Miracle-dz/UniFAR-FTUs huggingface dataset:
Concept of a Robotic System for Autonomous Coarse Waste Recycling

The recycling of coarse waste such as construction and demolition waste (CDW), bulky waste, etc., is a process that is currently performed mechanically and manually. Unlike packaging waste, commercial waste and the like, which is usually cut or shredded into small pieces and then automatically separated and sorted on conveyor belt-based systems, coarse waste is separated by specialized personnel using wheel loaders, cranes or excavators. This paper presents the concept of a robotic system designed to autonomously separate recyclable coarse materials from bulky waste, demolition and construction waste, etc. The proposed solution explicitly uses existing heavy equipment (e.g., an excavator currently in use on-site) rather than developing a robot from scratch. A particular focus is set on the sensory system options used to identify and classify waste objects.
Analyzing Code Embeddings for Coding Clinical Narratives

Medical professionals review clinical narratives to assign medical codes as per the International Classiﬁcation of Diseases (ICD) for billing and care management. This manual process is inefﬁcient and error-prone as it involves a nuanced one-to-many mapping. Recent works on automated ICD coding learn mappings between low-dimensional representations of the reports and the codes. While they propose novel neural networks for encoding varied types of information about the codes, it is unclear as to what information in the medical codes is helpful for performance improvement and why. Here, we compare different ways to represent, or embed, the codes based on their textual, structural and statistical characteristics, using a single deep learning base-line model in quantitative evaluations on discharge reports from the MIMIC-III Intensive Care Unit database. We also qualitatively analyse the nature of the cases that beneﬁt most from the code embeddings and demonstrate that code embeddings are important for predicting ambiguous and oblique codes.
Transfer Learning for Just-in-Time Design Smells Prediction using Temporal Convolutional Networks

This paper investigates whether the adoption of a transfer learning approach can be effective for just-in-time design smells prediction. The approach uses a variant of Temporal Convolutional Networks to predict design smells and a carefully selected fine-grained process and product metrics. The validation is performed on a dataset composed of three open-source systems and includes a comparison between transfer and direct learning. The hypothesis, which we want to verify, is that the proposed transfer learning approach is feasible to transfer the knowledge gained on mature systems to the system of interest to make reliable predictions even at the beginning of development when the available historical data is limited. The obtained results show that, when the class imbalance is high, the transfer learning provides F1-scores very close to the ones obtained by
Evaluation of the Capabilities of a Hybrid Driven Exoskeleton in Passive Mode of Interaction

A new construction of upper limbs exoskeleton with hybrid drive was studied in this work. The paper presents mechanical structure and actuation of exoskeleton with hybrid drive including pneumatic cylinders and electric motors. In order to evaluate the transparency and safety, the capabilities of the exoskeleton in passive mode of interaction was estimated. A model of the interaction force between the patient and the exoskeleton arm in passive mode was built. The model was based on harmonious movements imposed in one joint of the exoskeleton arm. Experiments and simulations were performed to assess the interaction force between the patient and the exoskeleton because of the mechanical impedance of the device. The force of interaction was obtained from passive forces, such as inertial, frictional and gravitational forces, as well as from the elasticity of the pneumatics. The patient-initiated harmonic motion was studied in two caseswithout pressure in the chambers and with pressure for gravity compensation. The results where demonstrated graphically. Conclusions where made about the behavior of the exoskeleton in the passive mode of interaction.
Point Defects and Non-stoichiometry in Li2TiO3

The intermediate-temperature, monoclinic β-phase of Li2TiO3 shows a stoichiometry range from 47 to 51.5 mol % TiO2. This broad stoichiometric range may be exploited for industrial applications, such as breeder material in a fusion reactor or a microwave dielectric. Here, density functional theory is employed to calculate formation energies for the intrinsic defect species, allowing the identification of the mechanisms responsible for accommodating both excess Li2O and TiO2 across a wide range of temperatures and oxygen partial pressures. The results predict that while the exact mode of accommodating non-stoichiometry depends on factors such as the temperature and oxygen partial pressure, cation disorder plays a major role in the incorporation of non-stoichiometry and that oxygen defects are of relatively minor importance.
A Framework for Security Monitoring of Real IoT Testbeds

Internet of Things (IoT) has been acknowledged as a novel transformation technology because of its wide range of applications in various domains, namely connected agriculture, industrial control, smart buildings and home automation. It promises innovative business models and improved user experience. However, the devices are prone to failures and malicious attacks on account of their resource-constrained characteristics. In this paper, we present a framework for security monitoring of IoT systems. It is based on MMT-IoT, which is a reactive monitoring tool to be deployed in a running IoT environment to address malicious behaviors, failures and attacks. In this paper we also present the experiments conducted on two practical IoT-6LoWPAN testbeds. The preliminary results confirmed the efficiency of the proposed solution.
Semantic Relation-aware Difference Representation Learning for Change Captioning

Change captioning is to describe the difference in a pair of images with a natural language sentence. In this task, the distractors, such as the illumination or viewpoint change, bring the huge challenges about learning the difference representation. In this paper, we propose a semantic relation-aware difference representation learning network to explicitly learn the difference representation in the existence of distractors. Speciﬁcally, we introduce a self-semantic relation embedding block to explore the underlying changed objects and design a cross-semantic relation measuring block to localize the real change and learn the discriminative difference representation. Besides, relying on the POS of words, we devise an attention-based visual switch to dynamically use visual information for caption generation. Extensive experiments show that our method achieves the state-of-the-art performances on CLEVR-Change and Spot-the-Diff datasets 1 .
Grammar-Constrained Neural Semantic Parsing with LR Parsers

Target meaning representations for semantic parsing tasks are often based on programming or query languages, such as S QL , and can be formalized by a context-free grammar. Assuming a priori knowledge of the target domain, such grammars can be exploited to enforce syntactical constraints when predicting logical forms. To that end, we assess how syntactical parsers can be integrated into modern encoder-decoder frameworks. Speciﬁcally, we implement an attentional S EQ 2S EQ model that uses an LR parser to maintain syntactically valid sequences throughout the decoding procedure. Compared to other approaches to grammar-guided decoding that modify the underlying neural network architecture or attempt to derive full parse trees, our approach is conceptually simpler, adds less computational overhead during inference and integrates seamlessly with current S EQ 2S EQ frameworks. We present preliminary evaluation results against a recurrent S EQ 2S EQ baseline on G EO Q UERY and ATIS and demonstrate improved performance while enforcing grammatical constraints.
Frustratingly Simple Few-Shot Slot Tagging

We propose a simple and effective few-shot model for slot tagging. Recent work shows that it is promising to extend standard few-shot classiﬁcation methods to sequence labeling with CRF-speciﬁc augmentations. Such methods show strengths in encoding slot name semantics and slot dependencies. However, we ﬁnd these strengths can be obtained by a much simpler method, which casts slot tagging into machine reading comprehension (MRC). We ﬁne-tune a standard BERT-based MRC model with a mixture of source domain and (few-shot) target domain data. Such simple method outperforms state-of-the-art methods by a large margin on the SNIPS dataset.
Inclusion of User Behavior and Social Context Information in ML-based QoE Prediction

: The widespread use of online video content in every area of the connected world increases the interest in Quality of Experience (QoE). QoE plays a crucial role in the success of video streaming services. However, QoE prediction is challenging as many compelling factors (i.e., human and context factors) impact the QoE and QoE management solution often neglect the impact of social context and user behavior factors on the end-user’s QoE. To address these challenges, we have developed a web application to conduct subjective study and collect data from application-layer, user-level, and service-level. The collected data is then used as training set for machine learning models including decision tree, K-nearest neighbor, and support vector machine for the purpose of QoE prediction.
Cross-Lingual Cross-Domain Nested Named Entity Evaluation on English Web Texts

Named Entity Recognition (NER) is a key Natural Language Processing task. However, most existing work on NER targets ﬂat named entities (NEs) and ignores the recognition of nested structures, where entities can be enclosed within other NEs. Moreover, evaluation of Nested Named Entity Recognition (NNER) across domains remains challenging, mainly due to the limited availability of datasets. To address these gaps, we present EWT-NNER, a dataset covering ﬁve web domains annotated for nested named entities on top of the English Web Treebank (EWT). We present the corpus and an empirical evaluation, including transfer results from German and Danish. EWT-NNER is annotated for four major entity types, including sufﬁxes for derivational entity markers and partial named entities, spanning a total of 12 classes. We envision the public release of EWT-NNER to encourage further research on nested NER, particularly on cross-lingual cross-domain evaluation.
A Survey of Social Emotion Prediction Methods

Emotions are an important factor that affects our communication. Considerable research has been done to detect and classify emotion in text. However, most deal with emotion from the writer’s perspective. Social emotion is the emotion of the reader when exposed to the text. With the increased use of social media, many works are performed for social emotion prediction. In this paper, we attempt to provide a survey of social emotion prediction methods. To the best of our knowledge, this is the first work to survey the literature of social emotion, review methods, and used techniques, compare the methods, and highlight their limitations.
Attending via both Fine-tuning and Compressing

Though being a primary trend for enhancing interpretability of neural networks, attention mechanism’s reliability and validity are still under debate. In this paper, we try to purify attention scores to obtain a more faithful explanation of downstream models. Speciﬁ-cally, we propose a framework consisting of a learner and a compressor, which performs ﬁne-tuning and compressing iteratively to enhance the performance and interpretability of the attention mechanism. The learner focuses on learning better text representations to achieve good decisions by ﬁne-tuning, while the compressor aims to perform compressions over the representations to retain the most useful clues for explanations with a Variational information bottleneck ATtention (VAT) mechanism. Extensive experiments on eight benchmark datasets show the great advantages of our proposed approach in terms of both performance and interpretability.
Understanding Feature Focus in Multitask Settings for Lexico-semantic Relation Identification

Discovering whether words are semantically related and identifying the speciﬁc semantic relation that holds between them is of crucial importance for automatic reasoning on text data. For that purpose, different methodologies have been proposed that either (1) tackle feature engineering, (2) ﬁne-tune latent semantic spaces, or (3) take advantage of cognitive links be-tween semantic relations in multitask settings. In this paper, we investigate how feature engineering and multitask architectures can be improved and consequently combined to identify lexico-semantic relations. Evaluation re-sults over a set of gold-standard datasets show that (1) combinations of similar features are beneﬁcial (feature sets), (2) asymmetric distributional features are a strong cue to discriminate asymmetric relations as well as they play an important role in multitask architectures, (3) shared-private models improve over binary and fully-shared classiﬁers as well as they correctly balance the focus on features between private and shared layers 1 .
Efficient Joint Random Number Generation for Secure Multi-party Computation

Large availability of smart devices and an increased number of online activities result in extensive personalized or customized services in many domains. However, the data these services mostly rely on are highly privacysensitive, as in pace-makers. In the last decades, many privacy breaches have increased privacy awareness, leading to stricter regulations on data processing. To comply with this legislation, proper privacy preservation mechanisms are required. One of the technological solutions, which is also provably secure, is Secure Multi-Party Computation (SMPC) that can compute any function with secret inputs. Mainly, in several SMPC solutions, such as data aggregation, we observe that secret values distributed among parties are masked with random numbers, encrypted and combined to yield the desired outcome. To ensure correct decryption of the final result, it is required that these numbers sum to a publicly known value, for instance, zero. Despite its importance, many of the corresponding works omit how to obtain such random numbers jointly or suggest procedures with high computational and communication overhead. This paper proposes two novel protocols for Joint Random Number Generation with very low computational and communication overhead. Our protocols are stand-alone and not embedded in others, and can therefore be used in data aggregation and other applications, for instance, machine learning algorithms, that require such random numbers. We first propose a protocol that relies on bit-wise sharing of individually generated random numbers, allowing parties to adapt random numbers to yield a public sum. Second, we propose a protocol that uses the sign function to generate a random number from broadcast numbers. We provide security and complexity analyses of our protocols.
Hashing based Efficient Inference for Image-Text Matching

Image-text matching has been a popular research topic which bridges vision and language through semantic understanding. Recent works mainly focus on exploring the interactions between images and sentences to improve the performance without considering inference efﬁciency. Speciﬁcally, for the large scale databases, it is unacceptable to perform such time-consuming mechanisms between a query (text/image) and each candidate data-point (image/text) in the whole retrieval set during inference. To tackle this problem, we propose a novel hashing based efﬁcient inference module called HEI, which can be plugged into the existing framework to speed up the inference step without reducing the retrieval performance. In details, HEI learns to map the original datapoints into short binary hash codes and coarsely preserve the heterologous matching relationship. Thus, in the inference phase, the proposed HEI module uses the hash codes to quickly select a few candidate datapoints from the retrieval set for a given query. Then, the image-text matching model ﬁne ranks the candidate set to ﬁnd the matching datapoint. Extensive experiments on two widely used benchmark MS-COCO and Flickr30k with four baseline methods demonstrate the efﬁciency and effectiveness of our proposed HEI module.
Slot Transferability for Cross-domain Slot Filling

Cross-domain slot ﬁlling focuses on using labeled data from source domains to train a slot ﬁlling model for target domains. It is of great signiﬁcance for transferring a dialogue system into new domains. Most of the existing work focused on building a cross-domain transfer model. From the perspective of slots themselves, this paper proposes a model-agnostic Slot Transferability Measure (STM) for evaluating the transferability from a source slot to a target slot, speciﬁcally, the degree that labeled data of the source slot is helpful to train the slot ﬁlling model for the target slot. We also give a STM-based method for a model to select helpful source slots and their labeled data for a given target slot. Experimental results on multiple existing models and datasets show that our method signiﬁcantly outperforms state-of-the-art baselines in cross-domain slot ﬁlling. The code is available at https://github. com/luhengtong/STM-for-cdsf.git .
Involving Humans in the Cryptographic Loop: Introduction and Threat Analysis of EEVEHAC

Our digital lives rely on modern cryptography that is based on complicated mathematics average human users cannot follow. Previous attempts at adding the human user into the cryptographic loop include things like Human Authenticated Key Exchange and visualizable cryptography. This paper presents our proof-of-concept implementation of these ideas as a system called EEVEHAC. It utilizes human capabilities to achieve an endto-end encrypted channel between a user and a server that is authenticated with human senses and can be used through untrusted environments. The security of this complete system is analyzed. We find that the combination of the two different systems into EEVEHAC on a theoretical level retains the security of the individual systems. We also identify the weaknesses of this implementation and discuss options for overcoming them.
Sustainable Development Goals Monitoring and Forecasting using Time Series Analysis

A framework for UN Sustainability for Development Goal (SDG) attainment prediction is presented, the SDG Track, Trace & Forecast (SDG-TTF) framework. Unlike previous SDG attainment frameworks, SDGTTF takes into account the potential for causal relationship between SDG indicators both with respect to the geographic entity under consideration (intra-entity), and neighbouring geographic entities to the current entity (inter-entity). The challenge is in the discovery of such causal relationships. Six alternatives mechanisms are considered. The identified relationships are used to build multivariate time series prediction models which feed into a bottom-up SDG prediction taxonomy, which in turn is used to make SDG attainment predictions. The framework is fully described and evaluated. The evaluation demonstrates that the SDG-TTF framework is able to produce better predictions than alternative models which do not take into consideration the potential for intra and intercausal relationships.
Probing Multi-modal Machine Translation with Pre-trained Language Model

Multi-modal machine translation (MMT) aimed at using images to help disambiguate the target during translation and improving robustness, but some recent works showed that the contribution of visual features is either negligible or incremental. In this paper, we show that incorporating pre-trained (vision) language model (VLP) on the source side can improve the multi-modal translation quality signiﬁcantly. Motivated by BERT, VLP aims to learn better cross-modal representations that improve target sequence generation. We simply adapt BERT to a cross-modal domain for the vision language pre-training, and the downstream multi-modal machine translation can substantially beneﬁt from the pre-training. We also introduce an attention based modality loss to promote the image-text alignment in the latent semantic space. Ablation study veriﬁes that it is effective in further improving the translation quality. Our experiments on the widely used Multi-30K dataset show increased BLEU score up to 6.2 points compared with the text-only model, achieving the state-of-the-art results with a large margin in the semi-unconstrained scenario and indicating a possible direction to rejuvenate the multi-modal machine translation.
Climbing the Tower of Treebanks: Improving Low-Resource Dependency Parsing via Hierarchical Source Selection

Recent work on multilingual dependency parsing focused on developing highly multilingual parsers that can be applied to a wide range of low-resource languages. In this work, we substantially outperform such “one model to rule them all” approach with a heuristic selection of languages and treebanks on which to train the parser for a speciﬁc target language. Our approach, dubbed T OWER , ﬁrst hierarchically clusters all Universal Dependencies languages based on their mutual syntactic similarity computed from human-coded URIEL vectors. For each low-resource target language, we then climb this language hierarchy starting from the leaf node of that language and heuristically choose the hierarchy level at which to collect training treebanks. This treebank selection heuristic is based on: (i) the aggregate size of all treebanks subsumed by the hierarchy level and (ii) the similarity of the languages in the training sample with the target language. For languages without development treebanks, we additionally use (ii) for model selection (i.e., early stopping) in order to prevent overﬁtting to development treebanks of closest languages. Our T OWER approach shows substantial gains for low-resource languages over two state-of-the-art multilingual parsers, with more than 20 LAS point gains for some of those languages. Parsing models and code available at: https: //github.com/codogogo/towerparse .
Task-adaptive Pre-training of Language Models with Word Embedding Regularization

Pre-trained language models (PTLMs) acquire domain-independent linguistic knowledge through pre-training with massive textual resources. Additional pre-training is effective in adapting PTLMs to domains that are not well covered by the pre-training corpora. Here, we focus on the static word embeddings of PTLMs for domain adaptation to teach PTLMs domain-specific meanings of words. We propose a novel fine-tuning process: task-adaptive pre-training with word embedding regularization (TAPTER). TAPTER runs additional pre-training by making the static word embeddings of a PTLM close to the word embeddings obtained in the target domain with fastText. TAPTER requires no additional corpus except for the training data of the downstream task. We confirmed that TAPTER improves the performance of the standard fine-tuning and the task-adaptive pre-training on BioASQ (question answering in the biomedical domain) and on SQuAD (the Wikipedia domain) when their pre-training corpora were not dominated by in-domain data.