Reviewerly/exHarmony huggingface dataset:
Few-Shot Class-Incremental Learning via Relation Knowledge Distillation

In this paper, we focus on the challenging few-shot class incremental learning (FSCIL) problem, which requires to transfer knowledge from old tasks to new ones and solves catastrophic forgetting. We propose the exemplar relation distillation incremental learning framework to balance the tasks of old-knowledge preserving and new-knowledge adaptation. First, we construct an exemplar relation graph to represent the knowledge learned by the original network and update gradually for new tasks learning. Then an exemplar relation loss function for discovering the relation knowledge between different classes is introduced to learn and transfer the structural information in relation graph. A large number of experiments demonstrate that relation knowledge does exist in the exemplars and our approach outperforms other state-of-the-art class-incremental learning methods on the CIFAR100, miniImageNet, and CUB200 datasets.
Tensor Representations for Action Recognition

Human actions in video sequences are characterized by the complex interplay between spatial features and their temporal dynamics. In this paper, we propose novel tensor representations for compactly capturing such higher-order relationships between visual features for the task of action recognition. We propose two tensor-based feature representations, viz. (i) sequence compatibility kernel (SCK) and (ii) dynamics compatibility kernel (DCK). SCK builds on the spatio-temporal correlations between features, whereas DCK explicitly models the action dynamics of a sequence. We also explore generalization of SCK, coined SCK ⊕, that operates on subsequences to capture the local-global interplay of correlations, which can incorporate multi-modal inputs e.g., skeleton 3D body-joints and per-frame classifier scores obtained from deep learning models trained on videos. We introduce linearization of these kernels that lead to compact and fast descriptors. We provide experiments on (i) 3D skeleton action sequences, (ii) fine-grained video sequences, and (iii) standard non-fine-grained videos. As our final representations are tensors that capture higher-order relationships of features, they relate to co-occurrences for robust fine-grained recognition (Lin, 2017), (Koniusz, 2018). We use higher-order tensors and so-called Eigenvalue Power Normalization (EPN) which have been long speculated to perform spectral detection of higher-order occurrences (Koniusz, 2013), (Koniusz, 2017), thus detecting fine-grained relationships of features rather than merely count features in action sequences. We prove that a tensor of order r, built from Z* dimensional features, coupled with EPN indeed detects if at least one higher-order occurrence is 'projected' into one of its [Formula: see text] subspaces of dim. r represented by the tensor, thus forming a Tensor Power Normalization metric endowed with [Formula: see text] such 'detectors'.
Stand-Alone Use of Artificial Intelligence for Digital Mammography and Digital Breast Tomosynthesis Screening: A Retrospective Evaluation

Background Use of artificial intelligence (AI) as a stand-alone reader for digital mammography (DM) or digital breast tomosynthesis (DBT) breast screening could ease radiologists' workload while maintaining quality. Purpose To retrospectively evaluate the stand-alone performance of an AI system as an independent reader of DM and DBT screening examinations. Materials and Methods Consecutive screening-paired and independently read DM and DBT images acquired between January 2015 and December 2016 were retrospectively collected from the Tomosynthesis Cordoba Screening Trial. An AI system computed a cancer risk score (range, 1-100) for DM and DBT examinations independently. AI stand-alone performance was measured using the area under the receiver operating characteristic curve (AUC) and sensitivity and recall rate at different operating points selected to have noninferior sensitivity compared with the human readings (noninferiority margin, 5%). The recall rate of AI and the human readings were compared using a McNemar test. Results A total of 15 999 DM and DBT examinations (113 breast cancers, including 98 screen-detected and 15 interval cancers) from 15 998 women (mean age, 58 years ± 6 [standard deviation]) were evaluated. AI achieved an AUC of 0.93 (95% CI: 0.89, 0.96) for DM and 0.94 (95% CI: 0.91, 0.97) for DBT. For DM, AI achieved noninferior sensitivity as a single (58.4%; 66 of 113; 95% CI: 49.2, 67.1) or double (67.3%; 76 of 113; 95% CI: 58.2, 75.2) reader, with a reduction in recall rate (P < .001) of up to 2% (95% CI: -2.4, -1.6). For DBT, AI achieved noninferior sensitivity as a single (77%; 87 of 113; 95% CI: 68.4, 83.8) or double (81.4%; 92 of 113; 95% CI: 73.3, 87.5) reader, but with a higher recall rate (P < .001) of up to 12.3% (95% CI: 11.7, 12.9). Conclusion Artificial intelligence could replace radiologists' readings in breast screening, achieving a noninferior sensitivity, with a lower recall rate for digital mammography but a higher recall rate for digital breast tomosynthesis. Published under a CC BY 4.0 license. See also the editorial by Fuchsjäger and Adelsmayr in this issue.
Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) is an emerging sentiment analysis task.Most of the existing studies focus on devising a new tagging scheme that enables the model to extract the sentiment triplets in an end-to-end fashion.However, these methods ignore the relations between words for ASTE task.In this paper, we propose an Enhanced Multi-Channel Graph Convolutional Network model (EMC-GCN) to fully utilize the relations between words.Specifically, we first define ten types of relations for ASTE task, and then adopt a biaffine attention module to embed these relations as an adjacent tensor between words in a sentence.After that, our EMC-GCN transforms the sentence into a multi-channel graph by treating words and the relation adjacent tensor as nodes and edges, respectively.Thus, relationaware node representations can be learnt.Furthermore, we consider diverse linguistic features to enhance our EMC-GCN model.Finally, we design an effective refining strategy on EMC-GCN for word-pair representation refinement, which considers the implicit results of aspect and opinion extraction when determining whether word pairs match or not.Extensive experimental results on the benchmark datasets demonstrate that the effectiveness and robustness of our proposed model, which outperforms state-of-the-art methods significantly.
Real-Time Intermediate Flow Estimation for Video Frame Interpolation

Real-time video frame interpolation (VFI) is very useful in video processing, media players, and display devices. We propose RIFE, a Real-time Intermediate Flow Estimation algorithm for VFI. To realize a high-quality flow-based VFI method, RIFE uses a neural network named IFNet that can estimate the intermediate flows end-to-end with much faster speed. A privileged distillation scheme is designed for stable IFNet training and improve the overall performance. RIFE does not rely on pre-trained optical flow models and can support arbitrary-timestep frame interpolation with the temporal encoding input. Experiments demonstrate that RIFE achieves state-of-the-art performance on several public benchmarks. Compared with the popular SuperSlomo and DAIN methods, RIFE is 4–27 times faster and produces better results. Furthermore, RIFE can be extended to wider applications thanks to temporal encoding. https://github.com/megvii-research/ECCV2022-RIFE
A Hypothesis for the Use of Cryptocrystalline Raw Materials Among Paleoindian Groups of North America

The widespread existence of distinctive Paleoindian chipped stone technologies has been recognized in North American archaeology for decades. Great attention has been paid to the striking homogeneity of tool forms within these technologies over time and space and the high standards of craftsmanship involved in the production of the characteristic bifacial and unifacial tools. The idea of widespread geographic movement among Paleoindian populations in the New World has been a commonly accepted idea for some time in American archaeology. The combined work of Crabtree and many of the people he has trained or influenced has indicated repeatedly that the significant variation in flaking properties exists within rocks that fracture conchoidally. Those materials easiest to shape especially for flat surfaces, even edges, and acute angles are those such as quartz crystal, chalcedony, jasper, agate, chert, homstone, and some of the fine-grained igneous rocks. These rocks all share in common the properties of brittleness and isotropism, both necessary ingredients for controlled reduction.
Multi-Objective VM Consolidation Based on Thresholds and Ant Colony System in Cloud Computing

With the large-scale deployment of cloud datacenters, high energy consumption and serious service level agreement (SLA) violations in datacenters have become an increasingly urgent problem to be addressed. Implementing an effective virtual machine (VM) consolidation methods is of great significance to reduce energy consumption and SLA violations. The VM consolidation problem is a well-known NP-hard problem. Meanwhile, efficient VM consolidation should consider multiple factors synthetically, including quality of service, energy consumption, and migration overhead, which is a multi-objective optimization problem. To solve the problem above, we propose a new multi-objective VM consolidation approach based on double thresholds and ant colony system (ACS). The proposed approach leverages double thresholds of CPU utilization to identify the host load status, VM consolidation is triggered when the host is overloaded or underloaded. During consolidation, the approach selects migration VMs and destination hosts simultaneously based on ACS, utilizing diverse selection policies according to the host load status. The extensive experiment is conducted to compare our proposed approach with the state-of-art VM consolidation approaches. The experimental results demonstrate that the proposed approach remarkably reduces energy consumption and optimizes SLA violation rates thus achieving better comprehensive performance.
Prediction of heavy metals in soils of an arid area based on multi-spectral data

With the rapid and extensive development of industry and agriculture, the soil environment inevitably becomes contaminated with heavy metals, thus creating adverse environmental conditions for flora and fauna. The traditional methods for combining field sampling with laboratory analysis of soil heavy metals are limited not only because they are time-consuming and expensive, but also because they are unable to obtain adequate information about the spatial distribution characteristics of heavy metals in soil over a large area. Three hundred and ninety-four soil samples (Gobi and farmland) were collected in an arid area in Jiuquan in Northwest China and analyzed for elements concentrations. Based on these measured concentrations, as well as rapid and environmentally friendly remote sensing (multi-spectral data), stepwise multiple linear regression (SMLR) and partial least-squares regression (PLS) were combined to predict concentrations and distributions of heavy metals in the soils of the study area. Furthermore, laboratory data were used to assess the accuracy of the prediction results. Obtained results suggest that the SMLR and PLS models were able to predict the metals contents in the study area. The concentrations of Cr, Ni, V and Zn could be predicted by two regression models, while those of Cu and Mn were predicted more accurately when they were attached to the SMLR model. The spatial distribution of heavy metals derived from the two models is consistent with measured values, indicating that it is reasonable to predict the concentrations of heavy metals in the soil of the study area using the multi-spectral data.
Performance and Reading Time of Automated Breast US with or without Computer-aided Detection

BackgroundComputer-aided detection (CAD) systems may be used to help radiologists interpret automated breast (AB) US images. However, the optimal use of CAD with AB US has, to the knowledge of the authors, not been determined.PurposeTo compare the performance and reading time of different readers by using AB US CAD system to detect breast cancer in different reading modes.Materials and MethodsIn this retrospective study, 1485 AB US images (282 with malignant lesions, 695 with benign lesions, and 508 healthy) in 1452 women (mean age, 43.7 years; age range, 19-82 years) including 529 (36.4%) women who were asymptomatic were collected between 2016 and 2017. A CAD system was used to interpret the images. Three novice readers with 1-3 years of US experience and three experienced readers with 5-10 years of US experience were assigned to read AB US images without CAD, at a second reading (after the reader completed a full unaided interpretation), and at concurrent reading (use of CAD at the start of the assessment). Diagnostic performances and reading times were compared by using analysis of variance.ResultsFor all readers, the mean area under the receiver operating characteristic curve improved from 0.88 (95% confidence interval [CI]: 0.85, 0.91) at without-CAD mode to 0.91 (95% CI: 0.89, 0.92; P < .001) at the second-reading mode and 0.90 (95% CI: 0.89, 0.92; P = .002) at the concurrent-reading mode. The mean sensitivity of novice readers in women who were asymptomatic improved from 67% (95% CI: 63%, 74%) at without-CAD mode to 88% (95% CI: 84%, 89%) at both the second-reading mode and the concurrent-reading mode (P = .003). Compared with the without-CAD and second-reading modes, the mean reading time per volume of concurrent reading was 16 seconds (95% CI: 11, 22; P < .001) and 27 seconds (95% CI: 21, 32; P < .001) shorter, respectively.ConclusionComputer-aided detection (CAD) was helpful for novice readers to improve cancer detection at automated breast US in women who were asymptomatic. CAD was more efficient when used concurrently for all readers.© RSNA, 2019Online supplemental material is available for this article.See also the editorial by Slanetz in this issue.
Tight analyses for non-smooth stochastic gradient descent

Consider the problem of minimizing functions that are Lipschitz and strongly convex, but not necessarily differentiable. We prove that after $T$ steps of stochastic gradient descent, the error of the final iterate is $O(\log(T)/T)$ with high probability. We also construct a function from this class for which the error of the final iterate of deterministic gradient descent is $\Omega(\log(T)/T)$. This shows that the upper bound is tight and that, in this setting, the last iterate of stochastic gradient descent has the same general error rate (with high probability) as deterministic gradient descent. This resolves both open questions posed by Shamir (2012). An intermediate step of our analysis proves that the suffix averaging method achieves error $O(1/T)$ with high probability, which is optimal (for any first-order optimization method). This improves results of Rakhlin (2012) and Hazan and Kale (2014), both of which achieved error $O(1/T)$, but only in expectation, and achieved a high probability error bound of $O(\log \log(T)/T)$, which is suboptimal. We prove analogous results for functions that are Lipschitz and convex, but not necessarily strongly convex or differentiable. After $T$ steps of stochastic gradient descent, the error of the final iterate is $O(\log(T)/\sqrt{T})$ with high probability, and there exists a function for which the error of the final iterate of deterministic gradient descent is $\Omega(\log(T)/\sqrt{T})$.
An IoMT-based geriatric care management system for achieving smart health in nursing homes

Purpose The purpose of this paper is to develop an Internet of medical things (IoMT)-based geriatric care management system (I-GCMS), integrating IoMT and case-based reasoning (CBR) in order to deal with the global concerns of the increasing demand for elderly care service in nursing homes. Design/methodology/approach The I-GCMS is developed under the IoMT environment to collect real-time biometric data for total health monitoring. When the health of an elderly deteriorates, the CBR is used to revise and generate the customized care plan, and hence support and improve the geriatric care management (GCM) service in nursing homes. Findings A case study is conducted in a nursing home in Taiwan to evaluate the performance of the I-GCMS. Under the IoMT environment, the time saving in executing total health monitoring helps improve the daily operation effectiveness and efficiency. In addition, the proposed system helps leverage a proactive approach in modifying the content of a care plan in response to the change of health status of elderly. Originality/value Considering the needs for demanding and accurate healthcare services, this is the first time that IoMT and CBR technologies have been integrated in the field of GCM. This paper illustrates how to seamlessly connect various sensors to capture real-time biometric data to the I-GCMS platform for responsively supporting decision making in the care plan modification processes. With the aid of I-GCMS, the efficiency in executing the daily routine processes and the quality of healthcare services can be improved.
Flexible gamma calculation algorithm based on probability distribution function in digital fringe projection system

The gamma effect of phase-measuring profilometry systems yields nonlinear errors, which will substantially reduce the 3D shape measurement accuracy. Here, a robust and flexible gamma correction method based on the probability distribution function (PDF) of the wrapped phase is presented. First, a series of PDF curves are generated from the simulated wrapped phase distributions with different gamma values. Second, an experimental PDF curve will be produced after obtaining the wrapped phase from the captured three-step phase-shift fringe images. Then, a correlation procedure will be used to find the most similar PDF curve from the simulated PDF curves, and the gamma value of the matched PDF curve is that of the current system. Note that the gamma value detected by this method will be smaller than the true value due to the defocusing effect of the projection system with a large aperture. Therefore, an improved PDF-based algorithm, which projects two sets of three-step phase-shifting sinusoidal fringe patterns with different pre-coded gamma values and produces two PDF curves, is also added. Then after one more correlation procedure, a more accurate systematic gamma value could be calculated. It does not need large-step phase-shift images and 2×3 fringe images are quite enough. The experimental results show that the technique is very fast, easy to use and quite accurate.
A Vision-Based System for Monitoring Elderly People at Home

Assisted living technologies can be of great importance for taking care of elderly people and helping them to live independently. In this work, we propose a monitoring system designed to be as unobtrusive as possible, by exploiting computer vision techniques and visual sensors such as RGB cameras. We perform a thorough analysis of existing video datasets for action recognition, and show that no single dataset can be considered adequate in terms of classes or cardinality. We subsequently curate a taxonomy of human actions, derived from different sources in the literature, and provide the scientific community with considerations about the mutual exclusivity and commonalities of said actions. This leads us to collecting and publishing an aggregated dataset, called ALMOND (Assisted Living MONitoring Dataset), which we use as the training set for a vision-based monitoring approach.We rigorously evaluate our solution in terms of recognition accuracy using different state-of-the-art architectures, eventually reaching 97% on inference of basic poses, 83% on alerting situations, and 71% on daily life actions. We also provide a general methodology to estimate the maximum allowed distance between camera and monitored subject. Finally, we integrate the defined actions and the trained model into a computer-vision-based application, specifically designed for the objective of monitoring elderly people at their homes.
Contextual Embeddings: When Are They Worth It?

We study the settings for which deep contextual embeddings (e.g., BERT) give large improvements in performance relative to classic pretrained embeddings (e.g., GloVe), and an even simpler baseline-random word embeddings-focusing on the impact of the training set size and the linguistic properties of the task.Surprisingly, we find that both of these simpler baselines can match contextual embeddings on industry-scale data, and often perform within 5 to 10% accuracy (absolute) on benchmark tasks.Furthermore, we identify properties of data for which contextual embeddings give particularly large gains: language containing complex structure, ambiguous word usage, and words unseen in training.
Connecting Embeddings for Knowledge Graph Entity Typing

Knowledge graph (KG) entity typing aims at inferring possible missing entity type instances in KG, which is a very significant but still under-explored subtask of knowledge graph completion.In this paper, we propose a novel approach for KG entity typing which is trained by jointly utilizing local typing knowledge from existing entity type assertions and global triple knowledge from KGs.Specifically, we present two distinct knowledge-driven effective mechanisms of entity type inference.Accordingly, we build two novel embedding models to realize the mechanisms.Afterward, a joint model with them is used to infer missing entity type instances, which favors inferences that agree with both entity type instances and triple knowledge in KGs.Experimental results on two real-world datasets (Freebase and YAGO) demonstrate the effectiveness of our proposed mechanisms and models for improving KG entity typing.
Industrial Cyber-Physical Systems-Based Cloud IoT Edge for Federated Heterogeneous Distillation

Deep convoloutional networks have been widely deployed in modern cyber-physical systems performing different visual classification tasks. As the fog and edge devices have different computing capacity and perform different subtasks, models trained for one device may not be deployable on another. Knowledge distillation technique can effectively compress well trained convolutional neural networks into light-weight models suitable to different devices. However, due to privacy issue and transmission cost, manually annotated data for training the deep learning models are usually gradually collected and archived in different sites. Simply training a model on powerful cloud servers and compressing them for particular edge devices failed to use the distributed data stored at different sites. This offline training approach is also inefficient to deal with new data collected from the edge devices. To overcome these obstacles, in this article, we propose the heterogeneous brain storming (HBS) method for object recognition tasks in real-world Internet of Things (IoT) scenarios. Our method enables flexible bidirectional federated learning of heterogeneous models trained on distributed datasets with a new “brain storming” mechanism and optimizable temperature parameters. In our comparison experiments, this HBS method outperformed multiple state-of-the-art single-model compression methods, as well as the newest multinetwork knowledge distillation methods with both homogeneous and heterogeneous classifiers. The ablation experiment results proved that the trainable temperature parameter into the conventional knowledge distillation loss can effectively ease the learning process of student networks in different methods. To the best of authors' knowledge, this is the first IoT-oriented method that allows asynchronous bidirectional heterogeneous knowledge distillation in deep networks.