Reviewerly/exHarmony huggingface dataset:
A Graph Embedding Approach for Link Prediction via Triadic Closure Based Direct Aggregation and Weighted Concatenation

Graph embedding based on deep learning is an effective approach for link prediction. However, there still remain some unsolved problems. Firstly, existing methods iteratively aggregate node embeddings from the neighborhood, which cannot retain the global structural information at lower node aggregation cost. Secondly, existing methods can hardly retain rich local structural information for edge embeddings obtained from node embeddings by Hadamard product, summation, or direct concatenation. To tackle these challenges, this paper proposes a novel graph embedding approach for link prediction via triadic closure based direct aggregation and weighted concatenation. To aggregate neighborhood information with high efficiency, our proposed approach directly aggregates multi-order neighbors to the central node and utilizes the triadic closure structure to assign different aggregating weights. To well retain the local structural information, our proposed approach generates edge embeddings through weighted summation. Extensive experiments demonstrate that the triadic closure based direct aggregation and weighted concatenation enable our proposed approach to efficiently learn more accurate embeddings for link prediction, outperforming state-of-the-art methods.
CMT: Cross-modal Memory Transformer for Medical Image Report Generation

Automatic medical image report generation has attracted extensive research interest in medical data mining, which effectively alleviates doctors' workload and improves report standardization. The mainstream approaches adopt the Transformer-based Encoder-Decoder architecture to align the visual and linguistic features. However, they rarely consider the importance of cross-modal interaction (e.g., the interaction between images and reports) and do not adequately explore the relations between multi-modal medical data, leading to inaccurate and incoherent reports. To address these issues, we propose a Cross-modal Memory Transformer model (CMT) to process multi-modal medical data (i.e., medical images, medical terminology knowledge, and medical report text), and leverage the relations between multi-modal medical data to generate accurate medical reports. To explore the interaction of cross-modal information, we design a novel cross-modal feature memory decoder to memorize the relations between image and report features. Furthermore, the multi-modal feature fusion module in CMT exploits the multi-modal medical data to adaptively measure the contribution of multi-modal features for word generation, which improves the accuracy of generated reports. Extensive experiments on three real datasets demonstrate that our proposed CMT outperforms benchmark methods on automatic metrics.
Unify the Usage of Lexicon in Chinese Named Entity Recognition

Lexicon plays a critical role in Chinese Named Entity Recognition (CNER). The major reason lies in that words in the lexicon, lexicon words for short, are highly related to entity mention boundaries. Most lexicon enhanced CNER approaches focus on introducing lexicon words to the input and hidden layers. However, existing lexicon enhanced methods make the method hard to be adaptable and put weak lexicon constraints on architectures. To tackle these challenge, we propose a unified lexicon enhanced CNER framework. Specifically, lexicon word identification (LWI) task is proposed to locate and classify textual references to lexicon words. Similar to CNER task, this task is formalized either as sequence labeling or character relation classification, adopts CRF or Co-Predictor (Character relation classification) as the task specific layer, and is optimized with log-likelihood function of sequence probability or cross entropy of character pair relation type distribution. LWI task shares the input and hidden layers with CNER task. The whole framework is pre-trained with LWI task and fine-tuned with CNER task. Experimental results on two benchmark CNER datasets show the better effectiveness and flexibility than state-of-the-art baselines.
Is a Single Embedding Sufficient? Resolving Polysemy of Words from the Perspective of Markov Decision Process

Polysemy is a widespread linguistic phenomenon. A word can carry a variety of semantic information and is recognized as a definite semantic in a specific context. However, in many models that are good at processing text sequences, words are usually treated as a single embedding, which ignores the polysemy characteristics of words and makes it difficult to model the process of semantic cognition. To fill this gap, this paper models a variety of RNNs from the perspective of the Markov decision process (MDP) and classifies them as Single-state RNN (SRNN), pointing out SRNN deficiencies in polysemy and semantic cognitive processes. A Polymorphic Recurrent Neural Network (PRNN) that can effectively simulate the process of human semantic cognition is proposed by improving the policy function. PRNN selects the specific semantics to be expressed according to the actual context in which the word is located. Extensive experimental results show that PRNNs are superior to RNNs in many natural language processing tasks. The analysis of specific cases shows how PRNNs simulate the process of human language cognition.
Multimodal Entity Linking with Mixed Fusion Mechanism

Many efficient multimodal entity linking (MEL) methods have been developed in recent years. However, most MEL methods still suffer from two drawbacks. On the one hand, the inconsistency of modal encoding brings the semantic gap between modalities in the feature space and blocks the multimodal fusion. On the other hand, previous attention-based multimodal fusions cannot efficiently handle noise. To address these issues, we propose a Multimodal Encoder Representation from Transformers for Multimodal Entity Linking (Mert-MEL). Firstly, we concatenate contexts of mentions and Wikidata abstracts of candidate entities as inputs. Then we utilize transformer encoders to extract features of both textual and visual information, and employ contrastive learning to better align feature spaces. We also incorporate phrase-level text embeddings to get rich textual representations. Subsequently, we use a combination of global fusion and bottleneck fusion to integrate multimodal information and extract key information instead of noise. Finally, we send the fused embeddings to an MEL head to predict the matching scores between the mention and the candidate entities, and then link the mention to the candidate with the highest score. Experiments demonstrate that Mert-MEL prominently outperforms strong baselines on two MEL datasets.
TE-DyGE: Temporal Evolution-Enhanced Dynamic Graph Embedding Network

Dynamic graph representation has gradually attached more attention in the research area since many real-world graphs evolve over time. Recent works typically represent the dynamic graph as a sequence of discrete static subgraphs and apply a recurrent neural network (RNN) to capture graph evolution over multiple discrete-time subgraphs. However, these approaches fail to capture the fine-grained temporal evolution leading to introducing temporal invalid information into the structure representation. In this paper, we propose a novel temporal evolution-enhanced dynamic graph embedding method (TE-DyGE) to address this limitation. To capture the fine-grained temporal evolution, TE-DyGE first applies a temporal random walk to filter out temporally valid information. Additionally, we introduce temporal-dependent weight to enhance the structure attention network. We evaluate the TE-DyGE in link prediction tasks. The experimental results demonstrate a generally higher performance of TE-DyGE compared with several state-of-the-art related baselines.
Few-Shot Link Prediction for Event-Based Social Networks via Meta-learning

With the thriving of social network analysis, large efforts have been made on link prediction for event-based social networks (EBSNs). Unfortunately, since society is evolving with constantly emerging social events, it is extremely difficult to accurately capture their semantics and evolution rules at an early stage. Meanwhile, traditional solutions require extensive training from scratch to accommodate new events, leading to lagging predictions and high maintenance costs. To tackle these challenges, we investigate this cross-network few-shot problem and propose a novel meta-learning model for link prediction on new EBSNs. To accurately simulate the few-shot scenarios, we first utilize existing EBSNs to define a task distribution that augments the new event with other observed events. Specifically, we define a unified and generalized target event to be transferred as the few-shot event. Then, we empower a simple but effective event-aware graph attention network to encode existing fine-grained events and the few-shot target events. Furthermore, we follow gradient-based episode learning to obtain transferable knowledge and adapt to unseen EBSNs with sparse connections. Finally, extensive experiments on both public and industrial datasets have demonstrated the performance of fast adaption and even overall performance.
MPGCL: Multi-perspective Graph Contrastive Learning

In recent years, contrastive learning has emerged as a successful method for unsupervised graph representation learning. It generates two or more different views by data augmentation and maximizes the mutual information between the views. Prior approaches usually adopt naive data augmentation strategies or ignore the rich global information of the graph structure, leading to suboptimal performance. This paper proposes a contrast-based unsupervised graph representation learning framework, MPGCL. Since data augmentation is the key to contrastive learning, this paper proposes constructing higher-order networks by injecting similarity-based global information into the original graph. Then, adaptive and random augmentation strategies are combined to generate two views with complementary semantic information, which preserve important semantic information while not being too similar. In addition, the previous methods only consider the same nodes as positive samples. In this paper, the positive samples are identified by capturing global information. In extensive experiments on eight real benchmark datasets, MPGCL outperforms both the SOTA unsupervised competitors and the fully supervised methods on the downstream task of node classification. The code is available at: https://github.com/asfdd3/-miao/tree/src/MPGCL .
Unleashing Pre-trained Masked Language Model Knowledge for Label Signal Guided Event Detection

Event detection (ED) aims to recognize triggers and their types in sentences. Previous work employs distantly supervised methods or pre-trained language models to generate sentences containing events to alleviate data scarcity. Further, determining the spans and types of triggers is complex and may have deviations. In this paper, we propose to unleash Pre-trained Masked Language Model (PMLM) knowledge for label signal guided ED by a novel trigger augmentation. We directly generate triggers by leveraging the rich knowledge of PMLM through masking triggers. However, these newly replaced triggers may not correspond to the label of the masked trigger. To control such trigger augmentation noises, we design a label signal guided classification mechanism with event type-subtype guidance. To ensure the quality of generated triggers, a semantic consistency mechanism is introduced. Experimental results on the ACE2005 and FewEvent show the effectiveness of our proposed approach.
A Checklist Proposal for Assessing the Potential of Language Teaching Apps

The use of mobile applications for English as a Foreign Language (EFL) teaching and learning has become widespread at all educational levels, increasing the difficulty in identifying the most technically and pedagogically suitable application for a specific teaching context. Although digital skills should be a central component in (prospective) FL teachers’ qualifications as one of their twenty-first-century skills, teachers may be inadequately prepared to select, evaluate, and use some of the newer technologies, namely mobile apps, to improve the language learning process within their teaching frameworks. Although there is a growing body of research on the evaluation of apps using checklists, very few studies focus on easily applicable, hands-on criteria for selecting and evaluating apps specifically for TEFL. Therefore, this chapter aims to provide a functional, accessible checklist to evaluate apps, enabling prospective and novice teachers to incorporate them into their instruction. The checklist considers technical, methodological, and linguistic features but maintains a focus on pedagogically relevant criteria.
Cognitive Conflict-Based E-Book with Real Experiment Video Analysis Integration to Enhance Conceptual Understanding of Motion Kinematics

Students find it challenging to understand motion concept or mechanics in physics learning and often experience misconceptions. There is also a lack of teaching materials that promote conceptual understanding. This study aims to produce a valid, practical, and effective e-book based on cognitive conflict to enhance conceptual understanding. This research used explanatory sequential mixed methods. The effectiveness test was carried out through an experimental method with a pretest-posttest control group design. Using the cluster random sampling, 61 science students performed as the samples. The research instruments used were interview guides, observation sheets, questionnaires, and two-level multiple-choice concept tests. The data were analyzed qualitatively and quantitatively using Aiken's V formula, the percentage technique, and the Mann-Whitney hypothesis test. This study produced an e-book according to the four syntaxes of the cognitive conflict-based learning model (CCBL). Real experiment video analysis was integrated into the third syntax. The e-book is valid in content, construct, language, and graphics, with an average of 0.83 in the high category. This e-book is practical in terms of user convenience, attractiveness, and benefits, with an average of 79.6 in the strong category. This e-book is effective, with a significant effect of 0.001 0.05 on conceptual understanding and misconceptions. We concluded that the cognitive conflict-based e-book with real experiment video analysis integration is valid, practical, and effective in enhancing students' conceptual understanding of motion kinematics.
SUPPORTING THE CHOICE OF THE BEST-FIT AGILE MODEL USING FITRADEOFF

The goal of this study is to propose a decision model for supporting the choice of the bestagile model, based on the Flexible and Interactive Tradeoff (FITradeoff) method.This study provides the definition of an extensive set of 38 measurable criteria that were considered for evaluation of the most popular agile process models used in small and medium-scale enterprises.To illustrate the application of the model, we applied it to a set of alternatives that includes DSDM, SCRUM, XP2 and Crystal and an experienced project manager acted as our decision maker during the interactive elicitation process of the FITradeoff.The results show that the FITradeoff is very valuable for this class of problem because of its strong mathematical foundation and the possibility of combining two different ways for modeling the preferences of decision makers, which makes the process cognitively easier than other multi-criteria methods and increase the confidence on the results.Although our study focuses on the context of small and medium-scale companies for software development, the approach can be used to other types of environments, including distributed software development and large enterprises.
A Surveillance Mobile Robot Based on Low-Cost Embedded Computers

Robot technology is everywhere from health to retail. It is very rare to see the use of robots in developing countries. It is also a big task for companies to hire staffs for the purpose of monitoring as it requires to work especially in the night. It is also dangerous for humans. It is a good idea to have some robots for the purpose of monitoring. This paper focuses on the development of surveillance mobile robot using an Arduino board, an ESP32 board and a Raspberry PI system. The robot is a proof of concept of possible robotic applications in warehouses where monitoring technologies need to be embedded in the system. In this context, the device is equipped with a servo motor and an onboard camera. The end-user can monitor the robot remotely by wirelessly controlling the position of the servo (i.e., the camera). The camera module is controlled by the Raspberry PI and incorporates a motion detection system which provides an alarm and captures the image frame as soon as the motion is detected. Finally, the image is sent to the end-user. A set of preliminary tests of the system has been performed in order to prove the reliability of the proposed prototype.
Handwritten Text Recognition for Regional Languages of Indian Subcontinent

Handwritten Text Recognition has been an area of interest in research for many decades as the demand to convert and process big chunks of handwritten data to Digital form is high which ultimately can become useful information in different fields. Handwritten text recognition may be well used in applications such as searching, postal services, banks etc., and many areas powered with such applications. Though researchers have been using a number of existing techniques and models to achieve enhanced accuracy in the area, many challenges and tasks need to be refined and new algorithms explored owing to variability in handwriting styles. Sufficient work has already been undertaken and is being regularly carried out in some languages all over the world such as English, Chinese, Latin etc. but more needs to be done at the level of regional languages of the Indian subcontinent. This study has undertaken a generalized review of various algorithms, classifier and feature selection techniques along with achievements and challenges with respect to handwritten text recognition of regional languages and the general outcome have been that algorithms and techniques cannot be the same for all languages and it varies accordingly to the writing style, nature, volume so as to achieve a better accuracy.
Hesitant Fuzzy Sets Based TSK Model for Sentiment Analysis

Sentiment analysis (SA) is an important part of psychology that helps to predict the attitude or personality traits of a human. In the present study extension of traditional fuzzy sets namely hesitant fuzzy sets (HFS) along with hesitant fuzzy aggregation operators are used to analyze Twitter data to predict sentiment parameters using fuzzy rule-based approach. Tools such as TextBlob and NaiveBayesClassifier of Natural Language Processing toolkit (NLTK) were used to perform initial Sentiment Analysis of Twitter data in Google Collaboratory. The results so obtained are used as inputs in the proposed model. Validation of the proposed model is done in terms of RMSE by comparing results with the traditional TSK model for equal and unequal interval lengths of fuzzy sets.
Traitor Tracing with $$N^{1/3}$$-Size Ciphertexts and O(1)-Size Keys from k-Lin

We present a pairing-based traitor tracing scheme for N users with $$\begin{aligned} | \textsf{pk}| = | \textsf{ct}| = O(N^{1/3}), \quad | \textsf{sk}| = O(1). \end{aligned}$$ This is the first pairing-based scheme to achieve $$| \textsf{pk}| \cdot | \textsf{sk}| \cdot | \textsf{ct}| = o(N)$$ . Our construction relies on the (bilateral) k-Lin assumption, and achieves private tracing and full collusion resistance. Our result simultaneously improves upon the sizes of $$ \textsf{pk}, \textsf{ct}$$ in Boneh–Sahai–Waters [Eurocrypt ’06] and the size of $$ \textsf{sk}$$ in Zhandry [Crypto ’20], while further eliminating the reliance on the generic group model in the latter work.
Sentimental Segregation for Social Media Using Lexicon Technology

Social media is widely used by today’s generation for expression. Twitter such as can be proven a highly efficient social media platform where the users are free to share their opinions, views, and ideas about different products and services launched in the market. Not only do various organizations and companies look for a public opinion about their products and services but also the customers are tended to review a product before making a purchase. Our project holds the prime goal to deliver honest reviews which a customer or user actually deserves. Object sentiment analysis helps us to determine that is the piece of writing is positive or negative or neutral. We analyze the textual reviews of a product on the social media platform and assign a sentimental label point of view. The scores on each point of view from the multiple reviews are then aggregated and a net sentimental profile of the product is generated. A search engine is made available where any product name searched by the user gives a net public sentimental reaction to it as analyzed by the various tweets on that social networking site. By retrieving the data from Twitter API, we come up with a database which provides the overall review of the product. The use of lexicon methodologies is done to interpret the sentiment held by the user. The evaluation process for a user is made simpler and convenient to make a purchase.
Emotional AI-enabled Interview Aid

Emotions help in providing additional meaning to the text written or words spoken by a person. They play an important role in several sectors such as maintaining a good relationship with customers by analyzing their feedback, analyzing the candidate’s speech and text to know their emotions and interest in the company, and so on. In this paper, we focus on extracting the emotions of interview candidates and analyzing their text and speech so that a company can find a better fit for the company as well as the candidate can know what meaning or emotion his/her speech or text conveys. Audio and text datasets are used which are preprocessed and fed to the Machine Learning models which help in classifying the emotion and analyzing them to provide analysis of the input given. Using this application, companies and candidates can get a good overview and brief analysis of the speech/text.
New Ways to Garble Arithmetic Circuits

The beautiful work of Applebaum, Ishai, and Kushilevitz [FOCS’11] initiated the study of arithmetic variants of Yao’s garbled circuits. An arithmetic garbling scheme is an efficient transformation that converts an arithmetic circuit $$C: \mathcal {R}^n \rightarrow \mathcal {R}^m$$ over a ring $$\mathcal {R}$$ into a garbled circuit $$\widehat{C}$$ and n affine functions $$L_i$$ for $$i \in [n]$$ , such that $$\widehat{C}$$ and $$L_i(x_i)$$ reveals only the output C(x) and no other information of x. AIK presented the first arithmetic garbling scheme supporting computation over integers from a bounded (possibly exponentially large) range, based on Learning With Errors (LWE). In contrast, converting C into a Boolean circuit and applying Yao’s garbled circuit treats the inputs as bit strings instead of ring elements, and hence is not “arithmetic”. In this work, we present new ways to garble arithmetic circuits, which improve the state-of-the-art on efficiency, modularity, and functionality. To measure efficiency, we define the rate of a garbling scheme as the maximal ratio between the bit-length of the garbled circuit $$|\widehat{C}|$$ and that of the computation tableau $$|C|\ell $$ in the clear, where $$\ell $$ is the bit length of wire values (e.g., Yao’s garbled circuit has rate $$O(\lambda )$$ ). To the best of our knowledge, constant-rate (Boolean or arithmetic) garbling was only achieved before using the powerful primitive of indistinguishability obfuscation, or for restricted circuits with small depth.