Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Rephrasing of Transcripts-based Action Items

The automated transcription of spoken language, and meetings, in particular, is becoming more widespread as automatic speech recognition systems are becoming more accurate. This trend has significantly accelerated since the outbreak of the COVID-19 pandemic, which led to a major increase in the number of online meetings. However, the transcription of spoken language has not received much attention from the NLP community compared to documents and other forms of written language. In this paper, we study a variation of the summarization problem over the transcription of spoken language: given a transcribed meeting, and an action item (i.e., a commitment or request to perform a task), our goal is to generate a coherent and self-contained rephrasing of the action item. To this end, we compiled a novel dataset of annotated meeting transcripts, including human rephrasing of action items. We use state-of-the-art supervised text generation techniques and establish a strong baseline based on BART and UniLM (two pretrained transformer models). Due to the nature of natural speech, language is often broken and incomplete and the task is shown to be harder than an analogous task over email data. Particularly, we show that the baseline models can be greatly improved once models are provided with additional information. We compare two approaches: one incorporating features extracted by coreference-resolution. Additional annotations are used to train an auxiliary model to detect the relevant context in the text. Based on the systematic human evaluation, our best models exhibit near-human-level rephrasing capability on a constrained subset of the problem. © 2021 Association for Computational Linguistics
Glue Level Estimation through Automatic Visual Inspection in PCB Manufacturing

: Nowadays, the increasing use of automatic visual inspection approaches in the manufacturing process is re-markable. The automation of production lines implies proﬁtability and product quality. Moreover, optimized human resources result in process optimization and production increase. This work addresses the problem of optimizing the glue tube replacement in Printed Circuit Boards (PCB) manufacturing, warning a human operator only just in time to replace the glue tube. We propose an approach to estimate the glue level, in the glue injection process, during PCB manufacturing. The proposed methodology is composed of three main steps: i ) Pre-Processing; ii ) Feature extraction; and iii ) Glue level estimation through machine learning. The proposed predictive model learns the relation between visual features and the glue level in the tube. Real and simulated experiments were carried out to validate the proposed approach. Results show the obtained Root Mean Square Error (RMSE) measure of 0.88, using Random Forest regression model. Furthermore, the proposed approach presents high accuracy even regarding noisy images, resulting in RMSE measures of 3.64 and 4.15 for a Salt and Pepper and Gaussian noise, respectively. Experiments demonstrate reliability and robustness, optimizing the manufacturing.
A Machine Learning Model for Automatic Emotion Detection from Speech

The paper aims to create a machine-learning model for automatic emotion detection from speech. The developed model is to be used in the system of monitoring public emotions. A short analysis of other research papers on the process of designing machine-learning models for automatic emotion detection from speech has been provided in the article. Classical and deep machine learning methods and algorithms and some features of the initial dataset have been considered. The DailyDialog and its applicability for training the classificatory have been regarded. Moreover, developing and selecting the optimal model for automatic emotion detection from speech has been described. The research results on the influence of factors such as the number of records of each category in the training data set, text pre-processing, methods of vectorisation or word embedding, the choice of machine learning method for text classification, parameters and architecture of the model have been given. The examples of using the machine-learning model to analyse the collected real-world data have been demonstrated in the last section. The process of correlation of the change in the number of records that belong to different emotional categories with specific events in the life of society, the population of a geographical region or a community has been shown. Finally, the limitations of the created machine-learning model and some possible steps to refine the system for detecting emotions have been considered.
Applications of Learning Methods to Imaging Issues in Archaeology, Regarding Ancient Ceramic Manufacturing

Archaeological studies involve more and more numerical data analyses. In this work, we are interested in the analysis and classification of ceramic sherds tomographic images in order to help archaeologists in learning about the fabrication processes of ancient pottery. More specifically, a particular manufacturing process (spiral patchwork) has recently been discovered in early Neolithic Mediterranean sites, along with a more traditional coiling technique. It has been shown that the ceramic pore distribution in available tomographic images of both archaeological and experimental samples can reveal which manufacturing technique was used. Indeed, with the spiral patchwork, the pores exhibit spiral-like behaviours, whereas with the traditional one, they are distributed along parallel lines, especially in the experimental samples. However, in archaeological samples, these distributions are very noisy, making analysis and discrimination hard to process. Here, we investigate how Learning Methods (Deep Learning and Support Vector Machine) can be used to answer these numerically difficult problems. In particular, we study how the results depend on the input data (either raw data at the output of the tomographic device, or after a preliminary pore segmentation step), and the quality of the information they could provide to archaeologists.
WLNI-LPA: Detecting Overlapping Communities in Attributed Networks based on Label Propagation Process

Several networks are enriched by two types of information: the network topology and the attributes information about each node. Such graphs are typically called attributed networks, where the attributes are always as important as the topological structure. In these attributed networks, community detection is a critical task that aims to discover groups of similar users. However, the majority of the existing community detection methods in attributed networks were created to identify separated groups in attributed networks. Therefore, detecting overlapping communities using a combination of nodes attributes and topological structure is challenging. In this paper, we propose an algorithm, called WLNI-LPA, based on label propagation for detecting efficient community structure in the attributed network. WLNI-LPA is an extension of LPA that combines node importance, attributes information, and topology structure to improve the quality of graph partition. In the experiments, we validate the performance of our method on synthetic weighted networks. Also, a part of the experiment focuses on the impact of detecting significantly overlapping communities in the recommender system to improve the quality of recommendation.
Automatic Acoustic Diagnosis of Heartbeats

: Automatic identiﬁcation of heart irregularities based on the respective acoustic emissions is a relevant research ﬁeld which receives ever-increasing attention over the last years. Devices such as digital stethoscope and smartphones can record the heartbeat sounds and are easily accessible, making this method more appealing. This paper presents different automatic procedures to classify heartbeat sounds coming from such devices into ﬁve different labels: normal , murmur , extra heart sound , extrasystole and artifact so that even people without medical knowledge can detect heart irregularities. The data used in this paper come from two different datasets. The ﬁrst dataset is collected through an iPhone application whereas the second one is collected from a digital stethoscope. To be able to classify heartbeat sounds, time and frequency domain features are extracted and modeled by different machine learning algorithms, i.e. k -NN, random forest, SVM and ANNs. We report the achieved performances and a thorough comparison.
BLT+L: Efficient Signatures from Timestamping and Endorsements

We propose a new digital signature scheme based on combining cryptographic timestamping with an endorsement scheme, both of which can be constructed from one-way and collision-resistant hash functions. The signature scheme is efficient and allows balancing of key generation and signing time for signature size and verification time. The security analysis is based on a realistic model of timestamping. As part of our construction, we introduce the novel concept of endorsements, which may be of independent interest.
Empirical Security and Privacy Analysis of Mobile Symptom Checking Apps on Google Play

Smartphone technology has drastically improved over the past decade. These improvements have seen the creation of specialized health applications, which offer consumers a range of health-related activities such as tracking and checking symptoms of health conditions or diseases through their smartphones. We term these applications as Symptom Checking apps or simply SymptomCheckers. Due to the sensitive nature of the private data they collect, store and manage, leakage of user information could result in significant consequences. In this paper, we use a combination of techniques from both static and dynamic analysis to detect, trace and categorize security and privacy issues in 36 popular SymptomCheckers on Google Play. Our analyses reveal that SymptomCheckers request a significantly higher number of sensitive permissions and embed a higher number of third-party tracking libraries for targeted advertisements and analytics exploiting the privileged access of the SymptomCheckers in which they exist, as a mean of collecting and sharing critically sensitive data about the user and their device. We find that these are sharing the data that they collect through unencrypted plain text to the third-party advertisers and, in some cases, to malicious domains. The results reveal that the exploitation of SymptomCheckers is present in popular apps, still readily available on Google Play.
Enhancing the Open-Domain Dialogue Evaluation in Latent Space

The notorious one-to-many nature of open-domain dialogues poses huge challenges for automatic evaluation methods. Recent studies attempt to mitigate this issue by considering the similarity of the generated response with the conversational context and design discriminative models to learn from multiple positive responses. Despite the promising results, they can not be applied to general scenarios where training data with multiple responses is unavailable. To this end, in this paper, we propose a self-supervised setting to obtain a smooth latent space that can both capture discourse-level context information and implicitly model more references in latent space. Speciﬁcally, we present EMS, an E nhanced dialogue evaluation M etric in latent S pace. Experimental results on two real-world dialogue datasets conﬁrm the superiority of our method for open-domain dialogue evaluation, where both Pearson and Spearman correlations with human judgments outperform all baselines.
Towards Protecting Vital Healthcare Programs by Extracting Actionable Knowledge from Policy

In challenging economic times, obtaining value for money by ensuring financial integrity and fairer distribution of services are among the top priorities for social and health-care systems globally. However, healthcare billing policies are complex and identifying non-compliance is often narrow-scope, manual and expensive. Maintaining ‘integrity’ is a challenge - ensuring that scarce resources get to those in need and are not lost to fraud and waste. Our approach fuses recent advances in dependency parsing with a policy ontology to convert the content of regulatory healthcare policy into human-friendly policy rules, that are amenable to machine-execution, with human oversight. We describe the ontology-guided transformation of textual patterns into a semantically-meaningful knowledge graph of rules, outline our experiments and evaluate results against policy rules obtained from professional investigators. The aim is to make a policy-compliance ‘landscape’ visible to healthcare programs - helping them identify Fraud, Waste or Abuse.
Recommender Systems in Food Retail: Modeling Repeat Purchase Decisions on Transaction Data of a Stationary Food Retailer

In the course of growing online retailing, recommendation systems have become established that derive recommendations from customers’ purchase histories. Recommending suitable food products can represent a lucrative added value for food retailers, but at the same time challenges them to make good predictions for repeated food purchases. Repeat purchase recommendations have been little explored in the literature. These predict when a product will be purchased again by a customer. This is especially important for food recommendations, since it is not the frequency of the same item in the shopping basket that is relevant for determining repeat purchase intervals, but rather their difference over time. In this paper, in addition to critically reflecting classical recommendation systems on the underlying repeat purchase context, two models for online product recommendations are derived from the literature, validated and discussed for the food context using real transaction data of a German stationary food retailer.
A New Delegated Authentication Protocol based on PRE

New trends highlight the use of delegated authentication solutions where identity providers do not need to synchronize user credentials with services. It is a facility for service providers and also for users who do not have to create multiple accounts. Different solutions for single sign-on and delegated authentication exist. Most of these solutions require many exchanges between the different actors involved in the protocol, an additional TLS layer and/or the use of signature schemes which, in terms of security, rely on random oracles for reasons of efficiency. In this article, we recall the concept of the best known solutions (e.g. Kerberos, OpenID, .), briefly discuss the possibility of using one-way accumulators and define the Proxy Re-Encryption (PRE). Next, we propose a new delegated authentication protocol that allows users to authenticate anonymously on insecure networks and therefore asynchronously without direct communication between identity providers and service providers while minimizing the number of interactions. We based our solution on the use of PRE which could be instantiated by schemes based on standard assumptions. We first show how our protocol behaves against different types of attacks. Then in a more formal manner, we present the proof of security based on an adaptation of BAN logic method that supports the use of PRE functionalities.
Using surprisal and fMRI to map the neural bases of broad and local contextual prediction during natural language comprehension

Context guides comprehenders’ expectations during language processing, and information-theoretic surprisal is commonly used as an index of cognitive processing effort. However, prior work using surprisal has considered only within-sentence context, using n-grams, neural language models, or syntactic structure as conditioning context. In this paper, we extend the surprisal approach to use broader topical context, investigating the inﬂuence of local and topical context on processing via an analysis of fMRI time courses collected during naturalistic listening. Lexical surprisal calculated from ngram and LSTM language models is used to capture effects of local context; to capture the effects of broader context a new metric based on topic models, topical surprisal, is introduced. We identify distinct patterns of neural activation for lexical surprisal and topical sur-prisal. These differing neuro-anatomical correlates suggest that local and broad contextual cues during sentence processing recruit different brain regions and that those regions of the language network functionally contribute to processing different dimensions of contextual information during comprehension. More generally, our approach adds to a growing literature using methods from computational linguistics to operationalize and test hypotheses about neuro-cognitive mechanisms in sentence processing.
Tailoring Taint Analysis for Database Applications in the K Framework

Maintaining the integrity of underlying databases of any information systems is one of the challenges. This could be either due to coding flaws or due to improper flow of information from source to sink in the associated database applications. Compromising this may lead to either disclosure of sensitive information to the attackers or illegitimately modification of private data stored in the databases. Taint analysis is a widely used program analysis technique that aims at averting malicious inputs from corrupting data values in critical computations of programs. In this paper, we propose K-DBTaint, a rewriting logic-based executable semantics for taint analysis of database applications in the K framework. We specify the semantics for a subset of SQL statements along with host imperative program statements. Our K semantics can be seen as a sound approximation of program semantics in the corresponding security type domain. With respect to the existing methods, K-DBTaint supports contextand flow-sensitive analysis, reduces false alarms, and provides a scalable solution. Experimental evaluation on several PL/SQL benchmark codes demonstrates encouraging results as an improvement in the precision of the analysis.
Unsupervised Domain Adaptation for Event Detection using Domain-specific Adapters

Due to the multi-dimensional variation of textual data, detection of event triggers from new domains can become a lot more challenging. This prompts a need to research on domain adaptation methods for event detection task, especially for the most practical unsupervised setting. Recently, large transformer-based language models, e.g. BERT, have become essential to achieve top performance for event detection. However, their unwieldy nature also prevents effective adaptation across domains. To this end, this work proposes a D omain-speciﬁc A dapter-based A daptation ( DAA ) framework to improve the adaptability of BERT-based models for event detection across domains. By explicitly representing data from different domains with separate adapter modules in each layer of BERT, DAA introduces a novel joint representation learning mechanism and a Wasserstein distance-based technique for data selection in adversarial learning to substantially boost the performance on target domains. Extensive experiments and analysis over different datasets (i.e., LitBank, TimeBank, and ACE-05) demonstrate the effectiveness of our approach.
Detecting Harmful Memes and Their Targets

Among the various modes of communication in social media, the use of Internet memes has emerged as a powerful means to convey political, psychological, and socio-cultural opinions. Although memes are typically humorous in nature, recent days have witnessed a proliferation of harmful memes targeted to abuse various social entities. As most harmful memes are highly satirical and abstruse without appropriate contexts, off-the-shelf multimodal models may not be adequate to understand their underlying semantics. In this work, we propose two novel problem formulations: detecting harmful memes and the social entities that these harmful memes target. To this end, we present HarMeme, the first benchmark dataset, containing 3,544 memes related to COVID-19. Each meme went through a rigorous two-stage annotation process. In the first stage, we labeled a meme as very harmful, partially harmful, or harmless;in the second stage, we further annotated the type of target(s) that each harmful meme points to: individual, organization, community, or society/general public/other. The evaluation results using ten unimodal and multimodal models highlight the importance of using multimodal signals for both tasks. We further discuss the limitations of these models and we argue that more research is needed to address these problems.
End-to-End Construction of NLP Knowledge Graph

This paper studies the end-to-end construction of an NLP Knowledge Graph (KG) from scientific papers. We focus on extracting four types of relations: evaluatedOn between tasks and datasets, evaluatedBy between tasks and evaluation metrics, as well as coreferent and related relations between the same type of entities. For instance, F1-score is coreferent with F-measure. We introduce novel methods for each of these relation types and apply our final framework (SciNLP-KG) to 30,000 NLP papers from ACL Anthology to build a large-scale KG, which can facilitate automatically constructing scientific leaderboards for the NLP community. The results of our experiments indicate that the resulting KG contains high-quality information.
Determination of Fatty Acids and Octadecatrienoic Acid in Radish Seed

The components of the fatty acid and the contents of octadecatrienoic acid in radish seed were analyzed. The radish seed oil was extracted by soxhlet extraction. The obtained fatty oil was esterified by sodium hydroxide-methanol. The fatty acid was separated and identified by gas chromatography-mass spectrometry (GC/MS). Nine fatty acids are identified in the radish seed, the main components are unsaturated fatty acids. The unsaturated fatty acids are octadecatrienoic acid, octadecadienoic acid and 13-docosenoic acid, respectively. The content of octadecatrienoic acid, in radish seed was determinated by high performance liquid chromatography (HPLC).
Domain-Aware Dependency Parsing for Questions

Parsing natural language questions in speciﬁc domains is crucial to a wide range of applications from question-answering to dialog systems. Pre-trained parsers are usually trained on corpora dominated by non-questions, and thus perform poorly on domain-speciﬁc questions. Retraining parsers with domain-speciﬁc questions labeled with syntactic parse trees is expensive, as these annotations require linguistic expertise. In this paper, we propose an automatic labeled domain question generation framework by leveraging domain knowledge and seed domain questions. We evaluate our approach in two domains, and release the generated question datasets. Our experimental re-sults demonstrate that auto-generated labeled questions indeed lead to signiﬁcant ( 4 . 9% − 9% ) increase in the accuracy of state-of-the-art (SoTA) parsers on domain questions.
Compact Variable-base ECC Scalar Multiplication using Euclidean Addition Chains

: The random generation of Euclidean addition chains ﬁts well with a GLV context (Dosso et al., 2018) and provides a method with decent performance despite the growth of the base ﬁeld required to get the same level of security. The aim of this paper is to reduce the size of the base ﬁeld required. Combined with an algorithmic improvement, we obtain a reduction of 21% of the memory usage. Hence, our method appears to be one of the most compact scalar multiplication procedure and is particularly suitable for lightweight applications.