Reviewerly/exHarmony huggingface dataset:
Contextualized Embeddings Encode Monolingual and Cross-lingual Knowledge of Idiomaticity

Potentially idiomatic expressions (PIEs) are ambiguous between non-compositional idiomatic interpretations and transparent literal interpretations.For example, hit the road can have an idiomatic meaning corresponding to 'start a journey' or have a literal interpretation.In this paper we propose a supervised model based on contextualized embeddings for predicting whether usages of PIEs are idiomatic or literal.We consider monolingual experiments for English and Russian, and show that the proposed model outperforms previous approaches, including in the case that the model is tested on instances of PIE types that were not observed during training.We then consider cross-lingual experiments in which the model is trained on PIE instances in one language, English or Russian, and tested on the other language.We find that the model outperforms baselines in this setting.These findings suggest that contextualized embeddings are able to learn representations that encode knowledge of idiomaticity that is not restricted to specific expressions, nor to a specific language.
Product Quality Evaluation Method (PQEM): A Comprehensive Approach for the Software Product Life Cycle

Project managers, product owners, and quality assurance leaders need to visualize and understand the entire picture of the development process as well as comprehend the product quality level, in a synthetic and intuitive way in order to facilitate the decision of accepting or rejecting each iteration within the software life cycle. This idea is extremely important nowadays, due to the fact that time is a key resource and it should be managed wisely to obtain a feasible quality level for each software deliverable. This article presents a novel solution called Product Quality Evaluation Method (PQEM) to evaluate a set of quality characteristics for each iteration of a software product. PQEM is based on the Goal-Question-Metric approach, the standard ISO/IEC 25010, and the extension made of testing coverage in order to obtain the quality coverage of each quality characteristic. The outcome of PQEM is a single value representing the quality per each iteration of a product, as an aggregated measure. Even though a value it is not the regular idea of measuring quality, we believe that it can be useful to use this value to understand easily the quality level of each iteration. An illustrative example of the method was carried out with a web and mobile application, within the healthcare environment.
Synchronous Syntactic Attention for Transformer Neural Machine Translation

This paper proposes a novel attention mechanism for Transformer Neural Machine Translation, "Synchronous Syntactic Attention," inspired by synchronous dependency grammars.The mechanism synchronizes source-side and target-side syntactic self-attentions by minimizing the difference between target-side selfattentions and the source-side self-attentions mapped by the encoder-decoder attention matrix.The experiments show that the proposed method improves the translation performance on WMT14 En-De, WMT16 En-Ro, and AS-PEC Ja-En (up to +0.38 points in BLEU).
Learning compositional structures for semantic graph parsing

AM dependency parsing is a method for neural semantic graph parsing that exploits the principle of compositionality.While AM dependency parsers have been shown to be fast and accurate across several graphbanks, they require explicit annotations of the compositional tree structures for training.In the past, these were obtained using complex graphbankspecific heuristics written by experts.Here we show how they can instead be trained directly on the graphs with a neural latent-variable model, drastically reducing the amount and complexity of manual heuristics.We demonstrate that our model picks up on several linguistic phenomena on its own and achieves comparable accuracy to supervised training, greatly facilitating the use of AM dependency parsing for new sembanks.
WVOQ at SemEval-2021 Task 6: BART for Span Detection and Classification

Simultaneous span detection and classification is a task not currently addressed in standard NLP frameworks. The present paper describes why and how an EncoderDecoder model was used to combine span detection and classification to address subtask 2 of SemEval-2021 Task 6.
Text Style Transfer: Leveraging a Style Classifier on Entangled Latent Representations

Learning a good latent representation is essential for text style transfer, which generates a new sentence by changing the attributes of a given sentence while preserving its content.Most previous work adopt disentangled latent representation learning to realize style transfer.We propose a novel text style transfer algorithm with entangled latent representation, and introduce a style classifier that can regulate the latent structure and transfer style.Moreover, our algorithm for style transfer applies to both single-attribute and multi-attribute transfer.Extensive experimental results show that our method generally outperforms state-of-theart approaches.
Exploring the limits of the generation of nonclassical states of spins coupled to a cavity by optimal control

We investigate the generation of nonclassical states of spins coupled to a common cavity by means of a collective driving of the spins. We propose a control strategy using specifically designed series of short coherent and squeezing pulses, which have the key advantage of being experimentally implementable with the state-of-the art techniques. The parameters of the control sequence are found by means of optimization algorithms. We consider the cases of two and four spins, the goal being either to reach a well-defined target state or a state maximizing a measure of nonclassicality. We discuss the influence of cavity damping and spin offset on the generation of nonclassical states. We also explore to what extent squeezing fields help to enhance the efficiency of the control process.
IIE-NLP-Eyas at SemEval-2021 Task 4: Enhancing PLM for ReCAM with Special Tokens, Re-Ranking, Siamese Encoders and Back Translation

This paper introduces our systems for all three subtasks of SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning.To help our model better represent and understand abstract concepts in natural language, we welldesign many simple and effective approaches adapted to the backbone model (RoBERTa).Specifically, we formalize the subtasks into the multiple-choice question answering format and add special tokens to abstract concepts, then, the final prediction of QA is considered as the result of subtasks.Additionally, we employ many finetuning tricks to improve the performance.Experimental results show that our approach gains significant performance compared with the baseline systems.Our system ¶ achieves eighth rank (87.51%) and tenth rank (89.64%) on the official blind test set of subtask 1 and subtask 2 respectively.
Use of Microblogging, Social Networking, and Short Messages in E-learning for Information Culture Building

This work examines the effect of microblogging, social networking, and short messaging in e-learning on the process of information culture formation. The study takes an experimental training course "The Psychology of Management" designed with Moodle, which implies the use of Instagram, and applies it to a sample of master’s level students. Learners were recruited from the Higher School of Economics and Business (Al-Farabi Kazakh National University) together with the Institute of Economics and Management in Agribusiness (Russian State Agrarian University - Timiryazev Moscow Agricultural Academy (RSAU – MTAA)). Students were asked to briefly express their thoughts, attitudes, and views toward the given topic on Instagram by creating blog posts and leave motivational and constructive comments under the blogs of other group members. The emotional responses of learners to the innovative style of teaching (i.e., the use of blogs) and its effect on the learning process were assessed using surveys. The study discusses the effect of blogging on Instagram as an educational tool. The majority of respondents reported a positive effect of blogs. According to learners, the use of Instagram gave the learning journey a makeover, making it relevant, effortless, and consistent. The use of blogs was found to create favorable conditions for collaborative learning, creative self-expression, and systematization of knowledge and experiences. Students reported that doing blogs was entertaining, motivating, and beneficial to their critical thinking, writing skills, and communication competency.
UPB at SemEval-2021 Task 8: Extracting Semantic Information on Measurements as Multi-Turn Question Answering

Extracting semantic information on measurements and counts is an important topic in terms of analyzing scientific discourses.The 8th task of SemEval-2021: Counts and Measurements (MeasEval) aimed to boost research in this direction by providing a new dataset on which participants train their models to extract meaningful information on measurements from scientific texts.The competition is composed of five subtasks that build on top of each other: (1) quantity span identification, (2) unit extraction from the identified quantities and their value modifier classification, (3) span identification for measured entities and measured properties, (4) qualifier span identification, and ( 5) relation extraction between the identified quantities, measured entities, measured properties, and qualifiers.We approached these challenges by first identifying the quantities, extracting their units of measurement, classifying them with corresponding modifiers, and afterwards using them to jointly solve the last three subtasks in a multiturn question answering manner.Our best performing model obtained an overlapping F1score of 36.91% on the test set.
CASE 2021 Task 2: Zero-Shot Classification of Fine-Grained Sociopolitical Events with Transformer Models

We introduce a method for the classification of texts into fine-grained categories of sociopolitical events.This particular method is responsive to all three Subtasks of Task 2, Fine-Grained Classification of Socio-Political Events, introduced at the CASE workshop of ACL-IJCNLP 2021.We frame Task 2 as textual entailment: given an input text and a candidate event class ("query"), the model predicts whether the text describes an event of the given type.The model is able to correctly classify in-sample event types with an average F 1score of 0.74 but struggles with some out-ofsample event types.Despite this, the model shows promise for the zero-shot identification of certain sociopolitical events by achieving an F 1 -score of 0.52 on one wholly out-of-sample event class.
HoneyNetCloud Investigation Model, A Preventive Process Model for IoT Forensics

With the pervasive usage of sensing systems and IoT things, the importance of security has increased. Attempts towards breaching IoT security systems by attackers are on upsurge. Many intrusions in embedded systems, sensing equipment and IoT things have occurred in the past. Though there are cyber security tools like Antivirus, Intrusion detection and prevention systems available for securing the digital devices and its networks. However, a forensic methodology to be followed for the analysis and investigation to detect origin cause of network incidents is lacking. This paper derives a comprehensive preventive cyber forensic process model with honeypots for the digital IoT investigation process which is formal, that can assist in the court of law in defining the reliability of the investigative process. One year data of various attacks to the IoT network has been recorded by the honeypots for this study. The newly derived model HIM has been validated using various methods and instead of converging on a particular aspect of investigation, it details the entire lifecycle of IoT forensic investigation. The model is targeted to address the forensic analysts’ requirements and the need of legal fraternity for a forensic model. The process model follows a preventive method which reduce further attacks on network.
ViTA: Visual-Linguistic Translation by Aligning Object Tags

Multimodal Machine Translation (MMT) enriches the source text with visual information for translation.It has gained popularity in recent years, and several pipelines have been proposed in the same direction.Yet, the task lacks quality datasets to illustrate the contribution of visual modality in the translation systems.In this paper, we propose our system under the team name Volta for the Multimodal Translation Task of WAT 2021 1 (Nakazawa et al., 2021) from English to Hindi.We also participate in the textual-only subtask of the same language pair for which we use mBART, a pretrained multilingual sequence-to-sequence model.For multimodal translation, we propose to enhance the textual input by bringing the visual information to a textual domain by extracting object tags from the image.We also explore the robustness of our system by systematically degrading the source text.Finally, we achieve a BLEU score of 44.6 and 51.6 on the test set and challenge set of the multimodal task.
Technical Report on Shared Task in DialDoc21

We participate in the DialDoc Shared Task subtask 1 (Knowledge Identification).The task requires identifying the grounding knowledge in form of a document span for the next dialogue turn.We employ two well-known pre-trained language models (RoBERTa and ELECTRA) to identify candidate document spans and propose a metric-based ensemble method for span selection.Our methods include data augmentation, model pre-training/fine-tuning, postprocessing, and ensemble.On the submission page, we rank 2nd based on the average of normalized F1 and EM scores used for the final evaluation.Specifically, we rank 2nd on EM and 3rd on F1.
A Multiparameter Analytical Model of the Physical Infrastructure of a Cloud-Based System

The large popularity of services offered by cloud computing (CC) requires constant expansion of its physical infrastructure. At the same time, CC operators apply various mechanisms that enable the existing physical resources to be optimally used. Useful tools in the design and optimization process are analytical and simulation models. They allow information about the operation of CC to be obtained without the need to make changes to the physical infrastructure. This article presents and discusses a multiservice, multiparameter model of a CC physical infrastructure that provides services of the infrastructure-as-a-service (IaaS) type. In the proposed model, the following four elements are used to specify network settings and describe the demands necessary to activate a virtual machine: the number of processors, the capacity of RAM, the HDD capacity, and the required network bitrate. Such an approach gives the opportunity to describe accurately the use of resources in real servers. To verify and validate the proposed model, we developed and implemented a simulator of the physical infrastructure of CC. The results of the simulations confirm the validity of all the theoretical assumptions adopted in the model. The proposed model can also serve as a tool, in the form of an appropriate application, for determining the resources that are necessary to service calls at the required loss level.
Environmental education of Z generation students for sustainable development of rural areas (the example of the Russian-Finnish project Luga-Balt-2)

In September 2015, the UN General Assembly adopted 17 goals and 169 targets for sustainable development until 2030. The fourth goal provides for the development of inclusive and equitable quality education, and goals 12, 13 and 14 are aimed at protecting the environment and organizing appropriate education for this. When implementing environmental education, it is necessary to take into account the characteristics of a generation of students, in this case, generation Z. For a number of years, Russian-Finnish projects were carried out in the Luga district of the Leningrad region. One of the main objectives of the projects concerned the environmental education of schoolchildren and students. Using international experience, a number of interactive teaching methods have been developed and are still being used. They are focused on fostering student independence and provide his motivation. A student conducts his own research work and participates in a youth team to prepare and to implement their own projects aimed at solving local problems. This approach forms an ecological culture of an ecocentric type among students, and their desire to bear their part of responsibility for the state of the environment.
Al-Quran recitation verification for memorization test using Siamese LSTM network

In the process of verifying Al-Quran memorization, a person is usually asked to recite a verse without looking at the text. This process is generally done together with a partner to verify the reading. This paper proposes a model using Siamese LSTM Network to help users check their Al-Quran memorization alone. Siamese LSTM network will verify the recitation by matching the input with existing data for a read verse. This study evaluates two Siamese LSTM architectures, the Manhattan LSTM and the Siamese-Classifier. The Manhattan LSTM outputs a single numerical value that represents the similarity, while the Siamese-Classifier uses a binary classification approach. In this study, we compare Mel-Frequency Cepstral Coefficient (MFCC), Mel-Frequency Spectral Coefficient (MFSC), and delta features against model performance. We use the public dataset from Every Ayah website and provide the usage information for future comparison. Our best model, using MFCC with delta and Manhattan LSTM, produces an F1-score of 77.35%
Cambridge at SemEval-2021 Task 1: An Ensemble of Feature-Based and Neural Models for Lexical Complexity Prediction

This paper describes our submission to the SemEval-2021 shared task on Lexical Complexity Prediction.We approached it as a regression problem and present an ensemble combining four systems, one feature-based and three neural with fine-tuning, frequency pre-training and multi-task learning, achieving Pearson scores of 0.8264 and 0.7556 on the trial and test sets respectively (sub-task 1).We further present our analysis of the results and discuss our findings.
The University of Edinburgh’s Submission to the IWSLT21 Simultaneous Translation Task

We describe our submission to the IWSLT 2021 shared task 1 on simultaneous text-to-text English-German translation.Our system is based on the re-translation approach where the agent re-translates the whole source prefix each time it receives a new source token.This approach has the advantage of being able to use a standard neural machine translation (NMT) inference engine with beam search, however, there is a risk that incompatibility between successive re-translations will degrade the output.To improve the quality of the translations, we experiment with various approaches: we use a fixed size wait at the beginning of the sentence, we use a language model score to detect translatable units, and we apply dynamic masking to determine when the translation is unstable.We find that a combination of dynamic masking and language model score obtains the best latency-quality trade-off.
Hybrid features for object detection in RGB-D scenes

&lt;div&gt;Object detection is considered a hot research topic in applications of artificial intel-ligence and computer vision. Historically, object detection was widely used in var-ious fields like surveillance, fine-grained activities and robotics. All studies focus on improving accuracy for object detection using images, whether indoor or outdoor scenes. Therefore, this paper took a shot by improving the doable features extraction and proposing crossed sliding window approach using exiting classifiers for object de-tection. In this paper, the contribution includes two parts: First, improving local depth pattern feature along side SIFT and the second part explains a new technique presented by proposing crossed sliding window approach using two different types of images (colored and depth). Two types of features local depth patterns for detection (LDPD) and scale-invariant feature transform (SIFT) were merged as one feature vector. The RGB-D object dataset has been used and it consists of 300 different objects and in-cludes thousands of scenes. The proposed approach achieved high results comparing to other features or separated features that are used in this paper. All experiments and comparatives were applied on the same dataset for the same objective. Experimental results report a high accuracy in terms of detection rate, recall, precision and F1 scorein RGB-D scenes.&lt;/div&gt;
Stanford MLab at SemEval-2021 Task 1: Tree-Based Modelling of Lexical Complexity using Word Embeddings

This paper presents our system for the singleand multi-word lexical complexity prediction tasks of SemEval Task 1: Lexical Complexity Prediction.Text comprehension depends on the reader's ability to understand the words present in it; evaluating the lexical complexity of such texts can enable readers to find an appropriate text and systems to tailor a text to an audience's needs.We present our model pipeline, which applies a combination of embedding-based and manual features to predict lexical complexity on the CompLex English dataset using various tree-based and linear models.Our method is ranked 27 / 54 on single-word prediction and 14 / 37 on multiword prediction.
You Were Always on My Mind: Introducing Chef’s Hat and COPPER for Personalized Reinforcement Learning

Reinforcement learning simulation environments pose an important experimental test bed and facilitate data collection for developing AI-based robot applications. Most of them, however, focus on single-agent tasks, which limits their application to the development of social agents. This study proposes the Chef’s Hat simulation environment, which implements a multi-agent competitive card game that is a complete reproduction of the homonymous board game, designed to provoke competitive strategies in humans and emotional responses. The game was shown to be ideal for developing personalized reinforcement learning, in an online learning closed-loop scenario, as its state representation is extremely dynamic and directly related to each of the opponent’s actions. To adapt current reinforcement learning agents to this scenario, we also developed the COmPetitive Prioritized Experience Replay (COPPER) algorithm. With the help of COPPER and the Chef’s Hat simulation environment, we evaluated the following: (1) 12 experimental learning agents, trained via four different regimens (self-play, play against a naive baseline, PER, or COPPER) with three algorithms based on different state-of-the-art learning paradigms (PPO, DQN, and ACER), and two “dummy” baseline agents that take random actions, (2) the performance difference between COPPER and PER agents trained using the PPO algorithm and playing against different agents (PPO, DQN, and ACER) or all DQN agents, and (3) human performance when playing against two different collections of agents. Our experiments demonstrate that COPPER helps agents learn to adapt to different types of opponents, improving the performance when compared to off-line learning models. An additional contribution of the study is the formalization of the Chef’s Hat competitive game and the implementation of the Chef’s Hat Player Club, a collection of trained and assessed agents as an enabler for embedding human competitive strategies in social continual and competitive reinforcement learning.