Reviewerly/exHarmony huggingface dataset:
Seq2Pat: Sequence-to-Pattern Generation for Constraint-Based Sequential Pattern Mining

Pattern mining is an essential part of knowledge discovery and data analytics. It is a powerful paradigm, especially when combined with constraint reasoning. In this paper, we present Seq2Pat, a constraint-based sequential pattern mining tool with a high-level declarative user interface. The library finds patterns that frequently occur in large sequence databases subject to constraints. We highlight key benefits that are desirable, especially in industrial settings where scalability, explainability, rapid experimentation, reusability, and reproducibility are of great interest. We then showcase an automated feature extraction process powered by Seq2Pat to discover high-level insights and boost downstream machine learning models for customer intent prediction.
RRL: Regional Rotate Layer in Convolutional Neural Networks

Convolutional Neural Networks (CNNs) perform very well in image classification and object detection in recent years, but even the most advanced models have limited rotation invariance. Known solutions include the enhancement of training data and the increase of rotation invariance by globally merging the rotation equivariant features. These methods either increase the workload of training or increase the number of model parameters. To address this problem, this paper proposes a module that can be inserted into the existing networks, and directly incorporates the rotation invariance into the feature extraction layers of the CNNs. This module does not have learnable parameters and will not increase the complexity of the model. At the same time, only by training the upright data, it can perform well on the rotated testing set. These ad-vantages will be suitable for fields such as biomedicine and astronomy where it is difficult to obtain upright samples or the target has no directionality. Evaluate our module with LeNet-5, ResNet-18 and tiny-yolov3, we get impressive results.
Design and Development of a Scale for Evaluating the Acceptance of Social Robotics for Older People: The Robot Era Inventory

Nowadays, several robots have been developed to provide not only companionship to older adults, but also to cooperate with them during health and lifestyle activities. Despite the undeniable wealth of socially assistive robots (SARs), there is an increasing need to customize the tools used for measuring their acceptance in real-life applications.Within the Robot-Era project, a scale was developed to understand the degree of acceptance of the robotic platform. A preliminary test with 21 participants was performed to assess the statistical validity of the Robot-Era Inventory (REI) scales.Based on the criteria observed in the literature, 41 items were developed and grouped in different scales (perceived robot personality, human-robot interaction, perceived benefit, ease of use, and perceived usefulness). The reliability of the Robot-Era Inventory scale was analyzed with Cronbach's alpha, with a mean value of 0.79 (range = 0.61-0.91). Furthermore, the preliminary validity of this scale has been tested by using the correlation analysis with a gold standard, the Unified Theory of Acceptance and Use of Technology (UTAUT) model.The Robot-Era Inventory represents a useful tool that can be easily personalized and included in the assessment of any SARs that cooperate with older people in real environment applications.
SAR Image Classification with Knowledge Distillation and Class Balancing for Long-Tailed Distributions

The classification of data following long-tailed distributions is a challenging problem that is encountered in practical situations. In the case of Synthetic Aperture Radar (SAR) image classification, the problem is even more difficult due to noisy data and lack of large datasets for training. In this paper, we explore a cross-modal framework for knowledge distillation from Electro-Optical (EO) data to SAR data, with class balancing considerations. Our methodology leverages coupled EO-SAR data and involves the following stages: (1) EO network training, (2) SAR network training with response-based knowledge distillation from the EO network, and (3) class balanced training of the SAR network to account for long-tailed distributions in the data. Our model can be used with a different network backbones without placing any constraints on the network architectures for EO or SAR. Class balancing is utilized during training to deal with imbalance across classes and an equal diversity loss is considered for improved training. We test our approach on an EO-SAR coupled dataset that is highly imbalanced and demonstrate performance gains in each stage of our model.
Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection

In the recent research era, artificial intelligence techniques have been used for computer vision, big data analysis, and detection systems. The development of these advanced technologies has also increased security and privacy issues. One kind of this issue is Deepfakes which is the combined word of deep learning and fake. DeepFake refers to the formation of a fake image or video using artificial intelligence approaches which are created for political abuse, fake data transfer, and pornography. This paper has developed a Deepfake detection method by examining the computer vision features of the digital content. The computer vision features based on the frame change are extracted using a proposed deep learning model called the Cascaded Deep Sparse Auto Encoder (CDSAE) trained by temporal CNN. The detection process is performed using a Deep Neural Network (DNN) to classify the deep fake image/video from the real image/video. The proposed model is implemented using Face2Face, FaceSwap, and DFDC datasets which have secured an improved detection rate when compared to the traditional deep fake detection approaches.
Developing a Syntactic and Semantic Annotation Tool for Research on Chinese Vocabulary

Syntax and semantics are of great importance for vocabulary research. Large-scale corpus-based studies concerning the syntactic and semantic features of Chinese vocabulary are needed. To fill the gap, this study develops a syntactic and semantic annotation tool based on dependency grammar, and delves into the syntax and semantics of the most frequently used disyllabic visual verb kànjiàn ‘see’ as an example. First, this study developed a syntactic and semantic annotation tool of dependency grammar. Second, this study extracted and selected simple sentences with kànjiàn from large-scale corpora. Third, the tool was used to conduct syntactic and semantic dependency tagging and manual checking. Results show that kànjiàn can appear in six types of syntactic dependencies as a dependent, namely, Head (predicate), Attribute, Coordination, Verb-Object, and Subject-Verb. Moreover, the most frequent syntactic function of kànjiàn is being a predicate, which accounts for 85.4%. The semantic roles that kànjiàn collocates with fall into five categories, which are CONTENT, EXPERIENCER, PLACE, TIME, and MANNER. The two most frequent ones are CONTENT and EXPERIENCER. Further, this paper compared the results with previous Chinese resources. The outcomes of this study prove the importance of the syntactic and semantic annotation tool in analyzing Chinese vocabulary and help us have a deep understanding of the characteristics of Chinese visual verbs.
NAYEL @LT-EDI-ACL2022: Homophobia/Transphobia Detection for Equality, Diversity, and Inclusion using SVM

Analysing the contents of social media platforms such as YouTube, Facebook and Twitter gained interest due to the vast number of users.One of the important tasks is homophobia/transphobia detection.This paper illustrates the system submitted by our team for the homophobia/transphobia detection in social media comments shared task.A machine learning-based model has been designed and various classification algorithms have been implemented for automatic detection of homophobia in YouTube comments.TF-IDF has been used with a range of bigram models for vectorization of comments.Support Vector Machines have been used to develop the proposed model and our submission reported 0.91, 0.92, 0.88 weighted F1-scores for English, Tamil and Tamil-English datasets respectively.
Pretrained Knowledge Base Embeddings for improved Sentential Relation Extraction

In this work we put forward to combine pretrained knowledge base graph embeddings with transformer based language models to improve performance on the sentential Relation Extraction task in natural language processing.Our proposed model is based on a simple variation of existing models to incorporate off-task pre-trained graph embeddings with an on-task finetuned BERT encoder.We perform a detailed statistical evaluation of the model on standard datasets.We provide evidence that the added graph embeddings improve the performance, making such a simple approach competitive with the state-of-the-art models that perform explicit on-task training of the graph embeddings.Furthermore, we observe for the underlying BERT model an interesting power-law scaling behavior between the variance of the F1 score obtained for a relation class and its support in terms of training examples.
A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms

Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region's cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain for cleaning is non-viable. Alternatively, a path planning approach to gathering dirt samples selectively at locations with a high likelihood of dirt accumulation is more feasible. This work presents a first-of-its-kind dirt sample gathering strategy for the cleaning auditing robots by combining the geometrical feature extraction and swarm algorithms. This combined approach generates an efficient optimal path covering all the identified dirt locations for efficient cleaning auditing. Besides being the foundational effort for cleaning audit, a path planning approach considering the geometric signatures that contribute to the dirt accumulation of a region has not been device so far. The proposed approach is validated systematically through experiment trials. The geometrical feature extraction-based dirt location identification method successfully identified dirt accumulated locations in our post-cleaning analysis as part of the experiment trials. The path generation strategies are validated in a real-world environment using an in-house developed cleaning auditing robot BELUGA. From the experiments conducted, the ant colony optimization algorithm generated the best cleaning auditing path with less travel distance, exploration time, and energy usage.
Unified DeepLabV3+ for Semi-Dark Image Semantic Segmentation

Semantic segmentation for accurate visual perception is a critical task in computer vision. In principle, the automatic classification of dynamic visual scenes using predefined object classes remains unresolved. The challenging problems of learning deep convolution neural networks, specifically ResNet-based DeepLabV3+ (the most recent version), are threefold. The problems arise due to (1) biased centric exploitations of filter masks, (2) lower representational power of residual networks due to identity shortcuts, and (3) a loss of spatial relationship by using per-pixel primitives. To solve these problems, we present a proficient approach based on DeepLabV3+, along with an added evaluation metric, namely, Unified DeepLabV3+ and S3core, respectively. The presented unified version reduced the effect of biased exploitations via additional dilated convolution layers with customized dilation rates. We further tackled the problem of representational power by introducing non-linear group normalization shortcuts to solve the focused problem of semi-dark images. Meanwhile, to keep track of the spatial relationships in terms of the global and local contexts, geometrically bunched pixel cues were used. We accumulated all the proposed variants of DeepLabV3+ to propose Unified DeepLabV3+ for accurate visual decisions. Finally, the proposed S3core evaluation metric was based on the weighted combination of three different accuracy measures, i.e., the pixel accuracy, IoU (intersection over union), and Mean BFScore, as robust identification criteria. Extensive experimental analysis performed over a CamVid dataset confirmed the applicability of the proposed solution for autonomous vehicles and robotics for outdoor settings. The experimental analysis showed that the proposed Unified DeepLabV3+ outperformed DeepLabV3+ by a margin of 3% in terms of the class-wise pixel accuracy, along with a higher S3core, depicting the effectiveness of the proposed approach.
Quantification of the urbanization impacts on solar dimming and brightening over China

Abstract Metropolis’ contribution (anthropogenic aerosols) to solar dimming and brightening remains a hot topic of special concern over the past several decades. However, urbanization effects on surface incident solar radiation ( R s ) have not been comprehensively investigated. In this study, the urbanization effects on solar dimming and brightening were addressed using the densely distributed reconstructed R s data at 375 stations and 92 urban–rural station pairs over the time period of 1960–2019 in China. The results indicate that the impacts of urbanization on the monthly mean R s is 0.86 ± 7.99 W m −2 during the study period, while the impact is 0.90 ± 8.30 W m −2 and 0.82 ± 8.26 W m −2 for the solar dimming (1960–1992) and brightening (1992–2019) phase, respectively. The urbanization effects on the trend of R s is −0.39 and 0.16 W m −2 per decade during dimming and brightening, respectively. It also found that urbanization effects on R s trend differs strikingly in magnitudes for specific regions in China. Generally, urbanization speeds up China’s dimming in the dimming phase and slows down China’s brightening in the brightening phase.
Mahalanobis Distance Based K-Means Clustering

In the current era, big data are everywhere. With advances in technology, high volumes of a wide variety of data are generated and collected in numerous real-life applications and services. Embedded in these big data is implicit, previously unknown and potentially useful information and knowledge. This calls for data science. Among various data science tasks, clustering is an important one. Although there have been techniques to improve the accuracy of k-means clustering algorithms, many of them are applied independently. In this paper, we present a k-means clustering algorithm with Mahalanobis distance. This is a non-trivial integration of partitioning based clustering, correlation based clustering, and Mahalanobis distance. Evaluation results show our algorithm is more accurate than the related works to cluster similar data.
When a sentence does not introduce a discourse entity, Transformer-based models still sometimes refer to it

Understanding longer narratives or participating in conversations requires tracking of discourse entities that have been mentioned.Indefinite noun phrases (NPs), such as a dog, frequently introduce discourse entities but this behavior is modulated by sentential operators such as negation.For example, a dog in Arthur doesn't own a dog does not introduce a discourse entity due to the presence of negation.In this work, we adapt the psycholinguistic assessment of language models paradigm to higher-level linguistic phenomena and introduce an English evaluation suite that targets the knowledge of the interactions between sentential operators and indefinite NPs.We use this evaluation suite for a fine-grained investigation of the entity tracking abilities of the Transformer-based models GPT-2 and GPT-3.We find that while the models are to a certain extent sensitive to the interactions we investigate, they are all challenged by the presence of multiple NPs and their behavior is not systematic, which suggests that even models at the scale of GPT-3 do not fully acquire basic entity tracking abilities.
Cross-Language Transfer of High-Quality Annotations: Combining Neural Machine Translation with Cross-Linguistic Span Alignment to Apply NER to Clinical Texts in a Low-Resource Language

In this work, cross-linguistic span prediction based on contextualized word embedding models is used together with neural machine translation (NMT) to transfer and apply the stateof-the-art models in natural language processing (NLP) to a low-resource language clinical corpus.Two directions are evaluated: (a) English models can be applied to translated texts to subsequently transfer the predicted annotations to the source language and (b) existing high-quality annotations can be transferred beyond translation and then used to train NLP models in the target language.Effectiveness and loss of transmission is evaluated using the German Berlin-Tübingen-Oncology Corpus (BRONCO) dataset with transferred external data from NCBI disease, SemEval-2013 drug-drug interaction (DDI) and i2b2/VA 2010 data.The use of English models for translated clinical texts has always involved attempts to take full advantage of the benefits associated with them (large pre-trained biomedical word embeddings).To improve advances in this area, we provide a general-purpose pipeline to transfer any annotated BRAT or CoNLL format to various target languages.For the entity class medication, good results were obtained with 0.806 F 1-score after re-alignment.Limited success occurred in the diagnosis and treatment class with results just below 0.5 F 1-score due to differences in annotation guidelines.
Retrieval-augmented Generation across Heterogeneous Knowledge

Retrieval-augmented generation (RAG) methods have been receiving increasing attention from the NLP community and achieved stateof-the-art performance on many NLP downstream tasks.Compared with conventional pretrained generation models, RAG methods have remarkable advantages such as easy knowledge acquisition, strong scalability, and low training cost.Although existing RAG models have been applied to various knowledge-intensive NLP tasks, such as open-domain QA and dialogue systems, most of the work has focused on retrieving unstructured text documents from Wikipedia.In this paper, I first elaborate on the current obstacles to retrieving knowledge from a single-source homogeneous corpus.Then, I demonstrate evidence from both existing literature and my experiments, and provide multiple solutions on retrieval-augmented generation methods across heterogeneous knowledge.
A framework for the digital competencies for teaching in science education – DiKoLAN

Abstract In order to systematically introduce (prospective) teachers to teaching in the digital world and to lay the foundations for concepts that are meaningful in terms of physics education, essential aspects of learning with and via digital media must be conveyed. In addition to cross-curricular competencies, it is essential to consider subject-specific competencies for the natural sciences since the formation of subject-specific knowledge on the use of digital media in the classroom (TPACK) is the decisive factor for acceptance behaviour and the intention for later use. The questions remain as to what core competencies must be built up by all (prospective) science teachers by the end of their studies and in which sections of university teacher training this should take place. Since no competence frameworks specific to the teaching of science were previously available, nine researchers from eight universities came together to develop a suitable orientation framework. As a result, this contribution presents the structured and graded framework “DiKoLAN – Digitale Kompetenzen für das Lehramt in den Naturwissenschaften [Digital Competencies for Teaching in Science Education]” (see https://dikolan.de).
Blockchain-Based Service Orchestration for 5G Vertical Industries in Multicloud Environment

Blockchain technologies are gradually being deployed in a variety of industries, including telecommunications. In this paper, due to the strict governance of telecommunication infrastructure, we propose a permissioned distributed ledger (PDL)-based blockchain supported architecture for a network management and orchestration platform. The work focuses on creating a trusted environment for both Cloud Service Providers (CSPs) and Mobile Network Operators (MNOs) for managing the lifecycle of network services (e.g., instantiation, scaling, termination, etc.) in a multi-cloud environment. We also validate our proposed approach with an experimental scenario using the Quorum blockchain network (BCN) to measure various performance metrics (e.g., number of transactions and blocks, time to write, and transactions per second) of different service orchestrator (SO)-related instantiation metrics. Our evaluation results show that the values for the service instantiation time and the corresponding BCN metrics can be completely different, suggesting that some logs arrive very quickly and generate a high transaction load, while others take longer and generate a low number of transactions. As a solution, at the end of the paper, we also provide some recommendations for appropriate optimizations during transfer of SO-related logs to BCNs and some observed challenges.
Framework Authentication e-document using Blockchain Technology on the Government system

As a sophisticated platform, namely Blockchain, which has 3 (three) potentials to change the governance system which is still considered traditional, solve the problem of principal agents, and minimize the crime of document falsification. However, in the government sector, the documents used can be insecure and lead to document falsification. Blockchain is becoming increasingly significant in document services and beyond until questions arise about the authenticity and security of manuscripts and documents in the government sector. So, Go-Chain (Government Blockchain) it is necessary to authenticate documents using Blockchain to minimize document forgery. By utilizing the potential of Blockchain technology, this research aims to maximize government e-documents in a modern and secure manner. Propose a Blockchain-based document framework method that is applied with a literature review study—in addition to ensuring the speed of system execution by utilizing DAO (Decentralized Autonomous Organization) and Smart Contracts. The result is that modern and safe government e-documents in document verification can significantly maintain transparency and increase trust in public services.