Reviewerly/exHarmony huggingface dataset:
Repairing mappings across biomedical ontologies by probabilistic reasoning and belief revision

The ontology matching approaches identify correspondences among entities across ontologies, and the quality of ontology mappings is crucial for supporting knowledge sharing and reuse on the Semantic Web. In the annual Ontology Evaluation Alignment Initiative (OAEI) competitions, matching large and complex, real-world biomedical ontologies is one of the most challenging endeavors. As matching methods are basically heuristic, wrong mappings often exist in the generated alignments. The general framework of mapping validation collects candidate wrong mappings based on unsatisfiable concepts and adopts the removal strategy to gain the coherence of alignments w.r.t. source ontologies. Although it ensures logical coherence, such repairing does not necessarily guarantee the quality of mappings obtained, i.e., the disposed mappings can be positive and the retained ones can be wrong in terms of the domain knowledge intended in the ontologies per se. This can be demonstrated by the existence of incoherences when the UMLS Metathesaurus® has been used as the basis of reference alignments for OAEI biomedical ontology matching tasks. To address this problem, we propose a novel approach for repairing biomedical ontology mappings by probabilistic reasoning and belief revision techniques, featuring a combination of removal strategy and revision strategy. More concretely, mappings are transformed into probabilistic description logics (PDL) conditional constraints and their weights into probability intervals based on our designed rules. Then, the incoherence checking of mappings is reduced to solving a linear program with the constraints under the PDL semantics. For identified incoherent mappings, instead of simply discarding, we revise them by relaxing their probability intervals until the probabilistic coherence is reached. The evaluation on repairing OAEI biomedical alignments shows that our approach can be effective in retaining correct mappings and removing wrong ones. Moreover, we show that repair systems following the general framework of mapping validation have improved their performance when equipped with our revision module at the repair stage. Furthermore, feeding structural matchers with repaired alignments as seeds shows that the mappings generated by our approach lead to the best structural matching result compared with other repair systems. Being non-aggressive, our approach is suitable for applications like ontology-supported medical information retrieval, semantic annotation and indexing of medical articles, and matchmaking and ranking objects among multiple ontologies.
An Improved PSO-FCM Algorithm for Image Segmentation

Abstract FCM algorithm is a kind of soft clustering method widely used in image segmentation, but the algorithm is prone to fall into local minimum and sensitive to initial value. In this paper, considering the local and global optimization capabilities, an improved PSO algorithm is proposed. Image segmentation experimental results show that the improved algorithm not only can effectively avoid the local optimal value due to the manual setting of FCM initial value, but also has better accuracy and anti-noise performance than traditional FCM.
Quantum Entanglement and Teleportation Based on Silicon Photonics

Quantum entanglement and teleportation are the key resources building the backbone of quantum technologies, such quantum communications, quantum networks, and quantum computations. The quantum communication networks are based on long-range distribution of entangled photons and teleportation of photon qubit states. The resource efficient measurement-based quantum computing models relies on cluster entangled states and the teleportation of logical operations between qubit sites. Silicon photonics provides a versatile platform for quantum information processing on chip, thanks to the CMOS-compatible fabrication process, ability to integrate thousands of components, and precise manipulation of quantum states on a single chip. Here we show our recent work on quantum entanglement and quantum teleportation technology based our advanced silicon photonics platform, a significant step towards future quantum network and optical quantum computing.
Bilingual-

Latent space based GAN methods and attention based sequence to sequence models have achieved impressive results in text generation and unsupervised machine translation respectively. Leveraging the two domains, we propose an adversarial latent space based model capable of generating parallel sentences in two languages concurrently and translating bidirectionally. The bilingual generation goal is achieved by sampling from the latent space that is shared between both languages. First two denoising autoencoders are trained, with shared encoders and back-translation to enforce a shared latent state between the two languages. The decoder is shared for the two translation directions. Next, a GAN is trained to generate synthetic ‘code’ mimicking the languages’ shared latent space. This code is then fed into the decoder to generate text in either language. We perform our experiments on Europarl and Multi30k datasets, on the English-French language pair, and document our performance using both supervised and unsupervised machine translation.
MultiRec: A Multi-Relational Approach for Unique Item Recommendation in Auction Systems

In auction-based systems such as in used car centers and online auction websites, users usually bid on items, and those items get sold to their highest bidders. In these settings, every item is unique and can be sold only once, which means users’ purchase histories will be unique, and no common items will exist across them. On the other hand, items will not have any historical sales at all. Such extreme settings pose a significant challenge to the current recommender systems models that rely on historical user-item interactions. While some of those models will not be applicable altogether, such as the matrix factorization models, neighborhood models, and even the naive most-popular model, the rest will need to rely only on items’ attributes. In this paper, we address the challenges of auction-based item recommendation by proposing a simple multi-relational recommender model (MultiRec) that can seamlessly leverage user and item attributes along with auxiliary relational information such as the user’s bidding history. Experiments on one proprietary dataset from Volkswagen Financial Services used-cars center, and on a real-world publicly available eBay dataset show that the proposed model significantly outperforms multiple state-of-art models in the task of auction-based unique item recommendation.
An Arabic Tweets Sentiment Analysis Dataset (ATSAD) using Distant Supervision and Self Training

As the number of social media users increases, they express their thoughts, needs, socialise and publish their opinions reviews. For good social media sentiment analysis, good quality resources are needed, and the lack of these resources is particularly evident for languages other than English, in particular Arabic. The available Arabic resources lack of from either the size of the corpus or the quality of the annotation. In this paper, we present an Arabic Sentiment Analysis Corpus collected from Twitter, which contains 36K tweets labelled into positive and negative. We employed distant supervision and self-training approaches into the corpus to annotate it. Besides, we release an 8K tweets manually annotated as a gold standard. We evaluated the corpus intrinsically by comparing it to human classification and pre-trained sentiment analysis models, Moreover, we apply extrinsic evaluation methods exploiting sentiment analysis task and achieve an accuracy of 86%.
90P Abnormal breast detection by an improved AlexNet model

Breast cancer is the most common female cancer worldwide. Breast screening programmes have increased detection of the earliest stages; however, mammography suffers from high false positivity and is labour intensive for radiologists. Recognition of breast abnormalities, which mainly consists of mass and micro-calcification, plays a critical role in the detection of breast cancer. We used the open accessed mini-MIAS dataset. 100 abnormal and 100 healthy images were selected randomly. The abnormal class contains six sub-types: asymmetry, distortion, calcification, ill-defined mass, circumscribed mass, and spiculated mass. A preprocessing technique was carried out by (i) removing additive and multiplicative noises and (ii) removing the background and pectoral muscle regions. For the classifier, we chose to use the popular AlexNet model. In order to improve its performance, we integrated the following improvements by: (i) reducing the kernel size of the first conv layer from 11x11 to 7x7, so our network can learn subtle feature information. (ii) utilizing ELU function to replace the traditional ReLU function. (iii) optimizing essential hyperparamters, including dropout ratio and learning rate. (iv) using data augmentation (rotation, translation, scaling, and contrast-adjusting) techniques to enhance the size of training set. Some improvements are inspired from Ref. [Liu Z, et al., JFE, 2020 & Clevert DA, et al., Arxiv, 2015]. The average of 20 runs of 10-fold cross validation showed this improved AlexNet deep neural network model can achieve a sensitivity of 93.80%, a specificity of 94.10%, a precision of 94.08%, an accuracy of 93.95%, and F1 score of 93.94%. This proposed method gives better performance than several state-of-the-art approaches, inlucing [Guo Z, et al., IEEE ROMAN, 2018, & Liu F, et al., LNCS 2019]. This proposed AI method is effective in detecting abnormal breasts. In the future, we shall try to extend our AI system into multi-class diagnosis systems together with the function of lesion region prediction. We expect the callback rate of abnormal breast diagnosis can be effectively reduced.
Similarity-based constraint score for feature selection

To avoid the curse of dimensionality resulting from a large number of features, the most relevant features should be selected. Several scores involving must-link and cannot-link constraints have been proposed to estimate the relevance of features. However, these constraint scores evaluate features one by one and ignore any correlation between them. In addition, they compute distance in the high-dimensional original feature space to evaluate similarity between samples. So, they would be corrupted by the curse of dimensionality. To deal with these drawbacks, we propose a new constraint score based on a similarity matrix that is computed in the selected feature subspace and that makes it possible to evaluate the relevance of a feature subset at once. Experiments on benchmark databases demonstrate the improvement brought by the proposed constraint score in the context of both supervised and semi-supervised learnings.
Nature soundscapes

Augmented Reality (AR) has developed to be a popular and exciting technology domain, gaining notable public interest from 2009 to the present day. AR applications have traditionally focused upon paradigms that are visually led. In this paper, we document an Audio Augmented Reality (AAR) project, which considers soundscapes and how they might be transformed via the application of music and sound technologies. This work is concerned with the augmentation of nature soundscapes and explores how this may be used to enhance public understanding of the natural world. At present, we are concerned with the augmentation of spaces with biophony. Two examples of acoustic augmented reality are described: an initial pilot study to investigate the feasibility of the approach and an installation at the Timber International Forest Festival 2019. A technical description of each is provided alongside our own reflection and participant feedback, garnered from a soundwalk inspired approach to evaluation by audiences at the festival.
Comparison of Deep Learning Methods for Spoken Language Identification

In this paper, we implement and compare deep learning based spoken language identification models. We also deploy two very recent and popular speech recognition methods, namely Wav2Vec and SpecAugment, in our classifiers and test if they are also applicable to the field of language identification. Out of the models we implement, X-vector based deep feed forward network classifier obtains the highest F1-score of 0.91, where the target set consists of five languages. SpecAugment data augmentation method turns out to increase the classification accuracy when applied to the input mel-spectrograms of the CRNN architecture. Although they obtain lower classification accuracies than some of the other methods, Wav2Vec speech representations also achieve promising results.
SIMFIC: An Explainable Book Search Companion

Consider a digital library of fiction books. A user has a certain book in mind and is searching for books which are similar in writing style, sentiment and general content. Classic retrieval techniques applied in such scenarios lack the support to explain why a set of top-K items are relevant to the query. Explainable AI (XAI) is attempting to explain the working mechanism of complex models like retrieval systems, helping humans to trust systems as companions. XAI research suggests two prominent directions: either develop add-on methods to peek inside complex models or design simple and explainable models. We adopt the latter and present a simple and explainable model for fiction books called SIMFIC (similarity in fiction). We partition books into smaller portions called chunks and extract features aligned with human cognition like writing style, sentence complexity and sentiment per chunk. In a query by example setting, a relevance ranked list of books is created based on similarities between chunks. A novel reward-penalty scheme is used while accumulating the similarities to ensure a fair comparison between short and long books. We perform feature selection using global feature vectors and pose them as plausible explanations, arguing them as global key factors differentiating between relevant and non-relevant books. SIMFIC is compared with a benchmark retrieval model and evaluated by domain experts in a study. Majority of the users found SIMFIC to provide more helpful results with respect to writing style, sentiment and general content, compared to the baseline. The results are statistically significant.
Learning Face Recognition Unsupervisedly by Disentanglement and Self-Augmentation

As the growth of smart home, healthcare, and home robot applications, learning a face recognition system which is specific for a particular environment and capable of self-adapting to the temporal changes in appearance (e.g., caused by illumination or camera position) is nowadays an important topic. In this paper, given a video of a group of people, which simulates the surveillance video in a smart home environment, we propose a novel approach which unsuper- visedly learns a face recognition model based on two main components: (1) a triplet network that extracts identity-aware feature from face images for performing face recognition by clustering, and (2) an augmentation network that is conditioned on the identity-aware features and aims at synthesizing more face samples. Particularly, the training data for the triplet network is obtained by using the spatiotemporal characteristic of face samples within a video, while the augmentation network learns to disentangle a face image into identity-aware and identity-irrelevant features thus is able to generate new faces of the same identity but with variance in appearance. With taking the richer training data produced by augmentation network, the triplet network is further fine-tuned and achieves better performance in face recognition. Extensive experiments not only show the efficacy of our model in learning an environment- specific face recognition model unsupervisedly, but also verify its adaptability to various appearance changes.
Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings.

We propose unifying techniques from probabilistic databases and relational embedding models with the goal of performing complex queries on incomplete and uncertain data. We formalize a probabilistic database model with respect to which all queries are done. This allows us to leverage the rich literature of theory and algorithms from probabilistic databases for solving problems. While this formalization can be used with any relational embedding model, the lack of a well-defined joint probability distribution causes simple query problems to become provably hard. With this in mind, we introduce \TO, a relational embedding model designed to be a tractable probabilistic database, by exploiting typical embedding assumptions within the probabilistic framework. Using a principled, efficient inference algorithm that can be derived from its definition, we empirically demonstrate that \TOs is an effective and general model for these querying tasks.
Cloud Load Balancing Using Optimization Techniques

Cloud computing is an integrated phenomenon that incorporates data, applications and services in a dynamic environment and enables worldwide optimization of resources. This computing technology is scalable and elastic in nature that opens door for large amount of incoming data from different venues with high velocity. Managing such data in distributive and heterogeneous environment imposes a challenge of load balancing on the service providers. They need to allocate the incoming tasks efficiently to the computing nodes to avoid imbalanced mapping and execution of the tasks. To achieve efficient load balancing, various load balancing algorithms have been proposed, and they all focus on achieving the effective distribution of data and improve the associated measurement factors. In this paper, different load balancing algorithms have been studied and analyzed with description of their techniques and focused parameters. Then, there is a brief discussion on the existing load balancing algorithms and further compares them based on parameters like throughput, scalability, resource utilization, etc., followed by the important findings thus made.
Diversity in Neural Architecture Search

Neural architecture search (NAS) is usually divided into two phases: model search, where candidate architectures go through an early training for a small number of epochs (e.g., 20) and a search strategy is used to find one or multiple top candidates, and model tuning, where the top candidates are trained fully (e.g., for 600 epochs) and one final best architecture is chosen. The top M-best strategy (M-Best) is typically used to help find better candidates during model search. However, the top M best solutions may concentrate in narrow similar areas and do not have enough diversity. Furthermore, empirical evidence suggests that performance distribution of the models which only go through the early training does not have a strong correlation with that of the models trained fully. Therefore, many of the M best solutions may turn out to be sub-optimal simultaneously because of their similarity, which limits the ability to find true top architectures. To alleviate the problems, we define diverse M-best architectures that are both of high quality and sufficiently different from each other based on a novel graph-based architecture distance. The concept is very general and is applicable to existing architecture search methods using top M-Best. To the best of our knowledge, this is the first time that diversity is introduced into architecture search. We applied the method in the progressive neural architecture search (PNAS) algorithm (Liu et al. 2018a). Experimental results show that our diverse M-Best is indeed beneficial for finding better architectures.
Weight and Gradient Centralization in Deep Neural Networks.

Batch normalization is currently the most widely used variant of internal normalization for deep neural networks. Additional work has shown that the normalization of weights and additional conditioning as well as the normalization of gradients further improve the generalization. In this work, we combine several of these methods and thereby increase the generalization of the networks. The advantage of the newer methods compared to the batch normalization is not only increased generalization, but also that these methods only have to be applied during training and, therefore, do not influence the running time during use. Link to CUDA code this https URL
Rotated Ring, Radial and Depth Wise Separable Radial Convolutions

Simple image rotations significantly reduce the accuracy of deep neural networks. Moreover, training with all possible rotations increases the data set, which also increases the training duration. In this work, we address trainable rotation invariant convolutions as well as the construction of nets, since fully connected layers can only be rotation invariant with a one-dimensional input. On the one hand, we show that our approach is rotationally invariant for different models and on different public data sets. We also discuss the influence of purely rotational invariant features on accuracy. The rotationally adaptive convolution models presented in this work are more computationally intensive than normal convolution models. Therefore, we also present a depth wise separable approach with radial convolution. Link to CUDA code this https URL
Primary Quality Factor Estimation Of Resized Double Compressed JPEG Images

Reconstructing the image processing chain and the parameters used in each step would provide important forensic clues. Many methods have been designed to estimate the primary quality factor of double compressed JPEG images. However, it is still a challenge to address such problem in the presence of resizing. In this paper, we concentrate on this topic by theoretically analysing the Welch Power Spectral Density (PSD) of the DC coefficients histogram of the counter-resized image. According to the analysis, We find that: i) the most prominent peak of PSD not only dependents on the first quality factor, but also relates to the second one, ii) the peak location nonlinearly maps to the quality factor in the first compression. A simple yet efficient method is proposed to estimate the primary quality factor based on the nonlinear mapping and geometric fitting. Experimental results demonstrate the proposed method provides superior performances.
Accident Detection Using Time-Distributed Model in Videos

CCTV surveillance cameras are installed in the majority of roads and highways these days; therefore, it generates millions and millions of hours of data, thus captures a variety of real-world incidents. Road accidents are one of the most severe and fatal incidents, which disrupt the smooth flow of traffic as well leading to wastage of time and resources. Detection of accidents not only helps us to save the life of victims, but also helps in reducing traffic congestion. In this, we have proposed a framework for accident detection based on hierarchical recurrent neural network. The framework localizes and identifies the presence of road accidents in the captured video. The framework contains a time-distributed model which learns both the temporal and spatial features of the video, making the framework more efficient. The proposed approach is evaluated on the dataset, built by obtaining recorded road accident videos from YouTube. Results demonstrate the applicability of our approach performs, accident detection, and localization effectively.