Reviewerly/exHarmony huggingface dataset:
Label Hallucination for Few-Shot Classification

Few-shot classification requires adapting knowledge learned from a large annotated base dataset to recognize novel unseen classes, each represented by few labeled examples. In such a scenario, pretraining a network with high capacity on the large dataset and then finetuning it on the few examples causes severe overfitting. At the same time, training a simple linear classifier on top of ``frozen'' features learned from the large labeled dataset fails to adapt the model to the properties of the novel classes, effectively inducing underfitting. In this paper we propose an alternative approach to both of these two popular strategies. First, our method pseudo-labels the entire large dataset using the linear classifier trained on the novel classes. This effectively ``hallucinates'' the novel classes in the large dataset, despite the novel categories not being present in the base database (novel and base classes are disjoint). Then, it finetunes the entire model with a distillation loss on the pseudo-labeled base examples, in addition to the standard cross-entropy loss on the novel dataset. This step effectively trains the network to recognize contextual and appearance cues that are useful for the novel-category recognition but using the entire large-scale base dataset and thus overcoming the inherent data-scarcity problem of few-shot learning. Despite the simplicity of the approach, we show that that our method outperforms the state-of-the-art on four well-established few-shot classification benchmarks. The code is available at https://github.com/yiren-jian/LabelHalluc.
Neighborhood-Adaptive Structure Augmented Metric Learning

Most metric learning techniques typically focus on sample embedding learning, while implicitly assume a homogeneous local neighborhood around each sample, based on the metrics used in training ( e.g., hypersphere for Euclidean distance or unit hyperspherical crown for cosine distance). As real-world data often lies on a low-dimensional manifold curved in a high-dimensional space, it is unlikely that everywhere of the manifold shares the same local structures in the input space. Besides, considering the non-linearity of neural networks, the local structure in the output embedding space may not be homogeneous as assumed. Therefore, representing each sample simply with its embedding while ignoring its individual neighborhood structure would have limitations in Embedding-Based Retrieval (EBR). By exploiting the heterogeneity of local structures in the embedding space, we propose a Neighborhood-Adaptive Structure Augmented metric learning framework (NASA), where the neighborhood structure is realized as a structure embedding, and learned along with the sample embedding in a self-supervised manner. In this way, without any modifications, most indexing techniques can be used to support large-scale EBR with NASA embeddings. Experiments on six standard benchmarks with two kinds of embeddings, i.e., binary embeddings and real-valued embeddings, show that our method significantly improves and outperforms the state-of-the-art methods.
IMPLI: Investigating NLI Models’ Performance on Figurative Language

Natural language inference (NLI) has been widely used as a task to train and evaluate models for language understanding.However, the ability of NLI models to perform inferences requiring understanding of figurative language such as idioms and metaphors remains understudied.We introduce the IMPLI (Idiomatic and Metaphoric Paired Language Inference) dataset, an English dataset consisting of paired sentences spanning idioms and metaphors.We develop novel methods to generate 24k semiautomatic pairs as well as manually creating 1.8k gold pairs.We use IMPLI to evaluate NLI models based on RoBERTa fine-tuned on the widely used MNLI dataset.We then show that while they can reliably detect entailment relationship between figurative phrases with their literal counterparts, they perform poorly on similarly structured examples where pairs are designed to be non-entailing.This suggests the limits of current NLI models with regard to understanding figurative language and this dataset serves as a benchmark for future improvements in this direction. 1* The work was done while the second author
MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes

We introduce MemSum (Multi-step Episodic Markov decision process extractive SUMmarizer), a reinforcement-learning-based extractive summarizer enriched at each step with information on the current extraction history.When MemSum iteratively selects sentences into the summary, it considers a broad information set that would intuitively also be used by humans in this task: 1) the text content of the sentence, 2) the global text context of the rest of the document, and 3) the extraction history consisting of the set of sentences that have already been extracted.With a lightweight architecture, MemSum obtains state-of-the-art test-set performance (ROUGE) in summarizing long documents taken from PubMed, arXiv, and GovReport.Ablation studies demonstrate the importance of local, global, and history information.A human evaluation confirms the high quality and low redundancy of the generated summaries, stemming from MemSum's awareness of extraction history.
Your fairness may vary: Pretrained language model fairness in toxic text classification

The popularity of pretrained language models in natural language processing systems calls for a careful evaluation of such models in down-stream tasks, which have a higher potential for societal impact.The evaluation of such systems usually focuses on accuracy measures.Our findings in this paper call for attention to be paid to fairness measures as well.Through the analysis of more than a dozen pretrained language models of varying sizes on two toxic text classification tasks (English), we demonstrate that focusing on accuracy measures alone can lead to models with wide variation in fairness characteristics.Specifically, we observe that fairness can vary even more than accuracy with increasing training data size and different random initializations.At the same time, we find that little of the fairness variation is explained by model size, despite claims in the literature.To improve model fairness without retraining, we show that two post-processing methods developed for structured, tabular data can be successfully applied to a range of pretrained language models.
Discriminative Marginalized Probabilistic Neural Method for Multi-Document Summarization of Medical Literature

Although current state-of-the-art Transformerbased solutions succeeded in a wide range for single-document NLP tasks, they still struggle to address multi-input tasks such as multidocument summarization.Many solutions truncate the inputs, thus ignoring potential summary-relevant contents, which is unacceptable in the medical domain where each information can be vital.Others leverage linear model approximations to apply multi-input concatenation, worsening the results because all information is considered, even if it is conflicting or noisy with respect to a shared background.Despite the importance and social impact of medicine, there are no ad-hoc solutions for multi-document summarization.For this reason, we propose a novel discriminative marginalized probabilistic method (DAMEN) trained to discriminate critical information from a cluster of topic-related medical documents and generate a multi-document summary via token probability marginalization.Results prove we outperform the previous state-of-theart on a biomedical dataset for multi-document summarization of systematic literature reviews.Moreover, we perform extensive ablation studies to motivate the design choices and prove the importance of each module of our method. 1
Sentiment Analysis of Covid-19 Vaccination using Support Vector Machine in Indonesia

Along with the development of the Covid-19 pandemic, many responses and news were shared through social media.The new Covid-19 vaccination promoted by the government has raised pros and cons from the public.Public resistance to covid-19 vaccination will lead to a higher fatality rate.This study carried out sentiment analysis about the Covid-19 vaccine using the Support Vector Machine (SVM).This research aims to study the public response to the acceptance of the vaccination program.The research result can be used to determine the direction of government policy.Data collection was taken via Twitter in the year 2021.The data then undergoes the preprocessing methods.Afterward, the data is processed using SVM classification.Finally, the result is evaluated by a confusion matrix.The experimental result shows that SVM produces 56.80% positive, 33.75% neutral, and 9.45% negative.The highest model accuracy was obtained by RBF kernel of 92%, linear and polynomial kernels obtained 90% accuracy, and sigmoid kernel obtained 89% accuracy.
Structured Sparse Coding With the Group Log-regularizer for Key Frame Extraction

Key frame extraction based on sparse coding can reduce the redundancy of continuous frames and concisely express the entire video. However, how to develop a key frame extraction algorithm that can automatically extract a few frames with a low reconstruction error remains a challenge. In this paper, we propose a novel model of structured sparse-coding-based key frame extraction, wherein a nonconvex group log-regularizer is used with strong sparsity and a low reconstruction error. To automatically extract key frames, a decomposition scheme is designed to separate the sparse coefficient matrix by rows. The rows enforced by the nonconvex group log-regularizer become zero or nonzero, leading to the learning of the structured sparse coefficient matrix. To solve the nonconvex problems due to the log-regularizer, the difference of convex algorithm (DCA) is employed to decompose the log-regularizer into the difference of two convex functions related to the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$I_{1}$</tex> norm, which can be directly obtained through the proximal operator. Therefore, an efficient structured sparse coding algorithm with the group log-regularizer for key frame extraction is developed, which can automatically extract a few frames directly from the video to represent the entire video with a low reconstruction error. Experimental results demonstrate that the proposed algorithm can extract more accurate key frames from most SumMe videos compared to the state-of-the-art methods. Furthermore, the proposed algorithm can obtain a higher compression with a nearly 18% increase compared to sparse modeling representation selection (SMRS) and an 8% increase compared to SC-det on the VSUMM dataset.
Learning Target-aware Representation for Visual Tracking via Informative Interactions

We introduce a novel backbone architecture to improve target-perception ability of feature representation for tracking. Having observed de facto frameworks perform feature matching simply using the backbone outputs for target localization, there is no direct feedback from the matching module to the backbone network, especially the shallow layers. Concretely, only the matching module can directly access the target information, while the representation learning of candidate frame is blind to the reference target. Therefore, the accumulated target-irrelevant interference in shallow stages may degrade the feature quality of deeper layers. In this paper, we approach the problem by conducting multiple branch-wise interactions inside the Siamese-like backbone networks (InBN). The core of InBN is a general interaction modeler (GIM) that injects the target information to different stages of the backbone network, leading to better target-perception of candidate feature representation with negligible computation cost. The proposed GIM module and InBN mechanism are general and applicable to different backbone types including CNN and Transformer for improvements, as evidenced on multiple benchmarks. In particular, the CNN version improves the baseline with 3.2/6.9 absolute gains of SUC on LaSOT/TNL2K. The Transformer version obtains SUC of 65.7/52.0 on LaSOT/TNL2K, which are on par with recent SOTAs.
Blockchain-Based Supply Chain Information Sharing Mechanism

This paper proposes a blockchain-based supply chain information operation method to solve the current shortage of business information sharing in the supply chain.These approaches include the idea of blockchain application to realize the sharing of supply chain information, the supply chain architecture of production enterprises based on blockchain, and the hierarchical model of supply chain information flow based on blockchain.This paper proposes the information block recording method of internal and external data sources in the supply chain, including the composition structure and the analysis of the multisource data inside and outside the supply chain.In addition, the multisource data information block recording method is also proposed.Building a blockchain-based supply chain system can improve the integration and reconstruction capabilities of the supply chain.The information recording of the internal data of the supply chain and the external related multisource data in the block can improve the learnability of the supply chain.The internal and external information block connection of the supply chain system is the further integration and reconstruction of the supply chain information resources in the blockchain system.The information storage and access control of the blockchain-based supply chain is an information security guarantee system in the supply chain information sharing environment.Through the method of this study, the system can form a business architecture system based on blockchain-based supply chain information sharing.
More than Code

Motivated and competent team members are a vital part of Agile Software development and make or break any project's success. Motivation is fostered by continuous progress and recognition of efforts. These concepts are founding pillars of the Scrum methodology, which focuses on self-organizing teams. The types of contributions Scrum development team members make to a project's progress are not only technical. However, a comprehensive model comprising the varied contributions in modern software engineering teams is not yet established. We propose a model that incorporates contributions of all Scrum roles, explicitly including those which are not directly related to project artifacts. It improves the visibility of performed tasks, acts as a starting point for team retrospection, and serves as a foundation for discussion in the research community.
Dynamic smart random preference for higher medical image confidentiality

It is essential to secure the information to store or transfer medical digital files without destruction. Currently, all used e-health files requests to be utilized in well-controlled, protected, and dependable style avoiding breaches and hacking. This research focuses on medical confidentiality encrypting grayscale health images for comfortable safe utilization. The work depends on resilience randomization and XOR operations for its medical-image cryptography. It tests performance of some random generators conveying the best every time running that is dynamically changing depending on e-health image variations. The research tests several randomizations structures processed as two sequenced encryption methods adopting substitution and transposition. The work tested random variations to encrypt different medical grayscale images revealing attractive remarks. The paper investigation intends to recognize appropriate preference via secrecy testing typical notations. The work indicates this flexibility of best applicable PRNG and its change features interesting privacy intellectual medical gray-image security for open e-health research direction to benefit from.
Intermediation in design as a practice of institutioning and commoning

Design practices often involve grassroots communities and institutional actors with designers working as or with intermediaries. This paper defines intermediation as a significant design practice, especially when designers engage in commoning and institutioning—concepts that have gained traction in recent discourses in design scholarship. To discuss intermediation, the paper introduces two case studies: a community radio project in Europe and a food supply chain project between Australia and China. The two cases show implicit and explicit ways that intermediation can form an integral part of the practices and competences of designers. The paper discusses the merits of intermediation skills and techniques as well as how intermediation can be better embedded and integrated in design practices.
TripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM

3D object tracking is a critical task in autonomous driving systems. It plays an essential role for the system's awareness about the surrounding environment. At the same time there is an increasing interest in algorithms for autonomous cars that solely rely on inexpensive sensors, such as cameras. In this paper we investigate the use of triplet embeddings in combination with motion representations for 3D object tracking. We start from an off-the-shelf 3D object detector, and apply a tracking mechanism where objects are matched by an affinity score computed on local object feature embeddings and motion descriptors. The feature embeddings are trained to include information about the visual appearance and monocular 3D object characteristics, while motion descriptors provide a strong representation of object trajectories. We will show that our approach effectively re-identifies objects, and also behaves reliably and accurately in case of occlusions, missed detections and can detect re-appearance across different field of views. Experimental evaluation shows that our approach outperforms state-of-the-art on nuScenes by a large margin. We also obtain competitive results on KITTI.
Pyramidal Attention for Saliency Detection

Salient object detection (SOD) extracts meaningful contents from an input image. RGB-based SOD methods lack the complementary depth clues; hence, providing limited performance for complex scenarios. Similarly, RGB-D models process RGB and depth inputs, but the depth data availability during testing may hinder the model’s practical applicability. This paper exploits only RGB images, estimates depth from RGB, and leverages the intermediate depth features. We employ a pyramidal attention structure to extract multi-level convolutional-transformer features to process initial stage representations and further enhance the subsequent ones. At each stage, the backbone transformer model produces global receptive fields and computing in parallel to attain fine-grained global predictions refined by our residual convolutional attention decoder for optimal saliency prediction. We report significantly improved performance against 21 and 40 state-of-the-art SOD methods on eight RGB and RGB-D datasets, respectively. Consequently, we present a new SOD perspective of generating RGB-D SOD without acquiring depth data during training and testing and assist RGB methods with depth clues for improved performance. The code and trained models are available at https://github.com/tanveer-hussain/EfficientSOD2
Intelligent Grazing UAV Based on Airborne Depth Reasoning

The existing precision grazing technology helps to improve the utilization rate of livestock to pasture, but it is still at the level of “collectivization” and cannot provide more accurate grazing management and control. (1) Background: In recent years, with the rapid development of agent-related technologies such as deep learning, visual navigation and tracking, more and more lightweight edge computing cell target detection algorithms have been proposed. (2) Methods: In this study, the improved YOLOv5 detector combined with the extended dataset realized the accurate identification and location of domestic cattle; with the help of the kernel correlation filter (KCF) automatic tracking framework, the long-term cyclic convolution network (LRCN) was used to analyze the texture characteristics of animal fur and effectively distinguish the individual cattle. (3) Results: The intelligent UAV equipped with an AGX Xavier high-performance computing unit ran the above algorithm through edge computing and effectively realized the individual identification and positioning of cattle during the actual flight. (4) Conclusion: The UAV platform based on airborne depth reasoning is expected to help the development of smart ecological animal husbandry and provide better precision services for herdsmen.
Predictive Analysis of Mental Health Conditions Using AdaBoost Algorithm

The presented research responds to increased mental illness conditions worldwide and the need for efficient mental health care (MHC) through machine learning (ML) implementations. The datasets employed in this investigation belong to a Kaggle repository named "Mental Health Tech Survey." The surveys for the years 2014 and 2016 were downloaded and aggregated. The prediction results for bagging, stacking, LR, KNN, tree class, NN, RF, and Adaboost yielded 75.93%, 75.93%, 79.89%, 90.42%, 80.69%, 89.95%, 81.22%, and 81.75% respectively. The AdaBoost ML model performed data cleaning and prediction on the datasets, reaching an accuracy of 81.75%, which is good enough for decision-making. The results were further used with other ML models such as Random Forest (RF), K-Nearest Neighbor (KNN), bagging, and a few others, with reported accuracy ranging from 81.22 to 75.93 which is good enough for decision making. Out of all the models used for predicting mental health treatment outcomes, AdaBoost has the highest accuracy.
Educational electronic platforms for STEAM-oriented learning environment at general education school

The article is devoted to the problem of the use of educational electronic platform for the organization of a STEAM-oriented environment of the general school. The purpose of the article is to analyze the use of educational electronic platforms for organizing the STEAM-oriented school learning environment and to identify the basic requirements for supporting the implementation and development of STEAM education in Ukraine. One of the main trends of education modernization is the STEAM education, which involves the integration between the natural sciences, the technological sciences, engineering, mathematics and art in the learning process of educational institutions, in particular, general school. The main components of electronic platform for education of the organization STEAM-oriented educational environment should be open e-learning and educational resources that include resources for students and resources for teachers; information and communication technologies that provide communication and collaboration among students; between teachers; between students and teachers; between specialists, employers, students, and teachers; information and communication technologies that promote the development of STEAM education and its implementation in the educational process of the school; online assessment and self-assessment of skills and competences in STEAM education and information and communication technologies fields; STEAM education labs that may include simulators, games, imitation models, etc.; STEAM-oriented educational environment profiles that reflect unconfirmed participants’ data, their contributions to projects and STEAM education, plans, ideas, personal forums, and more. Prospects for further research are the design of an educational electronic platform for the organization of the STEAM-oriented learning environment in accordance with the requirements specified in the paper.