Reviewerly/exHarmony huggingface dataset:
Graph Collaborative Reasoning

Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering. However, most of the graph-structured data in practice suffer from incompleteness, and thus link prediction becomes an important research problem. Though many models are proposed for link prediction, the following two problems are still less explored: (1) Most methods model each link independently without making use of the rich information from relevant links, and (2) existing models are mostly designed based on associative learning and do not take reasoning into consideration. With these concerns, in this paper, we propose Graph Collaborative Reasoning (GCR), which can use the neighbor link information for relational reasoning on graphs from logical reasoning perspectives. We provide a simple approach to translate a graph structure into logical expressions so that the link prediction task can be converted into a neural logic reasoning problem. We apply logical constrained neural modules to build the network architecture according to the logical expression and use backpropagation to efficiently learn the model parameters, which bridges differentiable learning and symbolic reasoning in a unified architecture. To show the effectiveness of our work, we conduct experiments on graph-related tasks such as link prediction and recommendation based on commonly used benchmark datasets, and our graph collaborative reasoning approach achieves state-of-the-art performance.
ALiSa: Acrostic Linguistic Steganography Based on BERT and Gibbs Sampling

In this letter, we propose a novel linguistic steganographic method that directly conceals a token-level secret message in a seemingly-natural steganographic text generated by the off-the-shelf BERT model equipped with Gibbs sampling. Compared with all modification based linguistic steganographic methods, the proposed method does not modify a given cover text. Instead, the proposed method utilizes the secret message to directly generate the steganographic text. Compared with mainstream generation based linguistic steganographic methods, the proposed method enables the receiver to collect the tokens of the specific positions to directly constitute the secret message, without a complex decoding process and much side information shared between the sender and the receiver. Experimental results show that the proposed method can generate fluent, highly readable steganographic texts, while enjoying pretty good anti-steganalysis ability. This work has great application potential in real-time covert communication.
Digital Citizenship and Professional Digital Competence — Swedish Subject Teacher Education in a Postdigital Era

Abstract Teacher education (TE) is not only about skills and knowledge but also about citizenship formation as student teachers are prepared for the democratic assignment of school. In a postdigital era, blurred boundaries between digital technologies and physical reality place new demands on citizenship, teacher education institutions (TEIs), and teacher educators (TEDs). This paper explores Swedish TEDs’ views of digital citizenship and the professional digital competence (PDC) required for teaching subject student teachers to teach for digital citizenship. Seven TEIs participated and 16 semi-structured interviews were conducted with TEDs teaching a Core Education Subjects module on education and democracy mandatory for all student teachers. TEDs generally believe that the digitalization of society impacts the democratic assignment and addressing this requires specific PDC. Conceptualizations of digital citizenship tend to foreground source criticism as well as ethical, safe, and sound use of digital technologies, and to some degree also (im-)material means of democratic participation. While generally believing that TE should address questions relating to digital citizenship and that TEDs have an important role in this regard, digital technologies are discussed in the module coincidentally and TEDs are unsure to what degree student teachers receive such training. Challenges include lack of time and unclear Degree Objectives. To develop TEDs’ PDC to include questions relating to digital citizenship in their teaching, support is needed through policy and continuous professional development for TEDs, including reviews of course content and program structure. Future TE research needs to explore digital citizenship in the school subject social studies.
Linear Canonical Stockwell Transform: Theory and Applications

The Stockwell transform (ST), which is a reversible method of time-frequency spectral representation, is an extension of the ideas of the wavelet transform (WT) and short-time Fourier transform (STFT). And yet, it represents the signal just in the time-frequency plane, which is unfavorable for nonstationary signals. In this paper, a new linear canonical Stockwell transform (LCST) is proposed based on the specific convolution structure in linear canonical transform (LCT) domain, which is a combination of the merits of ST and LCT to address this problem. It not only characterizes the signal in the time-linear canonical frequency plane, but more importantly, inherits the advantages of ST with a clear physical meaning. First, the theories about the continuous LCST are described at length, including its definition, basic properties and the time-LCT domain-frequency analysis. Next, the convolution theorem and cross-correlation theorem constructed in LCST domain are considered. Further, the discretization algorithm of the LCST is explored in order to realize it in the physical system. Finally, based on the proposed LCST, we study and discuss several applications of it, including time-frequency analysis and filtering of chirp signals. The rationality and validity of the work is verified out by simulations.
Artificial Intelligence-Virtual Trainer: Innovative Didactics Aimed at Personalized Training Needs

First, the benefits of AI-based training for business development are presented. Then, the theoretical literature on various stages from traditional training to AI-based training is analyzed. Finally, we analyze AI applications in the training process and managerial response. This study presents a framework for the application of artificial intelligence (AI) technology to training and managerial challenges. AI tools can be applied to the training process, including knowledge management (KM), needs analysis, training organization, and results feedback. AI-based training transforms organizations into knowledge organizations that can meet the demands of personalized training and improve learning quality. AI tools bring about a shift in the training phases of knowledge base creation, needs surveys, the organization of training, and feedback on results.
A novel phase-shifting profilometry to realize temporal phase unwrapping simultaneously with the least fringe patterns

A novel phase-shifting profilometry (PSP) to realize temporal phase unwrapping (TPU) simultaneously with the least fringe patterns is proposed. By combining a unit-frequency ramp pattern with the least three phase-shifting sinusoidal fringe patterns at a shifted-phase of π/2 respectively, three composite patterns are formed. While these three composite patterns are projected onto the measured object successively, three corresponding deformed patterns can be captured by a CCD camera. After demodulation, both the unit-frequency component and the high-frequency components characterized by phase-shifting deformed patterns can be extracted efficiently. So the wrapped phase caused by the measured object can be calculated from the high-frequency components of the deformed patterns with PSP and the fringe orders can be derived from the unit-frequency components to pixel-wise auxiliary phase unwrapping. It employs no other additional structure pattern to achieve full-field phase unwrapping. The accuracy of the measurement is guaranteed by using three phase-shifting fringe patterns, and the combination of the TPU can measure the discontinuous surfaces or multiple isolated objects simultaneously. PSP and TPU are integrated skillfully with least fringe patterns in the proposed method. The experimental results demonstrated the feasibility and validity of the proposed method.
Direct Differentiable Augmentation Search

Data augmentation has been an indispensable tool to improve the performance of deep neural networks, however the augmentation can hardly transfer among different tasks and datasets. Consequently, a recent trend is to adopt AutoML technique to learn proper augmentation policy without extensive hand-crafted tuning. In this paper, we propose an efficient differentiable search algorithm called Direct Differentiable Augmentation Search (DDAS). It exploits meta-learning with one-step gradient update and continuous relaxation to the expected training loss for efficient search. Our DDAS can achieve efficient augmentation search without relying on approximations such as Gumbel-Softmax or second order gradient approximation. To further reduce the adverse effect of improper augmentations, we organize the search space into a two level hierarchy, in which we first decide whether to apply augmentation, and then determine the specific augmentation policy. On standard image classification benchmarks, our DDAS achieves state-of-the-art performance and efficiency tradeoff while reducing the search cost dramatically, e.g. 0.15 GPU hours for CIFAR-10. In addition, we also use DDAS to search augmentation for object detection task and achieve comparable performance with AutoAugment [8], while being 1000× faster. Code will be released in https://github.com/zxcvfd13502/DDAS_code
Motion-Focused Contrastive Learning of Video Representations

Motion, as the most distinct phenomenon in a video to involve the changes over time, has been unique and critical to the development of video representation learning. In this paper, we ask the question: how important is the motion particularly for self-supervised video representation learning. To this end, we compose a duet of exploiting the motion for data augmentation and feature learning in the regime of contrastive learning. Specifically, we present a Motion-focused Contrastive Learning (MCL) method that regards such duet as the foundation. On one hand, MCL capitalizes on optical flow of each frame in a video to temporally and spatially sample the tubelets (i.e., sequences of associated frame patches across time) as data augmentations. On the other hand, MCL further aligns gradient maps of the convolutional layers to optical flow maps from spatial, temporal and spatio-temporal perspectives, in order to ground motion information in feature learning. Extensive experiments conducted on R(2+1)D backbone demonstrate the effectiveness of our MCL. On UCF101, the linear classifier trained on the representations learnt by MCL achieves 81.91% top-1 accuracy, outperforming ImageNet supervised pre-training by 6.78%. On Kinetics-400, MCL achieves 66.62% top-1 accuracy under the linear protocol.
Supervised outlier detection for classification and regression

Outlier detection, i.e., the task of detecting points that are markedly different from the data sample, is an important challenge in machine learning. When a model is built, these special points can skew the model training and result in less accurate predictions. Due to this fact, it is important to identify and remove them before building any supervised model and this is often the first step when dealing with a machine learning problem. Nowadays, there exists a very large number of outlier detector algorithms that provide good results, but their main drawbacks are their unsupervised nature together with the hyperparameters that must be properly set for obtaining good performance. In this work, a new supervised outlier estimator is proposed. This is done by pipelining an outlier detector with a following a supervised model, in such a way that the targets of the later supervise how all the hyperparameters involved in the outlier detector are optimally selected. This pipeline-based approach makes it very easy to combine different outlier detectors with different classifiers and regressors. In the experiments done, nine relevant outlier detectors have been combined with three regressors over eight regression problems as well as with two classifiers over another eight binary and multi-class classification problems. The usefulness of the proposal as an objective and automatic way to optimally determine detector hyperparameters has been proven and the effectiveness of the nine outlier detectors has also been analyzed and compared.
Decoupled Metric Network for Single-Stage Few-Shot Object Detection

Within the last few years, great efforts have been made to study few-shot learning. Although general object detection is advancing at a rapid pace, few-shot detection remains a very challenging problem. In this work, we propose a novel decoupled metric network (DMNet) for single-stage few-shot object detection. We design a decoupled representation transformation (DRT) and an image-level distance metric learning (IDML) to solve the few-shot detection problem. The DRT can eliminate the adverse effect of handcrafted prior knowledge by predicting objectness and anchor shape. Meanwhile, to alleviate the problem of representation disagreement between classification and location (i.e., translational invariance versus translational variance), the DRT adopts a decoupled manner to generate adaptive representations so that the model is easier to learn from only a few training data. As for a few-shot classification in the detection task, we design an IDML tailored to enhance the generalization ability. This module can perform metric learning for the whole visual feature, so it can be more efficient than traditional DML due to the merit of parallel inference for multiobjects. Based on the DRT and IDML, our DMNet efficiently realizes a novel paradigm for few-shot detection, called single-stage metric detection. Experiments are conducted on the PASCAL VOC dataset and the MS COCO dataset. As a result, our method achieves state-of-the-art performance in few-shot object detection. The codes are available at https://github.com/yrqs/DMNet.
FAR-Net: Fast Anchor Refining for Arbitrary-Oriented Object Detection

Compared with natural images, targets in remote-sensing images are often distributed with more flexible orientation, aspect ratio, and scale. Thus, anchor-based algorithms often employ plenty of preset anchors to encode the above-mentioned attributes in object detection tasks. However, they often suffer from the following issues: 1) significant computational burden caused by dense-sampling anchors; 2) serious background interference since many anchors only cover small parts of the actual target; and 3) feature misalignment between the targets with the preset anchors due to the absence of the most discriminant features for target extraction. Therefore, in this letter, a fast anchor refining network (FAR-Net) is advocated to address the remaining issues for arbitrary-oriented object detection in the remote-sensing field. To be specific, a rotation alignment module (RAM) and balanced regression loss function (BR-loss) are carefully designed in the FAR-Net. The RAM is capable of generating high-quality anchors based on a refinement convolution and adaptively aligning the convolutional features by complying with the anchor boxes to reduce redundant calculation. The BR-loss is designed by employing a balanced loss function to prevent misaligned anchors from causing major gradient descents, thereby achieving a more stable network training procedure. Extensive experiments on public remote-sensing datasets (HRSC2016 and UCAS-AOD) demonstrate the excellent detection performance of our algorithm in comparison with numerous existing detectors.
On Pairing-Free Blind Signature Schemes in the Algebraic Group Model

Studying the security and efficiency of blind signatures is an important goal for privacy sensitive applications. In particular, for large-scale settings (e.g., cryptocurrency tumblers), it is important for schemes to scale well with the number of users in the system. Unfortunately, all practical schemes either 1) rely on (very strong) number theoretic hardness assumptions and/or computationally expensive pairing operations over bilinear groups, or 2) support only a polylogarithmic number of concurrent (i.e., arbitrarily interleaved) signing sessions per public key. In this work, we revisit the security of two pairing-free blind signature schemes in the Algebraic Group Model (AGM) + Random Oracle Model (ROM). Concretely,
A Backdoor Attack against 3D Point Cloud Classifiers

Vulnerability of 3D point cloud (PC) classifiers has become a grave concern due to the popularity of 3D sensors in safety-critical applications. Existing adversarial attacks against 3D PC classifiers are all test-time evasion (TTE) attacks that aim to induce test-time misclassifications using knowledge of the classifier. But since the victim classifier is usually not accessible to the attacker, the threat is largely diminished in practice, as PC TTEs typically have poor transferability. Here, we propose the first backdoor attack (BA) against PC classifiers. Originally proposed for images, BAs poison the victim classifier’s training set so that the classifier learns to decide to the attacker’s target class whenever the attacker’s backdoor pattern is present in a given input sample. Significantly, BAs do not require knowledge of the victim classifier. Different from image BAs, we propose to insert a cluster of points into a PC as a robust backdoor pattern customized for 3D PCs. Such clusters are also consistent with a physical attack (i.e., with a captured object in a scene). We optimize the cluster’s location using an independently trained surrogate classifier and choose the cluster’s local geometry to evade possible PC preprocessing and PC anomaly detectors (ADs). Experimentally, our BA achieves a uniformly high success rate (≥ 87%) and shows evasiveness against state-of-the-art PC ADs. Code is available at https://github.com/zhenxianglance/PCBA.
Enzian: an open, general, CPU/FPGA platform for systems software research

Hybrid computing platforms, comprising CPU cores and FPGA logic, are increasingly used for accelerating data-intensive workloads in cloud deployments, and are a growing topic of interest in systems research. However, from a research perspective, existing hardware platforms are limited: they are often optimized for concrete, narrow use-cases and, therefore lack the flexibility needed to explore other applications and configurations.
Pano-AVQA: Grounded Audio-Visual Question Answering on 360° Videos

360° videos convey holistic views for the surroundings of a scene. It provides audio-visual cues beyond predetermined normal field of views and displays distinctive spatial relations on a sphere. However, previous benchmark tasks for panoramic videos are still limited to evaluate the semantic understanding of audio-visual relationships or spherical spatial property in surroundings. We propose a novel benchmark named Pano-AVQA as a large-scale grounded audio-visual question answering dataset on panoramic videos. Using 5.4K 360° video clips harvested online, we collect two types of novel question-answer pairs with bounding-box grounding: spherical spatial relation QAs and audio-visual relation QAs. We train several transformer-based models from Pano-AVQA, where the results suggest that our proposed spherical spatial embeddings and multimodal training objectives fairly contribute to a better semantic understanding of the panoramic surroundings on the dataset.
Leveraging Multi-source knowledge for Chinese clinical named entity recognition via relational graph convolutional network

External knowledge, such as lexicon of words in Chinese and domain knowledge graph (KG) of concepts, has been recently adopted to improve the performance of machine learning methods for named entity recognition (NER) as it can provide additional information beyond context. However, most existing studies only consider knowledge from one source (i.e., either lexicon or knowledge graph) in different ways and consider lexicon words or KG concepts independently with their boundaries. In this paper, we focus on leveraging multi-source knowledge in a unified manner where lexicon words or KG concepts are well combined with their boundaries for Chinese Clinical NER (CNER).We propose a novel method based on relational graph convolutional network (RGCN), called MKRGCN, to utilize multi-source knowledge in a unified manner for CNER. For any sentence, a relational graph based on words or concepts in each knowledge source is constructed, where lexicon words or KG concepts appearing in the sentence are linked to the containing tokens with the boundary information of the lexicon words or KG concepts. RGCN is used to model all relational graphs constructed from multi-source knowledge, and the representations of tokens from multi-source knowledge are integrated into the context representations of tokens via an attention mechanism. Based on the knowledge-enhanced representations of tokens, we deploy a conditional random field (CRF) layer for named entity label prediction. In this study, a lexicon of words and a medical knowledge graph are used as knowledge sources for Chinese CNER.Our proposed method achieves the best performance on CCKS2017 and CCKS2018 in Chinese with F1-scores of 91.88% and 89.91%, respectively, significantly outperforming existing methods. The extended experiments on NCBI-Disease and BC2GM in English also prove the effectiveness of our method when only considering one knowledge source via RGCN.The MKRGCN model can integrate knowledge from the external lexicon and knowledge graph effectively for Chinese CNER and has the potential to be applied to English NER.
Segmentation-grounded Scene Graph Generation

Scene graph generation has emerged as an important problem in computer vision. While scene graphs provide a grounded representation of objects, their locations and relations in an image, they do so only at the granularity of proposal bounding boxes. In this work, we propose the first, to our knowledge, framework for pixel-level segmentation-grounded scene graph generation. Our framework is agnostic to the underlying scene graph generation method and address the lack of segmentation annotations in target scene graph datasets (e.g., Visual Genome [24]) through transfer and multi-task learning from, and with, an auxiliary dataset (e.g., MS COCO [29]). Specifically, each target object being detected is endowed with a segmentation mask, which is expressed as a lingual-similarity weighted linear combination over categories that have annotations present in an auxiliary dataset. These inferred masks, along with a Gaussian masking mechanism which grounds the relations at a pixel-level within the image, allow for improved relation prediction. The entire framework is end-to-end trainable and is learned in a multi-task manner. Code is available at github.com/ubc-vision/segmentation-sg.