Reviewerly/exHarmony huggingface dataset:
Safe Trajectory Generation for Complex Urban Environments Using Spatio-Temporal Semantic Corridor

Planning safe trajectories for autonomous vehicles in complex urban environments is challenging since there are numerous semantic elements (such as dynamic agents, traffic lights, and speed limits) to consider. These semantic elements may have different mathematical descriptions, such as obstacle, constraint, and cost. It is non-trivial to tune the effects from different combinations of semantic elements for a stable and generalizable behavior. In this letter, we propose a novel unified spatio-temporal semantic corridor (SSC) structure, which provides a level of abstraction for different types of semantic elements. The SSC consists of a series of mutually connected collision-free cubes with dynamical constraints posed by the semantic elements in the spatio-temporal domain. The trajectory generation problem then boils down to a general quadratic programming formulation. Thanks to the unified SSC representation, our framework can generalize to any combination of semantic elements. Moreover, our formulation provides a theoretical guarantee that the entire trajectory is safe and constraint-satisfied, by using the convex hull and hodograph properties of piecewise Bézier curve parameterization. We also release the code of our method to accommodate benchmarking.
Orogenic Gold in Transpression and Transtension Zones: Field and Remote Sensing Studies of the Barramiya–Mueilha Sector, Egypt

Multi-sensor satellite imagery data promote fast, cost-efficient regional geological mapping that constantly forms a criterion for successful gold exploration programs in harsh and inaccessible regions. The Barramiya–Mueilha sector in the Central Eastern Desert of Egypt contains several occurrences of shear/fault-associated gold-bearing quartz veins with consistently simple mineralogy and narrow hydrothermal alteration haloes. Gold-quartz veins and zones of carbonate alteration and listvenitization are widespread along the ENE–WSW Barramiya–Um Salatit and Dungash–Mueilha shear belts. These belts are characterized by heterogeneous shear fabrics and asymmetrical or overturned folds. Sentinel-1, Phased Array type L-band Synthetic Aperture Radar (PALSAR), Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER), and Sentinel-2 are used herein to explicate the regional structural control of gold mineralization in the Barramiya–Mueilha sector. Feature-oriented Principal Components Selection (FPCS) applied to polarized backscatter ratio images of Sentinel-1 and PALSAR datasets show appreciable capability in tracing along the strike of regional structures and identification of potential dilation loci. The principal component analysis (PCA), band combination and band ratioing techniques are applied to the multispectral ASTER and Sentinel-2 datasets for lithological and hydrothermal alteration mapping. Ophiolites, island arc rocks, and Fe-oxides/hydroxides (ferrugination) and carbonate alteration zones are discriminated by using the PCA technique. Results of the band ratioing technique showed gossan, carbonate, and hydroxyl mineral assemblages in ductile shear zones, whereas irregular ferrugination zones are locally identified in the brittle shear zones. Gold occurrences are confined to major zones of fold superimposition and transpression along flexural planes in the foliated ophiolite-island arc belts. In the granitoid-gabbroid terranes, gold-quartz veins are rather controlled by fault and brittle shear zones. The uneven distribution of gold occurrences coupled with the variable recrystallization of the auriferous quartz veins suggests multistage gold mineralization in the area. Analysis of the host structures assessed by the remote sensing results denotes vein formation spanning the time–space from early transpression to late orogen collapse during the protracted tectonic evolution of the belt.
Hybrid neural recommendation with joint deep representation learning of ratings and reviews

Rating-based methods (e.g., collaborative filtering) in recommendation can explicitly model users and items from their rating patterns, nevertheless suffer from the natural data sparsity problem. In other hand, user-generated reviews can provide rich semantic information of user preference and item features, and can alleviate the sparsity problems of rating data. In fact, ratings and reviews are complementary and can be viewed as two different sides of users and items, hence fusing rating patterns and text reviews effectively has the potential to learn more accurate representations of users and items for recommendation. In this paper, we propose a hybrid neural recommendation model to learn the deep representations for users and items from both ratings and reviews. Our model contains three major components, i.e., a rating-based encoder to learn deep and explicit features from rating patterns of users and items, a review-based encoder to model users and items from text reviews, and the prediction module for recommendation according to the rating- and review-based representations of users and items. In addition, considering that different reviews have different informativeness for modelling users and items, we introduce a novel review-level attention mechanism incorporating with rating-based representation as query vector to select useful reviews. We conduct extensive experiments on several benchmark datasets and the experimental results demonstrate that our model can outperform the existing competitive baseline methods in recommendations.
Video Relationship Reasoning Using Gated Spatio-Temporal Energy Graph

Visual relationship reasoning is a crucial yet challenging task for understanding rich interactions across visual concepts. For example, a relationship {man, open, door} involves a complex relation {open} between concrete entities {man, door}. While much of the existing work has studied this problem in the context of still images, understanding visual relationships in videos has received limited attention. Due to their temporal nature, videos enable us to model and reason about a more comprehensive set of visual relationships, such as those requiring multiple (temporal) observations (e.g., {man, lift up, box} vs. {man, put down, box}), as well as relationships that are often correlated through time (e.g., {woman, pay, money} followed by {woman, buy, coffee}). In this paper, we construct a Conditional Random Field on a fully-connected spatiotemporal graph that exploits the statistical dependency between relational entities spatially and temporally. We introduce a novel gated energy function parametrization that learns adaptive relations conditioned on visual observations. Our model optimization is computationally efficient, and its space computation complexity is significantly amortized through our proposed parameterization. Experimental results on benchmark video datasets (ImageNet Video and Charades) demonstrate state-of-the-art performance across three standard relationship reasoning tasks: Detection, Tagging, and Recognition.
Improving BERT-Based Text Classification With Auxiliary Sentence and Domain Knowledge

General language model BERT pre-trained on cross-domain text corpus, BookCorpus and Wikipedia, achieves excellent performance on a couple of natural language processing tasks through the way of fine-tuning in the downstream tasks. But it still lacks of task-specific knowledge and domain-related knowledge for further improving the performance of BERT model and more detailed fine-tuning strategy analyses are necessary. To address these problem, a BERT-based text classification model BERT4TC is proposed via constructing auxiliary sentence to turn the classification task into a binary sentence-pair one, aiming to address the limited training data problem and task-awareness problem. The architecture and implementation details of BERT4TC are also presented, as well as a post-training approach for addressing the domain challenge of BERT. Finally, extensive experiments are conducted on seven public widely-studied datasets for analyzing the fine-tuning strategies from the perspectives of learning rate, sequence length and hidden state vector selection. After that, BERT4TC models with different auxiliary sentences and post-training objectives are compared and analyzed in depth. The experiment results show that BERT4TC with suitable auxiliary sentence significantly outperforms both typical feature-based methods and fine-tuning methods, and achieves new state-of-the-art performance on multi-class classification datasets. For binary sentiment classification datasets, our BERT4TC post-trained with suitable domain-related corpus also achieves better results compared with original BERT model.
Multidimensional entanglement transport through single-mode fiber

The global quantum network requires the distribution of entangled states over long distances, with substantial advances already demonstrated using polarization. While Hilbert spaces with higher dimensionality, e.g., spatial modes of light, allow higher information capacity per photon, such spatial mode entanglement transport requires custom multimode fiber and is limited by decoherence-induced mode coupling. Here, we circumvent this by transporting multidimensional entangled states down conventional single-mode fiber (SMF). By entangling the spin-orbit degrees of freedom of a biphoton pair, passing the polarization (spin) photon down the SMF while accessing multiple orbital angular momentum (orbital) subspaces with the other, we realize multidimensional entanglement transport. We show high-fidelity hybrid entanglement preservation down 250 m SMF across multiple 2 × 2 dimensions, confirmed by quantum state tomography, Bell violation measures, and a quantum eraser scheme. This work offers an alternative approach to spatial mode entanglement transport that facilitates deployment in legacy networks across conventional fiber.
Breast cancer detection using synthetic mammograms from generative adversarial networks in convolutional neural networks

The convolutional neural network (CNN) is a promising technique to detect breast cancer based on mammograms. Training the CNN from scratch, however, requires a large amount of labeled data. Such a requirement usually is infeasible for some kinds of medical image data such as mammographic tumor images. Because improvement of the performance of a CNN classifier requires more training data, the creation of new training images, image augmentation, is one solution to this problem. We applied the generative adversarial network (GAN) to generate synthetic mammographic images from the digital database for screening mammography (DDSM). From the DDSM, we cropped two sets of regions of interest (ROIs) from the images: normal and abnormal (cancer/tumor). Those ROIs were used to train the GAN, and the GAN then generated synthetic images. For comparison with the affine transformation augmentation methods, such as rotation, shifting, scaling, etc., we used six groups of ROIs [three simple groups: affine augmented, GAN synthetic, real (original), and three mixture groups of any two of the three simple groups] for each to train a CNN classifier from scratch. And, we used real ROIs that were not used in training to validate classification outcomes. Our results show that, to classify the normal ROIs and abnormal ROIs from DDSM, adding GAN-generated ROIs in the training data can help the classifier prevent overfitting, and on validation accuracy, the GAN performs about 3.6% better than affine transformations for image augmentation. Therefore, GAN could be an ideal augmentation approach. The images augmented by GAN or affine transformation cannot substitute for real images to train CNN classifiers because the absence of real images in the training set will cause over-fitting.
Speech Recognition with Augmented Synthesized Speech

Recent success of the Tacotron speech synthesis architecture and its variants in producing natural sounding multi-speaker synthesized speech has raised the exciting possibility of replacing expensive, manually transcribed, domain-specific, human speech that is used to train speech recognizers. The multi-speaker speech synthesis architecture can learn latent embedding spaces of prosody, speaker and style variations derived from input acoustic representations thereby allowing for manipulation of the synthesized speech. In this paper, we evaluate the feasibility of enhancing speech recognition performance using speech synthesis using two corpora from different domains. We explore algorithms to provide the necessary acoustic and lexical diversity needed for robust speech recognition. Finally, we demonstrate the feasibility of this approach as a data augmentation strategy for domain-transfer. We find that improvements to speech recognition performance is achievable by augmenting training data with synthesized material. However, there remains a substantial gap in performance between recognizers trained on human speech those trained on synthesized speech.
A sustainable production capability evaluation mechanism based on blockchain, LSTM, analytic hierarchy process for supply chain network

Due to the rapid development of information technology, supply chain network is evolving, which involves a higher level of interdependence between organisations. Conventional production capability evaluation relies on centralised approaches with limited sharing of performance and evaluation data. Besides, traditional evaluation methods are mainly based on subjective manual operation using limited data. In this paper, we propose a production capability evaluation system by incorporating Internet of Things (IoT), machine learning and blockchain technology for supply chain network. It contributes to achieving real-time data collection and automated enterprise production capability evaluation mechanism. Besides, blockchain technology is adopted to enable open and decentralised data storage and sharing, provide fair and automatic trading of data. The proposed system is evaluated through a simulation experiment. It demonstrated how to utilise the proposed system to choose suitable upstream enterprises. The successful development of the system could help to enhance production efficiency, reduce risk and provide a reasonable and more sustainable production management in supply chain network.
Weighted Nonlocal Low-Rank Tensor Decomposition Method for Sparse Unmixing of Hyperspectral Images

The low spatial resolution of hyperspectral images leads to the coexistence of multiple ground objects in a single pixel (called mixed pixels). A large number of mixed pixels in a hyperspectral image hinders the subsequent analysis and application of the image. In order to solve this problem, a novel sparse unmixing method, which considers highly similar patches in nonlocal regions of a hyperspectral image, is proposed in this article. This method exploits spectral correlation by using collaborative sparsity regularization and spatial information by employing total variation and weighted nonlocal low-rank tensor regularization. To effectively utilize the tensor decomposition, nonlocal similar patches are first grouped together. Then, these nonlocal patches are stacked to form a patch group tensor. Finally, weighted low-rank tensor regularization is enforced to constrain the patch group to obtain an estimated low-rank abundance image. Experiments on simulated and real hyperspectral datasets validated the superiority of the proposed method in better maintaining fine details and obtaining better unmixing results.
Attention-Based Dropout Layer for Weakly Supervised Single Object Localization and Semantic Segmentation

Both weakly supervised single object localization and semantic segmentation techniques learn an object's location using only image-level labels. However, these techniques are limited to cover only the most discriminative part of the object and not the entire object. To address this problem, we propose an attention-based dropout layer, which utilizes the attention mechanism to locate the entire object efficiently. To achieve this, we devise two key components, 1) hiding the most discriminative part from the model to capture the entire object, and 2) highlighting the informative region to improve the classification power of the model. These allow the classifier to be maintained with a reasonable accuracy while the entire object is covered. Through extensive experiments, we demonstrate that the proposed method effectively improves the weakly supervised single object localization accuracy, thereby achieving a new state-of-the-art localization accuracy on the CUB-200-2011 and a comparable accuracy existing state-of-the-arts on the ImageNet-1k. The proposed method is also effective in improving the weakly supervised semantic segmentation performance on the Pascal VOC and MS COCO. Furthermore, the proposed method is more efficient than existing techniques in terms of parameter and computation overheads. Additionally, the proposed method can be easily applied in various backbone networks.
Automatically Neutralizing Subjective Bias in Text

Texts like news, encyclopedias, and some social media strive for objectivity. Yet bias in the form of inappropriate subjectivity — introducing attitudes via framing, presupposing truth, and casting doubt — remains ubiquitous. This kind of bias erodes our collective trust and fuels social conflict. To address this issue, we introduce a novel testbed for natural language generation: automatically bringing inappropriately subjective text into a neutral point of view (“neutralizing” biased text). We also offer the first parallel corpus of biased language. The corpus contains 180,000 sentence pairs and originates from Wikipedia edits that removed various framings, presuppositions, and attitudes from biased sentences. Last, we propose two strong encoder-decoder baselines for the task. A straightforward yet opaque concurrent system uses a BERT encoder to identify subjective words as part of the generation process. An interpretable and controllable modular algorithm separates these steps, using (1) a BERT-based classifier to identify problematic words and (2) a novel join embedding through which the classifier can edit the hidden states of the encoder. Large-scale human evaluation across four domains (encyclopedias, news headlines, books, and political speeches) suggests that these algorithms are a first step towards the automatic identification and reduction of bias.
Phishing Scam Detection on Ethereum: Towards Financial Security for Blockchain Ecosystem

In recent years, blockchain technology has created a new cryptocurrency world and has attracted a lot of attention. It also is rampant with various scams. For example, phishing scams have grabbed a lot of money and has become an important threat to users' financial security in the blockchain ecosystem. To help deal with this issue, this paper proposes a systematic approach to detect phishing accounts based on blockchain transactions and take Ethereum as an example to verify its effectiveness. Specifically, we propose a graph-based cascade feature extraction method based on transaction records and a lightGBM-based Dual-sampling Ensemble algorithm to build the identification model. Extensive experiments show that the proposed algorithm can effectively identify phishing scams.
Online Learning Platforms: Reconstructing Modern Higher Education

Online learning platforms play an important role in modern education. However, they may not be sufficiently represented in educational institutions. In this regard, the research objectives are set, namely: organize a comparative analysis of several popular distance learning platforms (Moodle, Open edX and NEO LMS) in accordance with the criteria (system features, content support, content creation, user management, reporting system; conduct consultation with university teachers, during which they will highlight the benefits of distance education systems from the point of view of the teacher; and test students, who use the online platform for learning in order to clarify its impact on academic performance. While consulting with 40 teachers of Russian and Chinese universities (Moscow State University, Higher School of Economics, Peking University, Tsinghua University), the following advantages of distance platforms have been highlighted: greater freedom of access, lower education prices, the possibility of dividing the content of the e-course into modules, flexibility of education, the ability to keep up with modern pace of life, and the ability to define criteria for assessing knowledge. According to the results of testing, after working with the Moodle platform, students (300 people in total) with different academic performance have improved their results. The most significant improvement has occurred among students with “unsatisfactory” grades; more than 50% have improved their results. Analysis of distance learning systems, testing of students and consultation with teachers allow saying that such platforms help to make education more accessible and convenient. Besides, information technologies are introduced in the educational process. That is why it is important to implement such platforms in higher education.
Federated learning: a collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets

Abstract: Despite the overall success of using artificial intelligence (AI) to assist radiologists in performing computer-aided patient diagnosis, it remains challenging to build good models with small datasets at individual sites. Because many medical images do not come with proper labelling for training, this requires radiologists to perform strenuous labelling work and to prepare the dataset for training. Placing such demands on radiologists is unsustainable, given the ever-increasing number of medical images taken each year. We propose an alternative solution using a relatively new learning framework. This framework, called federated learning, allows individual sites to train a global model in a collaborative effort. Federated learning involves aggregating training results from multiple sites to create a global model without directly sharing datasets. This ensures that patient privacy is maintained across sites. Furthermore, the added supervision obtained from the results of partnering sites improves the global model’s overall detection abilities. This alleviates the issue of insufficient supervision when training AI models with small datasets. Lastly, we also address the major challenges of adopting federated learning.
Inducing Target-Specific Latent Structures for Aspect Sentiment Classification

Aspect-level sentiment analysis aims to recognize the sentiment polarity of an aspect or a target in a comment.Recently, graph convolutional networks based on linguistic dependency trees have been studied for this task.However, the dependency parsing accuracy of commercial product comments or tweets might be unsatisfactory.To tackle this problem, we associate linguistic dependency trees with automatically induced aspectspecific graphs.We propose gating mechanisms to dynamically combine information from word dependency graphs and latent graphs which are learned by self-attention networks.Our model can complement supervised syntactic features with latent semantic dependencies.Experimental results on five benchmarks show the effectiveness of our proposed latent models, giving significantly better results than models without using latent graphs.