Reviewerly/exHarmony huggingface dataset:
A Game Theoretic Scheme for Collaborative Vehicular Task Offloading in 5G HetNets

The 5G heterogeneous networks (HetNets) are capable of providing real-time computing services for autonomous vehicles (AVs) by deploying edge computing devices (ECDs) at macro cell base stations (MCBSs) and small cell base stations (SCBSs). With the imbalanced distribution and fast moving AVs contending intensely for computing services, how to efficiently exploit cooperations among participants in 5G HetNets to improve the service performance is therefore challenging. In this paper, we develop a game theoretic scheme for collaborative vehicular task offloading to facilitate the computing services in 5G HetNets. Specifically, we propose a two-stage vehicular task offloading mechanism to promote the cooperation among participants with the target of improving the task completion rate and the utilities of the participants, where the mechanism jointly considers the network architecture of the HetNets, the imbalanced distribution of AVs and the reuse of task results. In the first stage, an auction model is designed to help the MCBS select the optimal SCBS to execute the offloaded task based on the requirement of the task and the available computing resources of SCBSs. According to the task execution cost declared by the selected SCBS, the MCBS then bargains with the AV for the agreement of the task offloading service to maximize their utilities in the second stage. Using simulations, we show that the proposed collaborative task offloading scheme can achieve a higher task completion rate for the task offloading service and bring higher utilities to all participants than conventional schemes.
DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION

Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture \textbf{DeBERTa} (\textbf{D}ecoding-\textbf{e}nhanced \textbf{BERT} with disentangled \textbf{a}ttention) that improves the BERT and RoBERTa models using two novel techniques. The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions. Second, an enhanced mask decoder is used to incorporate absolute positions in the decoding layer to predict the masked tokens in model pre-training. We show that these two techniques significantly improve the efficiency of model pre-training and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). The DeBERTa code and pre-trained models will be made publicly available.
Optimization of decision trees using modified African buffalo algorithm

Decision tree induction is a simple, however powerful learning and classification tool to discover knowledge from the database. The volume of data in databases is growing to quite large sizes, both in the number of attributes and instances. Some important limitations of decision trees are instability, local decisions, and overfitting for this extensive data. The simple, effective and non-convergence nature of the African Buffalo Optimization (ABO) algorithm makes it suitable to solve complex optimization problems. In this paper, we propose the African Buffalo Optimized Decision Tree (ABODT) algorithm to create globally optimized decision trees using the intelligent and collective behaviour of African Buffalos. The modified African Buffalo optimization algorithm is used to create efficient and optimal decision trees. To evaluate the efficiency of the proposed African Buffalo Optimized Decision Tree algorithm, experiments are performed on 15 standard UCI learning repository datasets that are of various sizes and domains. Results show that the African Buffalo Optimized Decision Tree algorithm globally optimizes decision trees, increases accuracy and reduces the size of a decision tree. These optimized trees are stable and efficient than conventional decision trees.
Multivariate statistical analysis of trace element compositions of native gold from orogenic gold deposits: Implication for mineral exploration

The mineralogy and chemical composition of native gold from twenty-seven representative orogenic gold deposits were investigated using optical microscopy, electron probe microanalysis (EPMA), and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS). Minor elements, such as Ag and Cu, occur in solid solution, while trace elements, such as Fe, As, S, Hg, form impurities or micro inclusions in native gold. Partial least squares-discriminant analysis (PLS-DA) has identified compositional characteristics based on mineral association, gold texture and dominant country rocks. Gold grains are commonly associated with pyrite, arsenopyrite, chalcopyrite, pyrrhotite, and tourmaline in the studied orogenic deposits. Chemical variations in gold with different associated mineral assemblages are related to the partitioning of trace elements between co-crystalizing minerals and gold during precipitation. Gold inclusions in gangue minerals are discriminated from later gold in fractures based on contents of Ag, Fe, Pb, and Bi, which indicates that gold in different paragenetic stages of mineralization can be identified using its trace element signature. Gold hosted in different regional country rocks can be discriminated by Ag, Cu, Pd, Sb, and Hg, likely because of reactions of hydrothermal fluids with the regional country rocks along the fluid flow paths.
Hyperchaotic image encryption using phase-truncated fractional Fourier transform and DNA-level operation

A novel asymmetric image encryption method is developed by integrating with hyper-chaotic system (HCS), deoxyribonucleic acid (DNA) level operation, Arnold transform (ART) and phase-truncated fractional Fourier transform (ptFrFT). In this method, all the initial values and/or control parameters of HCS, DNA, Arnold transform and ptFrFT are generated from a plaintext image by utilizing the SHA-256 hash algorithm, which are also taken as security keys for one-time pad. In this case, the plaintext is scrambled by ART and then encoded into a noise-like interim image using ptFrFT with two random phase masks as private keys obtained by HCS. Afterwards, the interim image is converted into the final ciphertext using DNA-level diffusion and scrambling. All these operations yield large enough key space and high sensitivity to the keys. Only if all the correct keys are employed, the useful information can be achieved. Some numerical results are demonstrated that the proposal has good security and strong robustness to different attacks.
How to Trust Unlabeled Data? Instance Credibility Inference for Few-Shot Learning

Deep learning based models have excelled in many computer vision tasks and appear to surpass humans' performance. However, these models require an avalanche of expensive human labeled training data and many iterations to train their large number of parameters. This severely limits their scalability to the real-world long-tail distributed categories, some of which are with a large number of instances, but with only a few manually annotated. Learning from such extremely limited labeled examples is known as Few-Shot Learning (FSL). Different to prior arts that leverage meta-learning or data augmentation strategies to alleviate this extremely data-scarce problem, this paper presents a statistical approach, dubbed Instance Credibility Inference (ICI) to exploit the support of unlabeled instances for few-shot visual recognition. Typically, we repurpose the self-taught learning paradigm to predict pseudo-labels of unlabeled instances with an initial classifier trained from the few shot and then select the most confident ones to augment the training set to re-train the classifier. This is achieved by constructing a (Generalized) Linear Model (LM/GLM) with incidental parameters to model the mapping from (un-)labeled features to their (pseudo-)labels, in which the sparsity of the incidental parameters indicates the credibility of the corresponding pseudo-labeled instance. We rank the credibility of pseudo-labeled instances along the regularization path of their corresponding incidental parameters, and the most trustworthy pseudo-labeled examples are preserved as the augmented labeled instances. This process is repeated until all the unlabeled samples are included in the expanded training set. Theoretically, under the conditions of restricted eigenvalue, irrepresentability, and large error, our approach is guaranteed to collect all the correctly-predicted pseudo-labeled instances from the noisy pseudo-labeled set. Extensive experiments under two few-shot settings show the effectiveness of our approach on four widely used few-shot visual recognition benchmark datasets including miniImageNet, tieredImageNet, CIFAR-FS, and CUB. Code and models are released at https://github.com/Yikai-Wang/ICI-FSL.
CrossNER: Evaluating Cross-Domain Named Entity Recognition

Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains. However, most of the existing NER benchmarks lack domain-specialized entity types or do not focus on a certain domain, leading to a less effective cross-domain evaluation. To address these obstacles, we introduce a cross-domain NER dataset (CrossNER), a fully-labeled collection of NER data spanning over five diverse domains with specialized entity categories for different domains. Additionally, we also provide a domain-related corpus since using it to continue pre-training language models (domain-adaptive pre-training) is effective for the domain adaptation. We then conduct comprehensive experiments to explore the effectiveness of leveraging different levels of the domain corpus and pre-training strategies to do domain-adaptive pre-training for the cross-domain task. Results show that focusing on the fractional corpus containing domain-specialized entities and utilizing a more challenging pre-training strategy in domain-adaptive pre-training are beneficial for the NER domain adaptation, and our proposed method can consistently outperform existing cross-domain NER baselines. Nevertheless, experiments also illustrate the challenge of this cross-domain NER task. We hope that our dataset and baselines will catalyze research in the NER domain adaptation area. The code and data are available at https://github.com/zliucr/CrossNER.
Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search

Most differentiable neural architecture search methods construct a super-net for search and derive a target-net as its sub-graph for evaluation. There exists a significant gap between the architectures in search and evaluation. As a result, current methods suffer from an inconsistent, inefficient, and inflexible search process. In this paper, we introduce EnTranNAS that is composed of Engine-cells and Transit-cells. The Engine-cell is differentiable for architecture search, while the Transit-cell only transits a sub-graph by architecture derivation. Consequently, the gap between the architectures in search and evaluation is significantly reduced. Our method also spares much memory and computation cost, which speeds up the search process. A feature sharing strategy is introduced for more balanced optimization and more efficient search. Furthermore, we develop an architecture derivation method to replace the traditional one that is based on a hand-crafted rule. Our method enables differentiable sparsification, and keeps the derived architecture equivalent to that of Engine-cell, which further improves the consistency between search and evaluation. More importantly, it supports the search for topology where a node can be connected to prior nodes with any number of connections, so that the searched architectures could be more flexible. Our search on CIFAR-10 has an error rate of 2.22% with only 0.07 GPU-day. We can also directly perform the search on ImageNet with topology learnable and achieve a top-1 error rate of 23.8% in 2.1 GPU-day.
FINDINGS OF THE IWSLT 2021 EVALUATION CAMPAIGN

The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2021) featured this year four shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Multilingual speech translation, (iv) Low-resource speech translation.A total of 22 teams participated in at least one of the tasks.This paper describes each shared task, data and evaluation metrics, and reports results of the received submissions.
Multiview Subspace Clustering Using Low-Rank Representation

Multiview subspace clustering is one of the most widely used methods for exploiting the internal structures of multiview data. Most previous studies have performed the task of learning multiview representations by individually constructing an affinity matrix for each view without simultaneously exploiting the intrinsic characteristics of multiview data. In this article, we propose a multiview low-rank representation (MLRR) method to comprehensively discover the correlation of multiview data for multiview subspace clustering. MLRR considers symmetric low-rank representations (LRRs) to be an approximately linear spatial transformation under the new base, that is, the multiview data themselves, to fully exploit the angular information of the principal directions of LRRs, which is adopted to construct an affinity matrix for multiview subspace clustering, under a symmetric condition. MLRR takes full advantage of LRR techniques and a diversity regularization term to exploit the diversity and consistency of multiple views, respectively, and this method simultaneously imposes a symmetry constraint on LRRs. Hence, the angular information of the principal directions of rows is consistent with that of columns in symmetric LRRs. The MLRR model can be efficiently calculated by solving a convex optimization problem. Moreover, we present an intuitive fusion strategy for symmetric LRRs from the perspective of spectral clustering to obtain a compact representation, which can be shared by multiple views and comprehensively represents the intrinsic features of multiview data. Finally, the experimental results based on benchmark datasets demonstrate the effectiveness and robustness of MLRR compared with several state-of-the-art multiview subspace clustering algorithms.
Hate and harassment in academia: the rising concern of the online environment

Abstract Hostile online communication is a global concern. Academic research and teaching staff are among those professionals who routinely give public comments and are thus vulnerable to online attacks. This social psychological and criminological study investigated online harassment victimization among university researchers and teachers. Survey participants ( N = 2,492) were university research and teaching staff members from five major universities in Finland. Victimization was assessed with a 20-item inventory. The study included a wide range of both background and general measures on well-being at work. Participants also took part in an online experiment involving a death threat targeting a colleague. Results showed that 30% of the participants reported being victims of online harassment during the prior 6 months. Victims were more often senior staff members, minority group members, and from the social sciences and humanities. Those active in traditional or social media were much more likely to be targeted. Victims reported higher psychological distress, lower generalized trust, and lower perceived social support at work than non-victims. Individuals who were targeted by a colleague from their work community reported higher post-traumatic stress disorder scores and a higher impact of perceived online harassment on their work compared to other victims. In the experimental part of the study, participants reported more anxiety when a close colleague received a death threat. Participants also recommended more countermeasures to a close colleague than to an unknown person from the same research field. Results indicate that online harassment compromises well-being at work in academia. There is an urgent need to find ways of preventing online harassment, both in workplaces and in society at large.
Arithmetic Formulated Relevantly

&#x0D; &#x0D; &#x0D; The purpose of this paper is to formulate first-order Peano arithmetic within the resources of relevant logic, and to demonstrate certain properties of the system thus formulated. Striking among these properties are the facts that (1) it is trivial that relevant arithmetic is absolutely consistent, but (2) classical first-order Peano arithmetic is straightforwardly contained in relevant arithmetic. Under (1), I shall show in particular that 0 = 1 is a non-theorem of relevant arithmetic; this, of course, is exactly the formula whose unprovability was sought in the Hilbert program for proving arithmetic consistent. Under (2), I shall exhibit the requisite translation, drawing some Goedelian conclusions therefrom. Left open, however, is the critical problem whether Ackermann’s rule γ is admissible for theories of relevant arithmetic. &#x0D; &#x0D; &#x0D; &#x0D; The particular system of relevant Peano arithmetic featured in this paper shall be called R♯. Its logical base shall be the system R of relevant implication, taken in its first-order form RQ. Among other Peano arithmetics we shall consider here in particular the systems C♯, J♯, and RM3♯; these are based respectively on the classical logic C, the intuitionistic logic J, and the Sobocinski-Dunn semi-relevant logic RM3. And another feature of the paper will be the presentation of a system of natural deduction for R♯, along lines valid for first-order relevant theories in general. This formulation of R♯ makes it possible to construct relevantly valid arithmetical deductions in an easy and natural way; it is based on, but is in some respects more convenient than, the natural deduction formulations for relevant logics developed by Anderson and Belnap in Entailment. &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Online teaching during the COVID-19 pandemic: exploring science/STEM teachers’ curriculum and assessment practices in Canada

Abstract The COVID-19 pandemic necessitated school closures globally, resulting in an abrupt move to online/distance teaching or emergency remote teaching (ERT). Teachers and students pivoted from face-to-face engagement to online environments, thus impacting curriculum, pedagogy, and student outcomes across a variety of disciplines. In this paper, the authors focus on science/STEM teachers’ experiences with online teaching and learning in a Canadian context during the pandemic. Qualitative and quantitative data were collected through an online questionnaire administered to 75 Grade 1–12 science/STEM teachers in a Canadian province in May–July 2020. Through the TPACK framework and self-efficacy theory, the authors explore i) curriculum planning and implementation in online settings, ii) assessment practices and their effectiveness, and iii) student outcomes, as observed by the teachers. Results indicate that teachers used a variety of platforms, and choice of platform was mainly due to user-friendliness and interactivity, or administrative decision making. Despite teachers organizing online lessons during ERT, gaps were identified in teachers’ TPACK framework and self-efficacy, thus impacting their curriculum development, pedagogical approaches, and assessment practices. In general, teaching strategies included pre-recorded videos and self-directed learning in which teachers assigned specific tasks for students to perform independently. Teachers prioritized subject content and covering curriculum objectives over creative and student-centered pedagogical approaches. Assessment techniques employed were viewed by teachers as unauthentic and generally ineffective. Moreover, teachers reported difficulties addressing student needs and abilities, resulting in challenges providing equitable and inclusive online teaching. Finally, online teaching was viewed negatively by most teachers, in terms of student engagement and outcomes.
DigestPath: A benchmark dataset with challenge review for the pathological detection and segmentation of digestive-system

Examination of pathological images is the golden standard for diagnosing and screening many kinds of cancers. Multiple datasets, benchmarks, and challenges have been released in recent years, resulting in significant improvements in computer-aided diagnosis (CAD) of related diseases. However, few existing works focus on the digestive system. We released two well-annotated benchmark datasets and organized challenges for the digestive-system pathological cell detection and tissue segmentation, in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). This paper first introduces the two released datasets, i.e., signet ring cell detection and colonoscopy tissue segmentation, with the descriptions of data collection, annotation, and potential uses. We also report the set-up, evaluation metrics, and top-performing methods and results of two challenge tasks for cell detection and tissue segmentation. In particular, the challenge received 234 effective submissions from 32 participating teams, where top-performing teams developed advancing approaches and tools for the CAD of digestive pathology. To the best of our knowledge, these are the first released publicly available datasets with corresponding challenges for the digestive-system pathological detection and segmentation. The related datasets and results provide new opportunities for the research and application of digestive pathology.
Spectral Analysis to Improve Inputs to Random Forest and Other Boosted Ensemble Tree-Based Algorithms for Detecting NYF Pegmatites in Tysfjord, Norway

As an important source of lithium and rare earth elements (REE) and other critical elements, pegmatites are of great strategic economic interest for present and future technological development. Identifying new pegmatite deposits is a strategy adopted by the European Union (EU) to decrease its import dependence on non-European countries for these raw materials. It is in this context that the GREENPEG project was established, an EU project whose main objective is to identify new deposits of pegmatites in Europe in an environmentally friendly way. Remote sensing is a non-contact exploration tool that allows for identifying areas of interest for exploration at the early stage of exploration campaigns. Several RS methods have been developed to identify Li-Cs-Ta (LCT) pegmatites, but in this study, a new methodology was developed to detect Nb-Y-F (NYF) pegmatites in the Tysfjord area in Norway. This methodology is based on spectral analysis to select bands of the Sentinel 2 satellite and adapt RS methods, such as Band Ratios and Principal Component Analysis (PCA), to be used as input in the Random Forest (RF) and other tree-based ensemble algorithms to improve the classification accuracy. The results obtained are encouraging, and the algorithm was able to successfully identify the pegmatite areas already known and new locations of interest for exploration were also defined.