Reviewerly/exHarmony huggingface dataset:
Image Security Retrieval Based on Chaotic Algorithm and Deep Learning

At present, most image retrieval methods are based on plain-text images which poses a threat to some professional fields, such as medicine, military, and finance. In order to achieve greater security for the network transmission security of the image, we establish a deep artificial neural network model to extract features by sample training. Then an image-encryption algorithm that matches and secures image retrieval is designed and integrated into an image-retrieval process based on deep learning. Experiments on multiple authoritative datasets show that the proposed algorithm can not only achieve secure retrieval of ciphertext images, but also improve retrieval efficiency obviously. Specifically, the experimental results on five data sets show that compared with the average performance of 16 comparison algorithms, each evaluation indicator has been significantly improved in our research, with <i>Pre</i> increased by 12.54% - 88.20%, <i>Rec</i> increased by 1.46% - 10.95%, <i>F1</i> increased by 2.86% - 13.55% and <i>mAP</i> increased by 16.64% - 82.47%. Futhermore, the successful realization of the ciphertext retrieval provides some reference for the information security retrieval research.
Extending the Scope of Out-of-Domain: Examining QA models in multiple subdomains

Past work that investigates out-of-domain performance of QA systems has mainly focused on general domains (e.g.news domain, wikipedia domain), underestimating the importance of subdomains defined by the internal characteristics of QA datasets.In this paper, we extend the scope of "out-of-domain" by splitting QA examples into different subdomains according to their internal characteristics including question type, text length, answer position.We then examine the performance of QA systems trained on the data from different subdomains.Experimental results show that the performance of QA systems can be significantly reduced when the train data and test data come from different subdomains.These results question the generalizability of current QA systems in multiple subdomains, suggesting the need to combat the bias introduced by the internal characteristics of QA datasets.
Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation

We introduce Doctor XAvIer -a BERT-based diagnostic system that extracts relevant clinical data from transcribed patient-doctor dialogues and explains predictions using feature attribution methods.We present a novel performance plot and evaluation metric for feature attribution methods -Feature Attribution Dropping (FAD) curve and its Normalized Area Under the Curve (N-AUC).FAD curve analysis shows that integrated gradients outperforms Shapley values in explaining diagnosis classification.Doctor XAvIer outperforms the baseline with 0.97 F1-score in named entity recognition and symptom pertinence classification and 0.91 F1score in diagnosis classification.
A Comparison of Strategies for Source-Free Domain Adaptation

Data sharing restrictions are common in NLP, especially in the clinical domain, but there is limited research on adapting models to new domains without access to the original training data, a setting known as source-free domain adaptation.We take algorithms that traditionally assume access to the source-domain training data-active learning, self-training, and data augmentation-and adapt them for source-free domain adaptation.Then we systematically compare these different strategies across multiple tasks and domains.We find that active learning yields consistent gains across all SemEval 2021 Task 10 tasks and domains, but though the shared task saw successful self-trained and data augmented models, our systematic comparison finds these strategies to be unreliable for source-free domain adaptation.
Operations Smart Contract to Realize Decentralized System Operations Workflow for Consortium Blockchain

Enterprises have paid attention to consortium blockchains like Hyperledger Fabric, which is one of the most promising platforms, for efficient decentralized transactions without depending on any particular organization. A consortium blockchain-based system will be typically built across multiple organizations. In such blockchain-based systems, system operations across multiple organizations in a decentralized manner are essential to maintain the value of introducing consortium blockchains. Decentralized system operations have recently been becoming realistic with the evolution of consortium blockchains. For instance, the release of Hyperledger Fabric v2.x, in which individual operational tasks for a blockchain network, such as command execution of configuration change of channels (Fabric's sub-networks) and upgrade of chaincodes (Fabric's smart contracts), can be partially executed in a decentralized manner. However, the operations workflows also include the preceding procedure of pre-sharing, coordinating, and pre-agreeing the operational information (e.g., configuration parameters) among organizations, after which operation executions can be conducted, and this preceding procedure relies on costly manual tasks. To realize efficient decentralized operations workflows for consortium blockchain-based systems in general, we propose a decentralized inter-organizational operations method that we call Operations Smart Contract (OpsSC), which defines an operations workflow as a smart contract. Furthermore, we design and implement OpsSC for blockchain network operations with Hyperledger Fabric v2.x. This paper presents OpsSC for operating channels and chaincodes, which are essential for managing the blockchain networks, through clarifying detailed workflows of those operations. A cost evaluation based on an estimation model shows that the total operational cost for executing a typical operational scenario to add an organization to a blockchain network having ten organizations could be reduced by 54 percent compared with a conventional script-based method. The implementation of OpsSC has been open-sourced and registered as one of Hyperledger Labs projects, which hosts experimental projects approved by Hyperledger.
Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking

Online escort advertisement websites are widely used for advertising victims of human trafficking.Domain experts agree that advertising multiple people in the same ad is a strong indicator of trafficking.Thus, extracting person names from the text of these ads can provide valuable clues for further analysis.However, Named-Entity Recognition (NER) on escort ads is challenging because the text can be noisy, colloquial and often lacking proper grammar and punctuation.Most existing state-of-the-art NER models fail to demonstrate satisfactory performance in this task.In this paper, we propose NEAT (Name Extraction Against Trafficking) for extracting person names.It effectively combines classic rule-based and dictionary extractors with a contextualized language model to capture ambiguous names (e.g penny, hazel) and adapts to adversarial changes in the text by expanding its dictionary.NEAT shows 19% improvement on average in the F1 classification score for name extraction compared to previous state-of-the-art in two domain-specific datasets.
Phenomenon of pedagogical reflection in the context of system-thought-activity approach

In the modern world great importance is attached to the formation of a specialist&amp;apos;s focus on constant professional growth, self-learning, critical thinking and design skills. This necessitates the development of the reflexive activity of teachers. Pedagogical activity is complex in structure and content, refers to dynamic professions which require a high level of understanding of their activities and the activities of the subjects of the educational process. The growth of the teacher’s professional skills is not possible without a sufficient level of formation of pedagogical reflection. The article is devoted to the analysis of the phenomenon of pedagogical reflection. On the basis of theoretical analysis, the genesis of the concept of reflection, its representation in pedagogy, psychology and philosophy is revealed. Special attention is given to the consideration of the didactic potential of reflection in pedagogical activity.
Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking

Entity linking (EL) is the task of linking entity mentions in a document to referent entities in a knowledge base (KB).Many previous studies focus on Wikipedia-derived KBs.There is little work on EL over Wikidata, even though it is the most extensive crowdsourced KB.The scale of Wikidata can open up many new real-world applications, but its massive number of entities also makes EL challenging.To effectively narrow down the search space, we propose a novel candidate retrieval paradigm based on entity profiling.Wikidata entities and their textual fields are first indexed into a text search engine (e.g., Elasticsearch).During inference, given a mention and its context, we use a sequence-tosequence (seq2seq) model to generate the profile of the target entity, which consists of its title and description.We use the profile to query the indexed search engine to retrieve candidate entities.Our approach complements the traditional approach of using a Wikipedia anchor-text dictionary, enabling us to further design a highly effective hybrid method for candidate retrieval.Combined with a simple cross-attention reranker, our complete EL framework achieves state-of-the-art results on three Wikidata-based datasets and strong performance on TACKBP-2010 1 .
Flexible and cracked polymer membrane for thermal-expansive reversible fuses using three-dimensional groove patterns

In this study, a novel, facile, and cost-efficient manufacturing process for thermal-expansive reversible fuse (TRF) was demonstrated by using a cracked conductive layer on a flexible polymer with three-dimensional (3D) groove patterns.The current cut-off mechanism was demonstrated by the actuation of a thin membrane with a conductive layer of the TRF fabricated by a 3D-patterned mold and flexible polymer.When external heat was induced, the membrane of the TRF swelled, and the platinum-coated conductive layer was stretched, resulting in a current cut-off.In contrast, the membrane shrank, and the TRF was reconnected when the heat source was removed.The major cracks parallel to the 3D-printed patterns and minor cracks across the patterns were analyzed through the scanning electron microscope images.In addition, TRFs with pattern intervals of 250 µm (low resolution) and 100 µm (high resolution) were characterized by observing the current signal and expansion thickness of the membrane simultaneously to analyze the effect of actuation on the cut-off tendency.Finally, by applying the repetitive temperature profile between 35 °C and the cut-off temperature, the reversible performance of the TRFs was demonstrated by the cut-off and reconnection processes.These results can be applied to passive cooling systems of electronic devices to prevent overheating, which can affect the performance and durability of the device.
MUCIC@LT-EDI-ACL2022: Hope Speech Detection using Data Re-Sampling and 1D Conv-LSTM

Spreading positive vibes or hope content on social media may help many people to get motivated in their life.To address Hope Speech detection in YouTube comments, this paper presents the description of the models submitted by our team -MUCIC, to the Hope Speech Detection for Equality, Diversity, and Inclusion (HopeEDI) shared task at Association for Computational Linguistics (ACL) 2022.This shared task consists of texts in five languages, namely: English, Spanish (in Latin scripts), and Tamil, Malayalam, and Kannada (in code-mixed native and Roman scripts) with the aim of classifying the YouTube comment into "Hope", "Not-Hope" or "Not-Intended" categories.The proposed methodology uses the re-sampling technique to deal with imbalanced data in the corpus and obtained 1 st rank for English language with a macro-averaged F1-score of 0.550 and weighted-averaged F1-score of 0.860.The code to reproduce this work is available in GitHub 1 .
Distributed Extended Object Tracking Using Coupled Velocity Model From WLS Perspective

This paper proposes a coupled velocity model (CVM) that establishes the relation between the orientation and velocity using their correlation, avoiding that the existing extended object tracking (EOT) models treat them as two independent quantities. As a result, CVM detects the mismatch between the prior dynamic model and actual motion pattern to correct the filtering gain, and simultaneously becomes a nonlinear and state-coupled model with multiplicative noise. The paper considers CVM to design a feasible distributed weighted least squares (WLS) filter. The WLS criterion requires that the state-space model is a linear model with additive noise about the estimated state. To meet the requirement, we derive such two separate pseudo-linearized models by using the first-order Taylor series expansion. The separation is merely in form, and the estimates of interested states are embedded as parameters into each other's model, which implies that their interdependency is still preserved in the iterative operation of two linear filters. With the two models, we first propose a centralized WLS filter by converting the measurements from all nodes into a summation form. Then, a distributed consensus scheme, which directly performs an inner iteration on the priors across different nodes, is proposed to incorporate the cross-covariances between nodes. Under the consensus scheme, a distributed WLS filter over a realistic network with naive node is developed by proper weighting of the priors and measurements. Finally, the performance of proposed filters in terms of accuracy, robustness, and consistency is testified under different priors situations.
Predicting Activities of Daily Living for the Coming Time Period in Smart Homes

Activity prediction aims to predict what activities will occur in the future. In smart homes, to facilitate the daily living of the residents, automated or assistive services are provided. To provide these services, the ability of activity prediction is necessary. When we make a prediction, most of the existing works focus on predicting information about the next activity. However, in a smart home environment, compared with just predicting information about the next activity, another type of activity prediction problem has more practical value: predicting what activities will occur in the coming time period of a certain length. The necessity of this type of prediction is due to the purpose of the smart homes and the character of the activities. Many activities in smart homes need preparation time before being performed. Through this type of prediction, activities could be predicted sufficient time before being performed, and there will be adequate time for the smart home system to prepare corresponding automated or assistive services. As more than one activity could occur within the time period in which the prediction is made, this problem is a multilabel classification problem. In this article, we first give a formulation of the problem. Then, we propose a deep learning model to address it. The proposed model consists of the convolutional part, the long short-term memory layer, and the multilabel output layer. Experiments on real-world datasets show the effectiveness of our model.
Balancing a Practical Inverted Pendulum Model Employing Novel Meta-Heuristic Optimization-based Fuzzy Logic Controllers

This paper concentrates on proposing a newly effective control approach to ensure the balance of an inverted pendulum (IP) system consisting of a free-rotational rod and a small cart.The novel idea is to create an effective integration between a PD-like fuzzy logic architecture and a modified genetic algorithm (mGA).The mGA is executed with an appropriately fast optimization as an initial phase in dealing with optimizing scaling factors of the fuzzy logic controller.There are totally six meaningful scaling factors corresponding to two fuzzy logic controllers applied in the balance control system of the IP.These six scaling coefficients are highly significant to strongly affect control quality of the system applying such a PD-like fuzzy logic control methodology.Excellent results obtained in terms of numerical simulations compared with those of both the conventional PID and existed fuzzy logic counterparts as well as practical experiments implemented in a real IP topology verify the promising applicability of the new control methodology proposed in this study.
AdapLeR: Speeding up Inference by Adaptive Length Reduction

Pre-trained language models have shown stellar performance in various downstream tasks.But, this usually comes at the cost of high latency and computation, hindering their usage in resource-limited settings.In this work, we propose a novel approach for reducing the computational cost of BERT with minimal loss in downstream performance.Our method dynamically eliminates less contributing tokens through layers, resulting in shorter lengths and consequently lower computational cost.To determine the importance of each token representation, we train a Contribution Predictor for each layer using a gradient-based saliency method.Our experiments on several diverse classification tasks show speedups up to 22x during inference time without much sacrifice in performance.We also validate the quality of the selected tokens in our method using human annotations in the ERASER benchmark.In comparison to other widely used strategies for selecting important tokens, such as saliency and attention, our proposed method has a significantly lower false positive rate in generating rationales.Our code is freely available at https://github.com/amodaresi/AdapLeR.
A Transformational Biencoder with In-Domain Negative Sampling for Zero-Shot Entity Linking

Recent interest in entity linking has focused in the zero-shot scenario, where at test time the entity mention to be labelled is never seen during training, or may belong to a different domain from the source domain.Current work leverage pre-trained BERT has the implicit assumption that BERT bridges the gap between the source and target domain distributions.However, finetuned BERT has a considerable underperformance at zero-shot when applied in a different domain.We solve this problem by proposing a Transformational Biencoder that incorporates a transformation into BERT to perform a zeroshot transfer from the source domain during training.As like previous work, we rely on negative entities to encourage our model to discriminate the golden entities during training.To generate these negative entities, we propose a simple but effective strategy that takes the domain of the golden entity into perspective.Our experimental results on the benchmark dataset Zeshel show effectiveness of our approach and achieve new state-of-the-art.
Improving Computational Thinking in Nursing Students through Learning Computer Programming

Computational thinking is a fundamental skill for problem-solving, it uses computational concepts and other types of thinking such as algorithmic.The experience of improving computational thinking in nursing students using block-based programming environments such as Code.org,Lightbot, and the Python textual programming language is described.The results obtained are analyzed by applying a pre-and post-test of computational thinking to the students.The methodological design is quasi-experimental since it did not work with a control group.The study group was made up of 30 students from the Professional School of Nursing of the National University of San Agustin de Arequipa.The results show that teaching programming allows the understanding of computational concepts and improves computational thinking.It is concluded that block-based programming environments and the Python language facilitate the development of algorithmic thinking and computational thinking.
VIHS with ROTR Technique for Enhanced Light-Weighted Cryptographic System

Developing a bypass parallel processing block is one of the emerging and exciting research areas in the system encrypt/decrypt application areas.A Partial Pseudo-Randombased Hashing VIHS is the most suitable methodology for designing the system to encrypt/solve a block in cryptography.For this purpose, various VIHS and register techniques have been developed to process the storage system data.But, it is limited by the problems of reduced efficiency, increased computational complexity, high area consumption, and cost consumption.Thus, this research intends to develop a novel dynamic system register with hashing with optimal Hash Signature design to process the system's encryption /decrypt data.The main intention of this paper is to analyze the transfer characteristics of the current based on the pseudo-differential pair for a proficient system detection.Then, a system window can be created and adjusted to obtain an optimized power flow with less data loss sensitivity.The major stages involved in the proposed block design are register, partition design, and VIHS design.The dynamic system register is designed at first for getting a fast decision and to enable a low input-referred offset value.Then, the partition is formed concerning the output of the register, and the VIHS is used to produce the high proportional logical work.During performance evaluation, various measures have been utilized to analyze the performance of the proposed dynamic system registerbased hashing with optimal Hash Signature design.In addition to that, the estimated results are compared with the proposed technique to prove its efficiency.
KSAM: Infusing Multi-Source Knowledge into Dialogue Generation via Knowledge Source Aware Multi-Head Decoding

Knowledge-enhanced methods have bridged the gap between human beings and machines in generating dialogue responses.However, most previous works solely seek knowledge from a single source, and thus they often fail to obtain available knowledge because of the insufficient coverage of a single knowledge source.To this end, infusing knowledge from multiple sources becomes a trend.This paper proposes a novel approach Knowledge Source Aware Multi-Head Decoding, KSAM, to infuse multi-source knowledge into dialogue generation more efficiently.Rather than following the traditional single decoder paradigm, KSAM uses multiple independent source-aware decoder heads to alleviate three challenging problems in infusing multi-source knowledge, namely, the diversity among different knowledge sources, the indefinite knowledge alignment issue, and the insufficient flexibility/scalability in knowledge usage.Experiments on a Chinese multi-source knowledge-aligned dataset demonstrate the superior performance of KSAM against various competitive approaches.
Interpolation Kernel Machine and Indefinite Kernel Methods for Graph Classification

Graph kernels have been studied for a long time and applied among others for graph classification. In this paper we bring two novel aspects into the graph processing community. Currently, the backbone for kernel-based classification is solely the support vector machine. We introduce the interpolation kernel machine for this purpose. In addition, for both support vector machine and interpolation kernel machine, many kernels used in practice do not satisfy the formal requirements (e.g. positive definiteness). We thus introduce extensions of the standard version to indefinite kernel methods. We argue and experimentally demonstrate why these two aspects should be considered for graph classification. One of our conclusions will be that the interpolation kernel machine is a good alternative of support vector machine. Consequently, we will propose an extended experimental protocol. With this work we contribute to increasing the methodological plurality in the graph processing community.