Reviewerly/exHarmony huggingface dataset:
KaMiKla at SemEval-2022 Task 3: AlBERTo, BERT, and CamemBERT—Be(r)tween Taxonomy Detection and Prediction

This paper describes our system submitted for SemEval Task 3: Presupposed Taxonomies: Evaluating Neural Network Semantics (Zamparelli et al., 2022). We participated in both the binary classification and the regression subtask. Target sentences are classified according to their taxonomical relation in subtask 1 and according to their acceptability judgment in subtask 2. Our approach in both subtasks is based on a neural network BERT model. We used separate models for the three languages covered by the task, English, French, and Italian. For the second subtask, we used median averaging to construct an ensemble model. We ranked 15th out of 21 groups for subtask 1 (F1-score: 77.38%) and 11th out of 17 groups for subtask 2 (RHO: 0.078).
Learning Discriminative Representations for Open Relation Extraction with Instance Ranking and Label Calibration

Open relation extraction is the task to extract relational facts without pre-defined relation types from open-domain corpora.However, since there are some hard or semi-hard instances sharing similar context and entity information but belonging to different underlying relation, current OpenRE methods always cluster them into the same relation type.In this paper, we propose a novel method based on Instance Ranking and Label Calibration strategies (IRLC) to learn discriminative representations for open relation extraction.Due to lacking the original instance label, we provide three surrogate strategies to generate the positive, hard negative, and semi-hard negative instances for the original instance.Instance ranking aims to refine the relational feature space by pushing the hard and semi-hard negative instances apart from the original instance with different margins and pulling the original instance and its positive instance together.To refine the cluster probability distributions of these instances, we introduce a label calibration strategy to model the constraint relationship between instances.Experimental results on two public datasets demonstrate that our proposed method can significantly outperform the previous state-of-the-art methods 1
Helsinki-NLP at SemEval-2022 Task 2: A Feature-Based Approach to Multilingual Idiomaticity Detection

This paper describes the University of Helsinki submission to the SemEval 2022 task on multilingual idiomaticity detection.Our system utilizes several models made available by Hug-gingFace, along with the baseline BERT model for the task.We focus on feature engineering based on properties that characterize idiomatic expressions.The additional features lead to improvements over the baseline and the final submission achieves 15th place out of 20 submissions.The paper provides an error analysis of our model including visualisations of the contributions of individual features.
ZhichunRoad at SemEval-2022 Task 2: Adversarial Training and Contrastive Learning for Multiword Representations

This paper presents our contribution to the SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding.We explore the impact of three different pre-trained multilingual language models in the SubTaskA.By enhancing the model generalization and robustness, we use the exponential moving average (EMA) method and the adversarial attack strategy.In SubTaskB, we add an effective cross-attention module for modeling the relationships of two sentences.We jointly train the model with a contrastive learning objective and employ a momentum contrast to enlarge the number of negative pairs.Additionally, we use the alignment and uniformity properties to measure the quality of sentence embeddings.Our approach obtained competitive results in both subtasks.
JCT at SemEval-2022 Task 4-A: Patronism Detection in Posts Written in English using Preprocessing Methods and various Machine Leaerning Methods

In this paper, we describe our submissions to SemEval-2022 subtask 4-A - “Patronizing and Condescending Language Detection: Binary Classification”. We developed different models for this subtask. We applied 11 supervised machine learning methods and 9 preprocessing methods. Our best submission was a model we built with BertForSequenceClassification. Our experiments indicate that pre-processing stage is a must for a successful model. The dataset for Subtask 1 is highly imbalanced dataset. The f1-scores on the oversampled imbalanced training dataset were higher the results on the original training dataset.
JB132 submission to the SIGMORPHON 2022 Shared Task 3 on Morphological Segmentation

This paper describes the JB132 submission to the SIGMORPHON 2022 Shared Task 3 on Morpheme Segmentation.In this paper we describe probabilistic model trained with the Expectation-Maximization algorithm, we provide the results and analyze sources of errors and general limitations of our approach.The model was implemented within our own modular probabilistic framework.
High Tech team at SemEval-2022 Task 6: Intended Sarcasm Detection for Arabic texts

This paper presents our proposed methods for iSarcasmEval shared task.The shared task consists of three different subtasks.We participate in both subtask A and subtask C. The purpose of the subtask A was to predict if a text is sarcastic while the aim of subtask C is to determine which text is sarcastic given a sarcastic text and its non-sarcastic rephrase.Both of the developed solutions used BERT pre-trained models.The proposed models are optimized on simple objectives and easy to grasp.However, despite their simplicity our methods ranked 4 and 2 in iSarcasmEval subtask A and subtask C for Arabic texts.
Modeling Compositionality with Dependency Graph for Dialogue Generation

Because of the compositionality of natural language, syntactic structure which contains the information about the relationship between words is a key factor for semantic understanding.However, the widely adopted Transformer is hard to learn the syntactic structure effectively in dialogue generation tasks.To explicitly model the compositionaity of language in Transformer Block, we restrict the information flow between words by constructing directed dependency graph and propose Dependency Relation Attention (DRA).Experimental results demonstrate that DRA can further improve the performance of state-of-the-art models for dialogue generation.
Adversarial Text Normalization

Text-based adversarial attacks are becoming more commonplace and accessible to general internet users.As these attacks proliferate, the need to address the gap in model robustness becomes imminent.While retraining on adversarial data may increase performance, there remains an additional class of character-level attacks on which these models falter.Additionally, the process to retrain a model is time and resource intensive, creating a need for a lightweight, reusable defense.In this work, we propose the Adversarial Text Normalizer, a novel method that restores baseline performance on attacked content with low computational overhead.We evaluate the efficacy of the normalizer on two problem areas prone to adversarial attacks, i.e.Hate Speech and Natural Language Inference.We find that text normalization provides a task-agnostic defense against character-level attacks that can be implemented supplementary to adversarial retraining solutions, which are more suited for semantic alterations.
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning

Pre-trained language models (PLMs) can provide a good starting point for downstream applications.However, it is difficult to generalize PLMs to new tasks given a few labeled samples.In this work, we show that Relation Graph augmented Learning (RGL) can improve the performance of few-shot natural language understanding tasks.During learning, RGL constructs a relation graph based on the label consistency between samples in the same batch, and learns to solve the resultant node classification and link prediction problems on the relation graph.In this way, RGL fully exploits the limited supervised information, which can boost the tuning effectiveness.Extensive experimental results show that RGL consistently improves the performance of prompt-based tuning strategies.
Team dina at SemEval-2022 Task 8: Pre-trained Language Models as Baselines for Semantic Similarity

This paper describes the participation of the team "dina" in the Multilingual News Similarity task at SemEval 2022.To build our system for the task, we experimented with several multilingual language models which were originally pre-trained for semantic similarity but were not further fine-tuned.We use these models in combination with state-of-the-art packages for machine translation and named entity recognition with the expectation of providing valuable input to the model.Our work assesses the applicability of such "pure" models to solve the multilingual semantic similarity task in the case of news articles.Our best model achieved a score of 0.511, but shows that there is room for improvement.
Sapphire at SemEval-2022 Task 4: A Patronizing and Condescending Language Detection Model Based on Capsule Networks

This paper introduces the related work and the results of Team Sapphire's system for SemEval-2022 Task 4: Patronizing and Condescending Language Detection.We only participated in subtask 1.The task goal is to judge whether a news text contains PCL.This task can be considered as a task of binary classification of news texts.In this binary classification task, the BERT-base model is adopted as the pre-trained model used to represent textual information in vector form and encode it.Capsule networks is adopted to extract features from the encoded vectors.The official evaluation metric for subtask 1 is the F1 score over the positive class.Finally, our system's submitted prediction results on test set achieved the score of 0.5187.
Efficient Constituency Tree based Encoding for Natural Language to Bash Translation

Bash is a Unix command language used for interacting with the Operating System.Recent works on natural language to Bash translation have made significant advances, but none of the previous methods utilize the problem's inherent structure.We identify this structure and propose a Segmented Invocation Transformer (SIT) that utilizes the information from the constituency parse tree of the natural language text.Our method is motivated by the alignment between segments in the natural language text and Bash command components.Incorporating the structure in the modelling improves the performance of the model.Since such systems must be universally accessible, we benchmark the inference times on a CPU rather than a GPU.We observe a 1.8x improvement in the inference time and a 5x reduction in model parameters.Attribution analysis using Integrated Gradients reveals that the proposed method can capture the problem structure.
Domain Adaption for Knowledge Tracing

With the rapid development of online education system, knowledge tracing which aims at predicting students' knowledge state is becoming a critical and fundamental task in personalized education. Traditionally, existing methods are domain-specified. However, there are a larger number of domains (e.g., subjects, schools) in the real world and the lacking of data in some domains, how to utilize the knowledge and information in other domains to help train a knowledge tracing model for target domains is increasingly important. We refer to this problem as domain adaptation for knowledge tracing (DAKT) which contains two aspects: (1) how to achieve great knowledge tracing performance in each domain. (2) how to transfer good performed knowledge tracing model between domains. To this end, in this paper, we propose a novel adaptable framework, namely adaptable knowledge tracing (AKT) to address the DAKT problem. Specifically, for the first aspect, we incorporate the educational characteristics (e.g., slip, guess, question texts) based on the deep knowledge tracing (DKT) to obtain a good performed knowledge tracing model. For the second aspect, we propose and adopt three domain adaptation processes. First, we pre-train an auto-encoder to select useful source instances for target model training. Second, we minimize the domain-specific knowledge state distribution discrepancy under maximum mean discrepancy (MMD) measurement to achieve domain adaptation. Third, we adopt fine-tuning to deal with the problem that the output dimension of source and target domain are different to make the model suitable for target domains. Extensive experimental results on two private datasets and seven public datasets clearly prove the effectiveness of AKT for great knowledge tracing performance and its superior transferable ability.
Adaptive Expansion Bayesian Optimization for Unbounded Global  Optimization

Bayesian optimization is normally performed within fixed variable bounds. In cases like hyperparameter tuning for machine learning algorithms, setting the variable bounds is not trivial. It is hard to guarantee that any fixed bounds will include the true global optimum. We propose a Bayesian optimization approach that only needs to specify an initial search space that does not necessarily include the global optimum, and expands the search space when necessary. However, over-exploration may occur during the search space expansion. Our method can adaptively balance exploration and exploitation in an expanding space. Results on a range of synthetic test functions and an MLP hyperparameter optimization task show that the proposed method out-performs or at least as good as the current state-of-the-art methods.
Deeper Insights into Weight Sharing in Neural Architecture Search

With the success of deep neural networks, Neural Architecture Search (NAS) as a way of automatic model design has attracted wide attention. As training every child model from scratch is very time-consuming, recent works leverage weight-sharing to speed up the model evaluation procedure. These approaches greatly reduce computation by maintaining a single copy of weights on the super-net and share the weights among every child model. However, weight-sharing has no theoretical guarantee and its impact has not been well studied before. In this paper, we conduct comprehensive experiments to reveal the impact of weight-sharing: (1) The best-performing models from different runs or even from consecutive epochs within the same run have significant variance; (2) Even with high variance, we can extract valuable information from training the super-net with shared weights; (3) The interference between child models is a main factor that induces high variance; (4) Properly reducing the degree of weight sharing could effectively reduce variance and improve performance.
Spatial-Scale Aligned Network for Fine-Grained Recognition

Existing approaches for fine-grained visual recognition focus on learning marginal region-based representations while neglecting the spatial and scale misalignments, leading to inferior performance. In this paper, we propose the spatial-scale aligned network (SSANET) and implicitly address misalignments during the recognition process. Especially, SSANET consists of 1) a self-supervised proposal mining formula with Morphological Alignment Constraints; 2) a discriminative scale mining (DSM) module, which exploits the feature pyramid via a circulant matrix, and provides the Fourier solver for fast scale alignments; 3) an oriented pooling (OP) module, that performs the pooling operation in several pre-defined orientations. Each orientation defines one kind of spatial alignment, and the network automatically determines which is the optimal alignments through learning. With the proposed two modules, our algorithm can automatically determine the accurate local proposal regions and generate more robust target representations being invariant to various appearance variances. Extensive experiments verify that SSANET is competent at learning better spatial-scale invariant target representations, yielding superior performance on the fine-grained recognition task on several benchmarks.
Unpacking the evidence elasticity of digital traces

Digital traces are sought in most criminal investigations, due to their usefulness in shedding light on the case circumstances and evidential themes. They are often trusted to be value-neutral and credible. However, a recent study on digital forensic (DF) decision-making revealed that the human factor greatly influences the construction of digital evidence. The study found that DF practitioners were biased by contextual information and produced inconsistent results during DF casework. This article applies a qualitative lens to explore how the statistically determined variance materialises in DF reports. The article examines the role of interpretative flexibility when the DF practitioner constructs the digital evidence. It develops "evidence elasticity" as a concept for describing the mutability of digital traces as knowledge objects. The article explores the role of evidence elasticity for constructing narratives involving digital evidence and how this sometimes may result in misinformation with the propensity to mislead actors in the criminal justice chain, such as the investigators, prosecutors or judges.
Design of Library Information Systems for Collections Procurement at Hayam Wuruk Perbanas Library

&#x0D; &#x0D; &#x0D; &#x0D; Information disclosure is an opportunity and a challenge for libraries in managing information. Management of this information must be done quickly and precisely so that people can consume information according to their needs. Libraries take an important role in such conditions because the library as an information management organization has a strategic role so that the information circulating can be in accordance with the needs of the community. Up-to-date information is one way for the public to get appropriate information and this must be done by the library so that the existence and value of its benefits can be felt by the community. The community can take a role in supporting the updating of the information. One way to keep the information up to date is to adjust the collection of library materials by conducting it. The approach that is owned by the library is to monitor the process of procuring a collection of library materials carried out by the library. Librarians can build an information system design that can accommodate these activities. The research method used in this article is a research with a qualitative descriptive approach in the form of a case study. This article aims to offer an information system for the procurement of library material collections carried out by the Hayam Wuruk Perbanas University Surabaya Library. The result is a proposed information system for the procurement of a new collection of library materials with an open nature so that all stages or the process of procuring a collection of library materials can be monitored together and users can find out the up-to-date collection of library materials owned by the library.&#x0D; &#x0D; &#x0D; &#x0D;
The Lifelong Educational Meaning Found in Desire for Change and Life Change of People who Found Their Next Jobs

The purpose of this study is to explore the source of the participants desire to change when they meet a life event called planned coincidence and to explore the value they seek. By analyzing the interview data of five people, the‘desire for change' that appeared in their conversion process was analyzed, and the relationship with lifelong education was explored by examining change and growth. First, the essential‘desire' formed in the relationship with others called ‘accidental event' is a challenge to dreams. It can be said to be a‘desire for change', which means a new start to creating a path and discovering action energy. Reasons for experience, reflective reflection in the process of job transition, and participation in new learning promote growth. The second factor of‘life transition' is‘accidental event'. Their challenges are the driving forces that lead to ability, competency development, change and growth, and it was found that they established self-inspection and cognitive strategies in a given environment, and established a sense of purpose by looking at events positively. Job transition fosters a macroscopic eye and the power to look at oneself, and allows them to find goals and identities for their jobs. In addition, they make their own culture and beliefs, recognize others, attach meaning to the role for shared growth, establish a sense of purpose, and arbitrarily choose learning to create new values. Humans seek change to achieve happy lives in various environments and pursue internal change with open and more mature approach. In short, transitional learning that helps inner change is significant in adult learning. In addition, participation in learning to achieve the constant learning desire that appears by constantly seeking a new life transition has lifelong educational value.
Safety State Assessment of Network Control System Based on Belief Rule Base

The current network control system (NCS) cannot assess the system safety state in a timely, comprehensive, and accurate manner, which leads to serious security problems in NCS. Directing at the defects of existing security state assessment models for the NCS, a method of safety state assessment of the NCS based on the belief rule base (BRB) expert system is proposed in this paper. Firstly, the expert system of the BRB is used to combine qualitative knowledge with quantitative monitoring data. Then, the evidential reasoning (ER) algorithm is used for knowledge reasoning, and the initial parameters of BRB model are optimized. Finally, taking the data of a gas NCS as an example, the experimental results illustrate that the assessment accuracy is higher than that of back propagation (BP) and support vector machines (SVM) evaluation models.
Digital Forensics In Multimedia

Digital forensics and multimedia forensics are rapidly growing disciplines where electronic information is extracted and interpreted using scientifically accepted and validated processes, to be used in and outside of a court of law. As personal computing and the internet becomes more widespread, these two fields are becoming increasingly important in law enforcement and cybercrime investigation.Digital forensics involves investigating computer systems and digital artefacts in general, while multimedia forensics is a sub-topic of digital forensics which focuses on extracting and analyzing contents such as images, videos, and audio to produce forensic evidence from both regular computer systems and special multimedia devices, such as digital cameras, voice recorders etc. This paper seeks to shed some light on digital forensics in multimedia, methods of authentication and challenges. Keywords: Forensics, Multimedia, Scrutiny, Analysis, Video, Voice, Data, Camera, Authentication BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: Frey John Wisdom (2022): Digital Forensics In Multimedia Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 239-250 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P39