entanglement and the Werner state with a certain threshold noise. We also report that the average multipartite entanglement created from the initial Greenberger-Horne-Zeilinger- and the $W$-class states are almost the same. Interestingly, we also observe that for Haar uniformly generated pure states, unentangled auxiliary systems are sometimes more advantageous than the protocol with multiple copies of the initial entangled states.
Research on Real-Time Face Key Point Detection Algorithm Based on Attention Mechanism

The existing face detection methods were affected by the network model structure used. Most of the face recognition methods had low recognition rate of face key point features due to many parameters and large amount of calculation. In order to improve the recognition accuracy and detection speed of face key points, a real-time face key point detection algorithm based on attention mechanism was proposed in this paper. Due to the multiscale characteristics of face key point features, the deep convolution network model was adopted, the attention module was added to the VGG network structure, the feature enhancement module and feature fusion module were combined to improve the shallow feature representation ability of VGG, and the cascade attention mechanism was used to improve the deep feature representation ability. Experiments showed that the proposed algorithm not only can effectively realize face key point recognition but also has better recognition accuracy and detection speed than other similar methods. This method can provide some theoretical basis and technical support for face detection in complex environment.
Design and Implementation of Open Source Online Evaluation System Based on Cloud Platform

In order to provide a good practice platform for the program design contestants and algorithm enthusiasts, this paper designs and implements a programming online evaluation system based on cloud platform, which is a web system that can return the test results of the program source codes submitted by users in real time. It realizes the automatic evaluation of the program design training questions. The system is implemented by the way of front-end and backend separation and modular programming. The front-end of the web is implemented by Vue framework, the back-end is implemented by Django framework. The judgment core is written in C language for the sake of improving the efficiency of the system. Sandbox technology is used to ensure the security of the judgment system. Experimental results show that the system can meet the need of daily teaching and competition.
CLASSIFICATION MODEL FOR BREAST CANCER MAMMOGRAMS

Machine learning has been the topic of interest in research related to early detection of breast cancer based on mammogram images. In this study, we compare the performance results from three (3) types of machine learning techniques: 1) Naïve Bayes (NB), 2) Neural Network (NN) and 3) Support Vector Machine (SVM) with 2000 digital mammogram images to choose the best technique that could model the relationship between the features extracted and the state of the breast (‘Normal’ or ‘Cancer’). Grey Level Co-occurrence Matrix (GLCM) which represents the two dimensions of the level variation gray in the image is used in the feature extraction process. Six (6) attributes consist of contrast, variance, standard deviation, kurtosis, mean and smoothness were computed as feature extracted and used as the inputs for the classification process. The data has been randomized and the experiment has been repeated for ten (10) times to check for the consistencies of the performance of all techniques. 70% of the data were used as the training data and another 30% used as testing data. The result after ten (10) experiments show that, Support Vector Machine (SVM) gives the most consistent results in correctly classifying the state of the breast as ‘Normal’ or ‘Cancer’, with the accuracy of 99.4%, in training and 98.76% in testing. The SVM classification model has outperformed NN and NB model in the study, and it shows that SVM is a good choice for determining the state of the breast at the early stage. ABSTRAK: Pembelajaran mesin telah menjadi topik yang diminati dalam penyelidikan yang berkaitan dengan pengesanan awal kanser payudara berdasarkan imej mamogram. Dalam kajian ini, kami membandingkan hasil prestasi dari tiga (3) jenis teknik pembelajaran mesin: 1) Naïve Bayes (NB), 2) Neural Network (NN) dan 3) Support Vector Machine (SVM) dengan 2000 imej digital mammogram hingga teknik terbaik yang dapat memodelkan hubungan antara ciri yang diekstraksi dan keadaan payudara ('Normal' atau 'Cancer') dapat diperoleh. Grey Level Co-occurrence Matrix (GLCM) yang mewakili dua dimensi variasi tahap kelabu pada gambar digunakan dalam proses pengekstrakan ciri. Enam (6) atribut terdiri dari kontras, varians, sisihan piawai, kurtosis, min dan kehalusan dihitung sebagai fitur yang diekstrak dan digunakan sebagai input untuk proses klasifikasi. Eksperimen telah diulang selama sepuluh (10) kali untuk memeriksa kesesuaian prestasi semua teknik. 70% data digunakan sebagai data latihan dan 30% lagi digunakan sebagai data ujian. Hasil setelah sepuluh (10) eksperimen menunjukkan bahawa, Support Vector Machine (SVM) memberikan hasil yang paling konsisten dalam mengklasifikasikan keadaan payudara dengan betul sebagai 'Normal' atau 'Kanser', dengan akurasi 99.4%, dalam latihan dan 98.76% dalam ujian. Model klasifikasi SVM telah mengungguli model NN dan NB dalam kajian ini, dan ia menunjukkan bahawa SVM adalah pilihan yang baik untuk menentukan keadaan payudara pada peringkat awal.