Miracle-dz/UniFAR-FTUs huggingface dataset:
Federated Chinese Word Segmentation with Global Character Associations

Chinese word segmentation (CWS) is a fundamental task for Chinese information processing, which always suffers from out-of-vocabulary word issues, especially when it is tested on data from different sources. Although one possible solution is to use more training data, in real applications, these data are stored at different locations and thus are invisible and isolated among each other owing to the privacy or legal issues (e.g., clinical reports from different hospitals). To address this issue and beneﬁt from extra data, we propose a neural model for CWS with federated learning (FL) adopted to help CWS deal with data isolation, where a mechanism of global character associations is proposed to enhance FL to learn from different data sources. Experimental results on a simulated environment with ﬁve nodes conﬁrm the effectiveness of our approach, where our approach outperforms different baselines including some well-designed FL frameworks. 1
Verify It Yourself: A Note on Activation Functions' Influence on Fast DeepFake Detection

DeepFakes are videos that include maliciously added in the postprocessing changes, quite often substituting face of a portrayed individual with a different face using neural networks. Even though the technology gained its popularity as a carrier of jokes and parodies, it raises a serious threat to ones security – via biometric impersonation or besmearing. In this paper we focus on a method that allows of detecting DeepFakes for a user without significant computational power. In particular, we enhance MesoNet (Afchar et al., 2018) by replacing the original activation functions. We achieve over 1% improvement as well as increasing the consistency of the results. Moreover, we introduced and verified a new activation function — Pish that at the cost of slight time overhead allows even higher accuracy on certain datasets.
Well-Being in Plastic Surgery: Deep Learning Reveals Patients' Evaluations

This study deals with aspect-based sentiment analysis, the correlation of extracted aspects and their sentiment polarities with metadata. There are millions of review texts on the Internet that cannot be analyzed and thus people cannot benefit from the contained information. While most research so far has focused on explicit aspects from product or service data (e.g., hotels), we extract and classify implicit and explicit aspect phrases from German-language physician review texts. We annotated aspect phrases that indicate ratings about the doctor’s practice, such as waiting time or general perceived well-being conveyed by all staff members of a practice. We also apply a sentiment polarity classifier. While we compare several traditional and transformer networks, we apply the best model, the multilingual XLM-RoBERTa, to a dedicated German-language dataset dealing with plastic surgeons. We choose plastic surgery as sample domain because it is especially sensitive with its relation to a person’s self-image and felt acceptance. In addition to standard evaluation measures such as Precision, Recall, and F1-Score, we correlate our results with metadata from physician review websites, such as a physician’s gender. We figure out several correlations and present methods for analyzing unstructured review texts to enable service improvements in healthcare.
Exploring the Role of Context in Utterance-level Emotion, Act and Intent Classification in Conversations: An Empirical Study

the user inten-tion and background. In recent years, a number of context-aware approaches have been proposed for various utterance-level dialogue understanding tasks. In this paper, we explore and quantify the role of context for different aspects of a dialogue, namely emotion, dialogue act, and intent identiﬁcation, using state-of-the-art dialogue understanding methods as baselines. Speciﬁcally, we employ various perturbations to distort the context of a given utterance and study its impact on the different tasks and baselines. This provides us with insights into the fundamental context factors that have immediate implications on different aspects of a dialogue. Such insights may inspire more effective dialogue understanding models and provide support for future text generation approaches.
Lifelong Learning of Topics and Domain-Specific Word Embeddings

Lifelong topic models mainly focus on in-domain text streams in which each chunk only contains documents from a single domain. To overcome data diversity of the in-domain corpus, most of the existing methods exploit the information from limited sources in a separate and heuristic manner. In this study, we develop a lifelong collaborative model (LCM) based on non-negative matrix factorization to accurately learn topics and domain-speciﬁc word embeddings. LCM particularly investigates: (1) developing a knowledge graph based on the semantic relationships among words in the lifelong learning process, so as to accumulate global context information discovered by topic models and local context information reﬂected by context word embeddings from previous domains, and (2) developing a subword graph based on byte pair encoding and pairwise word relationships to exploit subword information of words in the current in-domain corpus. To the best of our knowledge, we are the ﬁrst to collaboratively learn topics and word embed-dings via lifelong learning. Experiments on real-world in-domain text streams validate the effectiveness of our method.
Perceptual Active Equalization of Multi-frequency Noise

In this paper we propose a novel multi-channel active noise equalizer (ANE) when music or speech signals are present inside the same room. Our perceptual ANE (PANE) can benefit from the masking effect of the music emitted carrying out a perceptual equalization (PEQ) of the undesired ambient noise. Our PEQ strategy automatically adapts the spectral profile of the ambient noise recorded at the error microphones to the masking threshold of the audio signal recorded at that same point. We present a real-time experiment carried out in our laboratory that simulates the position of a driver in a car to test the PANE with different audio signals. The experimental results are compared with two alternative strategies: the full cancellation (FC) profile that corresponds to an active noise cancellation strategy, and the hearing threshold (HT) profile that corresponds to an ANE system whose gains mimic the human audibility threshold. Both FC and HT profiles are independent of the music presented in the room. Results show that the noise power measured at the microphones is higher for the PEQ profile, but always below the masking threshold of the music, getting almost unnoticeable. However, the emitted anti-noise power in the case of PEQ is 15 dB lower compared to HT and FC profiles for frequencies above 300 Hz. This performance leads to a reduction of noise pollution in the room and a lower power consumption of the system loudspeakers. In addition, the PEQ profile provided by the novel PANE system is a versatile approach that can reduce the perceived noise as much as the user decides, even reaching the same performance than the HT or FC profiles if needed. Therefore, the PANE system is a versatile real-time alternative to the classic active noise cancellation systems for multi-frequency noise.
Predicting Headline Effectiveness in Online News Media using Transfer Learning with BERT

The decision to read an article in online news media or social networks is often based on the headline, and thus writing effective headlines is an important but difficult task for the journalists and content creators. Even defining an effective headline is a challenge, since the objective is to avoid click-bait headlines and be sure that the article contents fulfill the expectations set by the headline. Once defined and measured, headline effectiveness can be used for content filtering or recommending articles with effective headlines. In this paper, a metric based on received clicks and reading time is proposed to classify news media content into four classes describing headline effectiveness. A deep neural network model using the Bidirectional Encoder Representations from Transformers (BERT) is employed to classify the headlines into the four classes, and its performance is compared to that of journalists. The proposed model achieves an accuracy of 59% on the four-class classification, and 72-78% on corresponding binary classification tasks. The model outperforms the journalists being almost twice as accurate on a random sample of headlines.
Topology of Conceptual Representations in Unsupervised Generative Models

In this work latent representations of image data were investigated with neural network models of generative self-learning. A convolutional autoencoder with strong redundancy reduction was used to create latent representations of images of basic geometric shapes in the process of unsupervised generative learning and the characteristics of distributions of concept regions in the latent space of models investigated. It was demonstrated that conceptual representations with good separation of latent regions can be produced with generative models of limited complexity and that characteristic types of data, or “concepts” form well-defined, continuous and connected regions in the latent space. Geometric structure of the latent representations was described in detail confirming connected and continuous topology of latent concept regions clearly associated with characteristic types of observable data, such as shape, size and contrast. The results indicate that conceptual representations created in the process of unsupervised generative learning can form a natural basis for the emergence of abstract concepts in intelligent systems.
Modeling the Influence of Verb Aspect on the Activation of Typical Event Locations with BERT

Prior studies on event knowledge in sentence comprehension have shown that the aspect of the main verb plays an important role in the processing of non-core semantic roles, such as locations: when the aspect of the main verb is imperfective, locations become more salient in the mental representation of the event and are easier for human comprehenders to process. In our study, we tested the popular language model BERT on two datasets derived from experimental studies to determine whether BERT’s predictions of prototypical event locations were also influenced by aspect. We found that, although BERT efficiently modelled the typicality of locations, it did so independently of the verb aspect. Even when the transformer was forced to focus on the verb phrase by masking the context words in the sentence, the typicality predictions were still accurate; in addition, we found aspect to have a stronger influence on the scores, with locations in the imperfective setting being associated with lower surprisal values.
TransSum: Translating Aspect and Sentiment Embeddings for Self-Supervised Opinion Summarization

In this paper, we propose a novel self-supervised opinion summarization framework TransSum, which models opinion summaries as translations operating on the low-dimensional aspect and sentiment embedding spaces. Speciﬁcally, we propose two contrastive objectives to learn the crucial aspect and sentiment embeddings of reviews, by taking advantage of the intra-and inter-group invariances that have not been considered in previous studies. Furthermore, these embeddings can be used to reduce opinion redundancy and construct highly relevant reviews-summary pairs to train a supervised multi-input opinion summarization model. Experimental results on three different domains show that TransSum outperforms several strong baselines in generating informative, relevant and low-redundant summaries, unveiling the effectiveness of our approach.
Global Attention Decoder for Chinese Spelling Error Correction

Recent progress has been made in using BERT framework for Chinese spelling error correction (CSC). However, most existing methods correct words based on local contextual information, without considering the inﬂuence of error words in sentences. Imposing attention on error contextual information could mislead and decrease the overall performance of CSC. To address this issue, we propose a G lobal A ttention D ecoder (GAD) approach for CSC. Speciﬁcally, the proposed method learns the global relationship of the potential correct input characters and the candidates of potential error characters. Rich global contextual information is obtained to alleviate the impact of the local error contextual information. In addition, a BERT with C onfusion set guided R eplacement S trategy (BERT CRS) is designed to narrow the gap between BERT and CSC. The candidates generated by BERT CRS covering the correct character with more than 99.9% probability. To demonstrate the effectiveness of our proposed framework, we test our method on three human-annotated datasets. The experimental results show that our approach outperforms all competitor models by a large margin of up to 6.2%, achieving state-of-the-art methods on all datasets.
RoBINN: Robust Bird Species Identification using Neural Network

Recent developments in machine and deep learning have made it possible to expand the realms of traditional audio pattern recognition to real-time and practical applications. This work proposes a novel framework for robust bird species identification using the neural network (RoBINN) based on their unique vocal signatures. To make the network robust and efficient, data augmentation is performed to create synthetic training samples for bird species with less available recordings. Further, inherent properties of audio signals are suitably leveraged via effective speech recognition-based feature engineering techniques to develop an end-to-end convolutional neural network (CNN). Additionally, the proposed model architecture for the CNN framework employs residual learning and attention mechanism to generate attention-aware features, which enhances the overall accuracy of birdcall identification. The proposed architecture employs an exhaustive dataset with 21375 recordings corresponding to 264 bird species. Experimental results validate the proposed bird species classification technique in terms of accuracy, F1-score, and binary cross-entropy loss.
GRASP: Graph-based Mining of Scientific Papers

Over the past two decades, academia has witnessed numerous tools and search engines which facilitate the retrieval procedure in the literature review process and aid researchers to review the literature with more ease and accuracy. These tools mostly work based on a simple textual input which supposedly encapsulates the primary keywords in the desired research areas. Such tools mainly suffer from the following shortcomings: (i) they rely on textual search queries that are expected to reflect all the desired keywords and concepts, and (ii) shallow results which makes following a paper through time via citations a cumbersome task. In this paper, we introduce GRASP, a search engine that retrieves scientific papers starting from a sub-graph query provided by the user, offering (i) a list of time papers based on the query and (ii) a graph with papers and authors as vertices and edges being cited and published-by. GRASPhas been created using a Neo4j graph database, based on DBLP and AMiner corpora provided by their API. Acting performance evaluation by asking ten computer science experts, we demonstrate how GRASPcan efficiently retrieve and rank the most related papers based on the user’s input.
Documents Representation via Generalized Coupled Tensor Chain with the Rotation Group constraint

Continuous representations of linguistic structures are an important part of modern natu-ral language processing systems. Despite the diversity, most of the existing log-multilinear embedding models are organized under vector operations. However, these operations can not precisely represent the compositionality of nat-ural language due to a lack of order-preserving properties. In this work, we focus on one of the promising alternatives based on the embedding of documents and words in the rotation group through the generalization of the coupled tensor chain decomposition to the exponential family of the probability distributions. In this model, documents and words are represented as matrices, and n-grams representations are combined from word representations by matrix multiplication. The proposed model is optimized via noise-contrastive estimation. We show empirically that capturing word order and higher-order word interactions allows our model to achieve the best results in several document classiﬁcation benchmarks.
Robust and Hybrid Crypto-watermarking Approach for 3D Multiresolution Meshes Security

Since the release of the first 3D watermarking algorithm, several approaches have grown up with a diversity of techniques used during the embedding of information into meshes. The main objective is always to secure data shared by remote users. The originality of the present work is issued from combining encryption and hybrid watermarking algorithm to secure 3D multiresolution meshes. The new crypto-watermarking system is composed of three parts: the first part is said watermark preparation and it aims to prepare data to be inserted. During this step, the logo (which refers to copyright information) is encrypted using RSA (Rivest, Shamir,Adleman) algorithm and then encoded by applying a convolutional encoder to the encrypted logo already transformed into a binary sequence. As for the second part, it is called mesh preparation and it consists on decomposing the 3D multiresolution mesh by applying wavelet transform to generate wavelet coefficient vector. Finally, the third part of our algorithm, called hybrid watermarking, occurs to insert encrypted logo and RSA keys into both multiresolution and spatial presentations of the mesh. In fact, the encrypted logo is inserted into resulting wavelet coefficients after applying the transformation to spherical coordinate system, modulation and demodulation. As for RSA key, it is inserted into the mesh resulting from the first watermarking around by modifying geometric information of vertices. Found results prove that we are able to insert a high amount of data without influencing the mesh quality. The application of the most popular attacks does not prevent a correct extraction of data already inserted which is justified by the use of the RSA to encode the watermark and the convolutional error correcting code to retrieve the corrupted information . Our algorithm is, then, robust against these attacks.
Do It Once: An Embarrassingly Simple Joint Matching Approach to Response Selection

Existing matching models for response selection adopt the independent matching ( IM ) approach. To complete a prediction, they have to perform N independent matches, where N is the number of response options. In this paper, we explore a joint matching ( JM ) approach which performs matching only once regardless of the number of options. The JM approach does not change the structure of matching component but only modiﬁes its input and output format. It also enables a cheap but effective data augmentation method. Extensive experiments on the MuTual dataset demonstrate that, even with the simplest formulation, JM outperforms IM approach by a large margin and reduces training time by over half.
A Multi-modal Visual Emotion Recognition Method to Instantiate an Ontology

Human emotion recognition from visual expressions is an important research area in computer vision and machine learning owing to its significant scientific and commercial potential. Since visual expressions can be captured from different modalities (e.g., face expressions, body posture, hands pose), multi-modal methods are becoming popular for analyzing human reactions. In contexts in which human emotion detection is performed to associate emotions to certain events or objects to support decision making or for further analysis, it is useful to keep this information in semantic repositories, which offers a wide range of possibilities for implementing smart applications. We propose a multi-modal method for human emotion recognition and an ontology-based approach to store the classification results in EMONTO, an extensible ontology to model emotions. The multi-modal method analyzes facial expressions, body gestures, and features from the body and the environment to determine an emotional state; this processes each modality with a specialized deep learning model and applying a fusion method. Our fusion method, called EmbraceNet+, consists of a branched architecture that integrates the EmbraceNet fusion method with other ones. We experimentally evaluate our multi-modal method on an adaptation of the EMOTIC dataset. Results show that our method outperforms the single-modal methods.
Multi-Lingual Question Generation with Language Agnostic Language Model

Question generation is the task of generating coherent and relevant question given context paragraph. Recently, with the development of large-scale question answering datasets such as SQuAD, the English question generation has been rapidly developed. However, for other languages such as Chinese, the available training data is limited, which hinders the development of question generation in the corresponding language. To investigate the multi-lingual question generation, in this paper, we develop a language-agnostic language model, which learns the shared representation from several languages in a single architecture. We propose an adversarial training ob-jective to encourage the model to learn both language-speciﬁc and language-independent information. We utilize abundant monolingual text to improve the multi-lingual question generation via pre-training. With the language-agnostic language model, we achieve significant improvement in multi-lingual question generation over ﬁve languages. In addition, we propose a large-scale Chinese question generation dataset containing more than 220k human-generated questions to beneﬁt the multi-lingual question generation research.
Program Protection through Software-based Hardware Abstraction

Software companies typically embed one or more secrets in their programs to protect their intellectual property (IP) investment. These secrets are most often processed in code through evaluation of point functions, where only the correct password, PIN, or registration/activation code will authorize an end-user to legally install or use a product. Man-at-the-End (MATE) attacks can break assumptions of program security to find embedded secrets because they involve legitimate software owners who have complete access to the software and its execution environment. In this research, we present a novel approach to software MATE protection that leverages gate-level hardware representation, namely software-based hardware abstraction (SBHA). As a new proposed form of virtualization for software protection, SBHA demonstrates a light overhead – especially compared to much costlier traditional virtualization transformations, while completely defeating almost all symbolic execution-based attackers that were studied. Overall, SBHA bridges the gap between hardware and software protection, paving the way for future developments.