Reviewerly/exHarmony huggingface dataset:
NG-MVEE: A New Proposed Hybrid Technique for Enhanced Mitigation of Code Re-Use Attack

Code-Reuse Attacks (CRAs) are solid mechanisms to bypass advanced software and hardware defenses because they use the software’s own code and they are very hard to be detected without significant overhead. Numerous methods have been proposed to protect against memory-based attacks that result from reusing parts of the attacked binary code. In this paper, two problems were tackled. the first problem is the lack of a categorized survey, analysis, and evaluation of the different CRAs proposed in the literature. The second problem is the inherent vulnerability that exists in protection techniques that are based on Multi-Variant Execution Environment (MVEE) since they are using shared Linux libraries with gadget-prone codes. In the paper a novel framework of CRA mitigation is introduced; fusing the two different prominent techniques of control flow integrity and multi-variant execution environment. The novel mitigation technique, named Next Generation MVEE (NG-MVEE), was built upon an existing generic CRA detection system (GHUMVEE) and complemented with a different CRA detection technique (G-Free) in order to provide comprehensive protection against code-reuse attacks. The outcome of the hybrid system is an optimized hybrid version of an MVEE technique, with minimal performance overhead increase due to the added protection layer of the G-Free technique. A median of 7% performance overhead resulted from the proposed protection system.
Question answering over knowledge graphs: a graph-driven approach

With the growth of knowledge graphs (KGs), question answering systems make the KGs easily accessible for end-users. Question answering over KGs aims to provide crisp answers to natural language questions across facts stored in the KGs. This paper proposes a graph-driven approach to answer questions over a KG through four steps, including (1) knowledge subgraph construction, (2) question graph construction, (3) graph matching, and (4) query execution. Given an input question, a knowledge subgraph, which is likely to include the answer is extracted to reduce the KG’s search space. A graph, named question graph, is built to represent the question’s intention. Then, the question graph is matched over the knowledge subgraph to find a query graph corresponding to a SPARQL query. Finally, the corresponding SPARQL is executed to return the answers to the question. The performance of the proposed approach is empirically evaluated using the 6th Question Answering over Linked Data Challenge (QALD-6). Experimental results show that the proposed approach improves the performance compared to the-state-of-art in terms of recall, precision, and F1-score.
Multiple Neighbor Relation Enhanced Graph Collaborative Filtering

Graph convolutional networks (GCNs) have substantially advanced state-of-the-art collaborative filtering (CF) methods. Recent GCN-based CF methods have started to explore potential neighbor relations instead of only focusing on direct user-item interactions. Despite the encouraging progress, they still suffer from two notable limitations: (1) only one type of potential neighbor relations is explored, i.e., co-interacting with the same item/user, neglecting the fact that user-item interactions are associated with various attributes and thus there can exist multiple potential neighbor relations from different aspects; (2) the distinction between information from direct user-item interactions and potential neighbor relations and their different extents of influence are not fully considered, which represent very different aspects of a user or an item. In this paper, we propose a novel Multiple Neighbor Relation enhanced method for Graph Collaborative Filtering (MNR-GCF) to address these two limitations. First, in order to capture multiple potential neighbor relations, we introduce a new construction of heterogeneous information networks with multiple types of edges to account for multiple neighbor relations, and a multi-relation aggregation mechanism to effectively integrate relation-aware information. We then enhance CF with a degree-aware dynamic routing mechanism to dynamically and adaptively fuse information from direct user-item interactions and potential neighbor relations at each aggregation layer. Our extensive experimental results show that our solution consistently and substantially outperforms a large number of state-of-the-art CF methods on three public benchmark datasets.
Personalized Dialogue System with External Knowledge

In this paper, we propose a new personalization method for a dialogue system using external knowledge and Graph Convolutional Networks (GCNs). The proposed system is divided into a personalize-part and a response-generation part with Japanese Transformer model. The personalize-part consists of a learned GCN, which can estimate the user’s interests, and a knowledge graph as external knowledge. The personalize-part can add an appropriate template to the response by Transformer according to the dialogue situation and user’s interests. Estimating a topic interesting for each user, the new system can provide an intimate dialogue reflecting individualized interests. We carried out extensive evaluation experiments, and it has shown that the proposed system exceeds the conventional Transformer dialogue model by 35.6% in the variety of information and by 34.0% in the level of desire to use the system.
Event Detection Based on Generative Adversarial Learning

Event detection (ED) aims at identifying event trigger words in a sentence and classifying event types. The quantity as well as quality of datasets hinder the event detection task severely due to the manual annotation is complex and expensive. To alleviate this problem, we present a data augmentation method based on generative adversarial network. In this adversarial learning mechanism, a generator is treated as a predictor of randomly selected event triggers, and the discriminator is to make guidance. Experimental results show that the model with generated data achieves competitive performance and further demonstrates the effectiveness of our proposed method.
Coalitional Game Theoretic Federated Learning

This study approaches federated learning (FL) from the viewpoint of coalitional games with coalition structure generation (CSG). In conventional FL, even if each client has data from a different distribution, they still learn a single global model. However, the performance of each local model can degrade. To address such issues, we propose an algorithm in which clients form coalitions and the clients in the same coalition jointly train a specialized model for the coalition, namely a coalition model. We formulate the algorithm as a graphical coalition game given by a weighted undirected graph in which a node indicates a client and the weight of an edge indicates the synergy between two connected clients. Formulating FL as a CSG problem enables us to generate an optimal CS that maximizes the sum of synergies. We first define two types of synergy, i.e., that based on the average improvement in classification accuracy of two agents as they join the same coalition and that based on the cosine similarity between the gradients of the loss functions, which is intended to exclude adversaries having adversarial data from a set of non-adversaries. We conduct experiments to evaluate our algorithm, and the results indicate that it outperforms current algorithms.
Practical Privacy-Preserving Behaviour-Based Authentication on the Edge

Sensing-enabled Internet of Things (IoT) devices provide an attractive source of data that can be used for seamless authentication of people located within the reach of these sensors. However, current behaviour-biometric solutions often require that the data is centrally stored and processed. This may pose privacy risks, as the data may be used not only for identifying a user, but also for inferring a user’s activities within an IoT environment. The risk can be alleviated by shifting the processing to the edge, so that only selected, obfuscated data is sent to the cloud. Previous work proposes a conceptually elegant privacy-preserving distributed deep learning inference method for user identification. In this work we attempt to actually implement such a method distributed over edge devices and the cloud, and uncover significant challenges related to hardware and system limitations of the distributed setup. We then propose amelioration strategies for privacy-preserving distributed deep learning training. Nevertheless, these strategies only partly address the identified issues, calling for further work in bringing distributed sensor-based behaviour authentication to reality.
User Embedding Sharing with Deep Canonical Correlation Analysis for Dual-Target Cross-Domain Recommender System

Cross-domain recommender systems (CDRSs) focus on transferring a model learned in an auxiliary domain to construct improved user embeddings in a target domain, which are generally sparse. It can transfer overall user preferences across domains to solve sparsity problems, but it is not sufficient to train good user embeddings in the target domain, which are normally obtained at an individual user level. Although some works have tried to transfer at the individual user level to cover dominant user characteristics in each domain, these rely on simple combining operations. If users from two domains differ, such operations cannot properly represent their individual dominant characteristics. Traditionally, CDRSs consider improving recommendation performance only in the target domain. However, the shared user embeddings contain dominant user characteristics from two domains, which can improve the performance of both domains simultaneously; this is referred to as a dual-target CDRS. In this work, we propose a user embedding sharing method that can extract and share dominant user characteristics of two domains at the individual user level to improve the recommendation performance in dual-target systems using deep canonical correlation analysis (DCCA). User embeddings from the proposed method can preserve important user information from each domain while obtaining dominant user characteristics from maximally correlated user embeddings of the same user from two domains. Using four real-world datasets, experiments indicate that our method outperforms four baselines in rating prediction and ranking.
Cross-Domain Sentiment Analysis Based on Small in-Domain Fine-Tuning

Significant progress has been made in sentiment analysis over the past few years, especially due to the application of deep neural language models.However, there is a problem of transferability of trained models from one domain to another, especially for less studied languages such as Russian.We propose an approach to build cross-domain sentiment analysis models based on a two-stage procedure: first, we finetune a pre-trained RuBERT language model on a combined non-domain corpus, and then fine-tune this model on a small domain corpus.We conducted large-scale experiments with 30 sentiment annotated corpora across 12 domains.In order to increase the representativeness of news texts with high-quality annotation, we created a novel RuNews corpus, containing 1,823 news articles annotated by sentiment.The results show that finetuning the model using a small number (about several hundred) of annotated domain texts can significantly improve the performance of sentiment analysis for a new domain (on average by 4.6 p.p.).We also obtained the state-of-the-art results for 7 out of 14 test corpora.
Designing an Intelligent Learning System For Practicing the Oboe Embouchure

The oboe is considered one of the most difficult woodwind instruments to learn due to the labor involved in mastering the embouchure required to produce a steady, long tone through the instrument’s reeds. Previous research in music education has suggested that using visualization or imagery as a training method, so-called audiation, helps novice learners gain a better understanding of the relationship between sound and embouchure. Inspired by this concept, we designed an interactive learning system using real-time acoustic analysis (MFCCs) and support vector machines (SVM). Our interactive visualization aims to support the user’s learning process by rendering the quality of good and bad sounds as pitch indications through particle swarm animations. A pilot user study showed that this makes the learning experience more reflective and self-directed, as the learner can understand the relationship between their breathing technique and the visualization of the generated sound quality. In this paper, we intend to address issues potentially involved in designing an intelligent learning system using sensing technologies in the context of music education.
Gesture Recognition Using MediaPipe for Online Realtime Gameplay

Hand gesture recognition has advanced greatly in the recent years due to its effectiveness in interacting with computers, machines, and other equipment and devices. It has been used in various fields such as hospitals, sign language recognition. While applications of the techniques for traditional video games at this current stage are still quite limited, we see the potentials using gesture-based controls as training and rehabilitation tools. This paper explores in particular how gestures can be used to play video games. It uses the gestures captured via the user’s video camera and performs various actions in the game. Two games, Hill Climb Racing and Subway Surfers, have been investigated in this paper. For Hill Climb Racing, only two hand gestures are needed since there are only two actions in the game. Body gestures and movements are used to play the game subway surfers. Promising results (real time with webcam only) indicating practicability of the approach are obtained.
A New Approach to Detect Deepfake Video using Multi-Input Convolutional Neural Network

In this modern age, Deepfake videos are spreading around, having a severe impact on the social and personal lives of the general public. Efficient techniques, operating better than others, is hence in the requirement. In this research, combination of many objects like left eye, right eye and mouth shapes from image frames of videos are taken into consideration, thus achieving better accuracy than other detection techniques. In this proposal, deep learning techniques, specially the multi-input Convolution Neural Network, are executed. The proposal firstly detects the face, followed by specific objects such as left eye, right eye and mouth. Then multi-input CNN is applied that classifies the data based on eyes and mouth. After applying multi-input CNN, the accuracy increases impressively. Novel techniques are used to extract specific features like eyes, mouth. Memory storage was a concern for the proposal. Despite this, comparison with other relevant research works proved better accuracy and data analysis.
Improved hybrid Jaya Grey Wolf optimization algorithm

The grey wolf optimization algorithm is a competitive optimization technique, however, it has the disadvantage of low precision and slow convergence. Aiming at the above shortcomings, inspired by Jaya algorithm, an improved grey wolf algorithm is proposed. The novel algorithm introduces the worst position of the best wolf pack and the individual historical best position into the original position update equation. The simulation results of nine classical test functions show that compared with other standard algorithms, the hybrid grey wolf optimization algorithm has advantages in both convergence speed and solution accuracy in most cases.
Categorizing Citation Relations in Scientific Papers Based on the Contributions of Cited Papers

With the massive increase in the number of research papers, it becomes difficult for researchers to keep track of the current state of research. Unlike the current classification methods that use citation intent, from a reverse perspective, we propose a method to Classify Citation Relationships based on the Contributions of Cited papers. This classification method can count the number of citations for each contribution, which can be used as a feature of a paper summarization system to generate a summary. Since the number of citations changes over time, the generated paper summary is dynamic. It can also generate a citation summary based on the citations of each contribution. We build a dataset for this method called C2RC2. We achieve an accuracy of 0.7896 on the test set using the SciBERT model, which indicates that it is feasible to classify citation relations by the contributions of cited papers.
Evaluation Dataset of Multiple-Choice Cloze Items for Vocabulary Training and Testing

Vocabulary learning is a typical part of nearly any second language learning curriculum. This entails methodologies and materials for training and testing vocabulary knowledge in learners. In large-scale programs, the preparation of such materials can be labor intensive and thus automatic means of generation are desirable. VocaTT (Vocabulary Training and Testing) is an ongoing project to use machine learning methods to generate novel multiple choice cloze (i.e., fill-in-the-blank) items for use in second language learning programs. This paper describes the ongoing creation of a gold standard set of multiple-choice cloze items to be used in training a machine learning algorithm. Machine-generated multiple choice cloze items were reviewed by two experienced language teachers, who evaluated each item for well-formedness (i.e., suitability as multiple-choice cloze test item) with three options: reject as unsalvageable, keep as-is, or revise into a well-formed item as they thought best. Results for a 600-item set that both checkers evaluated show moderate agreement on the question of rejection but slight agreement for keeping as-is. For revised items, the agreement on what type of revisions to make was slight to fair. In an expanded set of 2,792 items, checkers judged most items as needing revision but made varying kinds of revisions to yield well-formed items. Interested researchers may contact the authors to inquire about how they may access and use the evaluation dataset.
Organizing Search Results into a Table to Assist in Understanding and Expanding Search Target

We propose a meta-search system that assists users in understanding search results and expanding the search scope. The system assumes that the documents to be searched are tagged and that hierarchical semantic relationships are defined among the tags. It organizes search results generated by a search engine in the form of a table based on the hierarchical relationships and presents them to the user. The user can proceed with information exploration by selecting documents and tags hierarchically arranged in the table. To evaluate the proposed system, we conducted a user experiment using a Wikipedia search service built with the system. The results confirmed that the proposed system is superior to the system using the conventional method of presenting search results in exploratory search support.
Federated Capsule Graph Neural Network with Personalization

Capsule-based GNN models have recently shown their competitiveness in graph classification. In this work, we propose FedCapsGNN, a novel framework which enables multiple clients to collaboratively perform capsule-based graph classification tasks under the federated learning paradigm. Our framework effectively combats the ill-effects of graph statistical heterogeneity with a novel local primary capsule augmentation method and a dexterously designed aggregation strategy with clustered capsule network layers. Experimental results demonstrate that our FedCapsGNN framework is able to capture the personality of each local client while effectively utilizing global knowledge, achieving superior accuracy for graph classification tasks on multiple benchmark datasets.
A Concept Net-based semantic constraint method for query expansion

Existing query expansion methods usually select the expansion terms according to the term frequency, proximity, location, etc., neglecting the semantic information related to the original query. To solve this problem, we propose a ConceptNet-based semantic constraint method for query expansion. Firstly, the ConceptNet semantic network constrains the range of candidate terms, avoiding all terms in feedback documents is selected as candidate terms. It will reduce the impact of irrelevant terms on query expansion. Secondly, BERT is used to filter out sentences related to query semantics in feedback documents and construct subsets of candidate expansion terms. Then, the two subsets of candidate terms are combined to screen out the expansion terms related to query on semantics. To verify the effectiveness of the proposed method, a series of experiments are carried out on TREC standard text retrieval data sets via applying the proposed method to pseudo-relevance feedback model called Rocchio. Experimental results show that the proposed model significantly improves the results of MAP and P@10 on four data sets.
Adversarial Sample Generation Method for Spam SMS Classification

Research shows that adding small perturbation information to the deep neural network (DNN) can lead to DNN classification errors, which is called an adversarial sample attack. Adversarial sample attack also exists in the detection of spam messages in deep neural networks. Therefore, this paper proposes an adversarial sample generation method SWordAttacker for spam messages in the black-box situation. This method designed a new calculation method of word importance, found key clauses by combining attention mechanisms, and used the scoring function to find keywords in key clauses. Finally, the adversarial samples were generated by combining attack strategies such as insertion, exchange, and similar substitution. The experiments were conducted on two DNN models, long-short memory networks(LSTM) and convolutional neural networks(CNN), using short message service(SMS) Spam datasets, and AG’s News datasets to verify the effectiveness of the proposed method. The experimental results show that SWordAttacker can greatly reduce the accuracy of the model with small perturbations and improve efficiency.
Entity Level QA Pairs Dataset for Sentiment Analysis

In this paper, we present a named entity based sentiment analysis dataset OTTQA V1.0 that aims to detect primary TV series character opinions along with their sentiment polarities from the tweets generated from answer span extraction. The dataset, named OTTQA V1.0, contains 5237 unique question answer pairs from "Game Of Thrones" TV series . Along with the dataset, supportive tweets are extracted according to their relevancy with answer span keyword which is used to gauge opinion changes of OTT series characters over a given time period. The primary goal of proposing this sentiment analysis task is to provide the users with an utility dataset that calculates the sentiments of primary TV series characters from the tweets.
Design and Strategy Research of Online Learning Process Evaluation System Based on Big Data Intelligent Analysis Technology

With the integration of the Internet and education information, education data has been developed rapidly in the new era of education big data. After analyzing the online learning evaluation system supported by big data, the main characteristics of the process evaluation in online learning are analyzed in this study under the condition of big data analysis. As a result, the main contents of the process evaluation in online teaching are discussed to redesign the process evaluation system model of online teaching supported by big data. The specific process of the management system is also proposed. The results of this study provide a new research perspective and reference object for the evaluation system of online learning and contribute to the more accurate and humanized theoretical research of online learning services.