Reviewerly/exHarmony huggingface dataset:
“You’ve Been Living Here For as Long as You Can Remember”: Trauma in <i>OMORI</i>'s Environmental Design

Developed by Omocat and released in December 2020, OMORI is a surreal psychological horror role-playing game. The game follows the titular protagonist Omori as it examines such sensitive topics as suicide, grief, death, and depression. Such traumatic events are triggered in several planes of existence—White Space, Headspace, and Black Space—leading to anxiety, regression, and resurfacing trauma. In our article, we examine such representations of trauma with particular attention to the role of environmental design. The planes represent different approaches to memory, trauma, and repression, which Omori and the player navigate in non-linear, recursive paths. We analyze how each space seeks to illuminate and explore aspects of trauma in its respective atmosphere. Through environmental design, OMORI provides players with three distinct experiences with escapism and trauma that are representative of the experiences of trauma victims, ultimately elucidating the psychological phenomenon on a larger scale to de-stigmatize trauma.
Location Privacy-Preserving Scheme in IoBT Networks Using Deception-Based Techniques

The Internet of Battlefield Things (IoBT) refers to interconnected battlefield equipment/sources for synchronized automated decision making. Due to difficulties unique to the battlefield, such as a lack of infrastructure, the heterogeneity of equipment, and attacks, IoBT networks differ significantly from regular IoT networks. In war scenarios, real-time location information gathering is critical for combat effectiveness and is dependent on network connectivity and information sharing in the presence of an enemy. To maintain connectivity and guarantee the safety of soldiers/equipment, location information must be exchanged. The location, identification, and trajectory of soldiers/devices are all contained in these messages. A malicious attacker may utilize this information to build a complete trajectory of a target node and track it. This paper proposes a location privacy-preserving scheme in IoBT networks using deception-based techniques. Dummy identifier (DID), sensitive areas location privacy enhancement, and silence period concepts are used to minimize the attacker’s ability to track a target node. In addition, to consider the security of the location information, another security layer is proposed, which generates a pseudonym location for the source node to use instead of its real location when sending messages in the network. We develop a Matlab simulation to evaluate our scheme in terms of average anonymity and probability of linkability of the source node. The results show that the proposed method improves the anonymity of the source node. It reduces the attacker’s ability to link the old DID of the source node with its new DID. Finally, the results show further privacy enhancement by applying the sensitive area concept, which is important for IoBT networks.
Artificial Intelligence Classification Model for Modern Chinese Poetry in Education

Various modern Chinese poetry styles have influenced the development of new Chinese poetry; therefore, the classification of poetry style is very important for understanding these poems and promoting education regarding new Chinese poetry. For poetry learners, due to a lack of experience, it is difficult to accurately judge the style of poetry, which makes it difficult for learners to understand poetry. For poetry researchers, classification of poetry styles in modern poetry is mainly carried out by experts, and there are some disputes between them, which leads to the incorrect and subjective classification of modern poetry. To solve these problems in the classification of modern Chinese poetry, the eXtreme Gradient Boosting (XGBoost) algorithm is used in this paper to build an automatic classification model of modern Chinese poetry, which can automatically and objectively classify poetry. First, modern Chinese poetry is divided into words, and stopwords are removed. Then, Doc2Vec is used to obtain the vector of each poem. The classification model for modern Chinese poetry was iteratively trained using XGBoost, and each iteration promotes the optimization of the next generation of the model until the automatic classification model of modern Chinese poetry is obtained, which is named Modern Chinese Poetry based on XGBoost (XGBoost-MCP). Finally, the XGBoost-MCP model built in this paper was used in experiments on real datasets and compared with Support Vector Machine (SVM), Deep Neural Network (DNN), and Decision Tree (DT) models. The experimental results show that the XGBoost-MCP model performs above 90% in all data evaluations, is obviously superior to the other three algorithms, and has high accuracy and objectivity. Applying this to education can help learners and researchers better understand and study poetry.
User-Centered Software Design: User Interface Redesign for Blockly–Electron, Artificial Intelligence Educational Software for Primary and Secondary Schools

According to the 2021 and 2022 Horizon Report, AI is emerging in all areas of education, in various forms of educational aids with various applications, and is carving out a similarly ubiquitous presence across campuses and classrooms. This study explores a user-centered approach used in the design of the AI educational software by taking the redesign of the user interface of AI educational software Blockly–Electron as an example. Moreover, by analyzing the relationship between the four variables of software usability, the abstract usability is further certified so as to provide ideas for future improvements to the usability of AI educational software. User-centered design methods and attribution analysis are the main research methods used in this study. The user-centered approach was structured around four phases. Overall, seventy-three middle school students and five teachers participated in the study. The USE scale will be used to measure the usability of Blockly–Electron. Five design deliverables and an attribution model were created and discovered in the linear relationship between Ease of Learning, Ease of Use, Usefulness and Satisfaction, and Ease of use as a mediator variable, which is significantly different from the results of previous regression analysis for the USE scale. This study provides a structural user-centered design methodology with quantitative research. The deliverables and the attribution model can be used in the AI educational software design. Furthermore, this study found that usefulness and ease of learning significantly affect the ease of use, and ease of use significantly affects satisfaction. Based on this, the usability will be further concretized to facilitate the production of software with greater usability.
Two Majority Voting Classifiers Applied to Heart Disease Prediction

Two novel methods for heart disease prediction, which use the kurtosis of the features and the Maxwell–Boltzmann distribution, are presented. A Majority Voting approach is applied, and two base classifiers are derived through statistical weight calculation. First, exploitation of attribute kurtosis and attribute Kolmogorov–Smirnov test (KS test) result is done by plugging the base categorizer into a Bagging Classifier. Second, fitting Maxwell random variables to the components and summating KS statistics are used for weight assignment. We have compared state-of-the-art methods to the proposed classifiers and reported the results. According to the findings, our Gaussian distribution and kurtosis-based Majority Voting Bagging Classifier (GKMVB) and Maxwell Distribution-based Majority Voting Bagging Classifier (MKMVB) outperform SVM, ANN, and Naive Bayes algorithms. In this context, which also indicates, especially when we consider that the KS test and kurtosis hack is intuitive, that the proposed routine is promising. Following the state-of-the-art, the experiments were conducted on two well-known datasets of Heart Disease Prediction, namely Statlog, and Spectf. A comparison of Optimized Precision is made to prove the effectiveness of the methods: the newly proposed methods attained 85.6 and 81.0 for Statlog and Spectf, respectively (while the state of the heart attained 83.5 and 71.6, respectively). We claim that the Majority Voting family of classifiers is still open to new developments through appropriate weight assignment. This claim is obvious, especially when its simple structure is fused with the Ensemble Methods’ generalization ability and success.
Super-Resolution Reconstruction of Depth Image Based on Kriging Interpolation

The super-resolution of depth images is a research hotspot. In this study, the classical Kriging algorithm is applied to the spatial interpolation of depth images, together with the fractional-order differential method for edge recognition, to realise the super-resolution reconstruction of depth images. The resulting interpolation model improves the edge performance of Kriging interpolation by harnessing the superior characteristics of fractional-order differential edge recognition and effectively solving the edge blurring problem in super-resolution interpolation of depth images. Experimental results show that, compared with the classical algorithms, the super-resolution reconstruction based on Kriging interpolation is greatly improved in terms of visual effects and the peak signal-to-noise ratio of the depth image. In particular, edge recognition based on fractional-order differentiation solves the image blurring problem at the edges of the depth images. Inspection of the point clouds of the depth images shows that the output of the proposed interpolation model has obvious fractal characteristics.
Detecting vulnerabilities in IoT software: New hybrid model and comprehensive data analysis

Software vulnerabilities have always been an essential issue in cyberspace, for which many vulnerability detection techniques have been investigated. Among them, deep learning-based detection techniques have demonstrated promising detection results. However, due to the various programming patterns of developers, vulnerabilities are usually associated with the code context, such as Internet of Things (IoT) programs. Therefore, we propose a contextual embedding model to integrate three hybrid models, CLSTM, CBiLSTM (sequential structure), and CNN-BiLSTM (parallel structure), based on the code characteristics of IoT applications. To further improve the precision and robustness, we apply information augment by adding synthetic data to the real-world vulnerability data to address the severe data imbalance and facilitate neural models learning vulnerability patterns. The new method inherits the architecture of CodeBERT with multiheaded attention mechanisms and learns a richer set of vulnerable code patterns with long-range context dependencies when processing code sequence data. To assess the effectiveness of hybrid contextual embedding, we contrast the neural network models developed using the representations obtained from the three embedding methods, including CodeBERT, Word2Vec, and FastText. The experiments involve IoT applications and well-known open-source APIs. The results show that the hybrid model built based on the neural features outperforms the non-hybrid model in vulnerability detection.
Estimating and detecting random processes on the unit circle

Detecting random processes on a circle has been studied for many decades. The Neyman-Pearson detector, which evaluates the likelihood ratio, requires first the conditional mean estimate of the circle-valued signal given noisy measurements, which is then correlated with the measurements for detection. This is the estimator-correlator detector. However, generating the conditional mean estimate of the signal is very rarely solvable. In this paper, we propose an approximate estimator-correlator detector by estimating the truncated moments of the signal, with estimated signal substituted into the likelihood ratio. Instead of estimating the random phase, we estimate the complex circle-valued signal directly. The effectiveness of the proposed method in terms of estimation and detection is shown through numerical experiments, where the tracking accuracy and receiver operating curves, compared with the extended Kalman filter are shown under various process/measurement noise.
Recognition of Vowels, Consonants and Compound Character Sequences from Ancient Tamil Stone Inscriptions using Deep Neural Networks

Tamil Stone Inscriptions are ancient handwritten documents engraved on stone that contain a veritable mine of information and traditional knowledge. With reference to the authenticated sources, around 65% Indian inscriptions were found in Tamil language. Amidst many inscriptions character recognition studies that have been published for different languages, no significant effort has been taken for Tamil language. There are few high performances online Handwritten Tamil OCRs for modern Tamil alphabets, but they perform with less than 20% accuracy while dealing stone inscription characters since the alphabets are of ancient form and also the difference between background and foreground is very meager in stone inscriptions. Most of the existing character recognition studies for Handwritten Tamil scripts have relied upon the widely used Hidden Markov Model (HMM), in spite of its familiar shortcomings and few reported in ANN. Though character recognition from the images of inscribed documents is challenging because of the complex character structure of the Tamil language scripts and other artifacts like aging and degradation, modern techniques needs to be developed to digitize such inscriptions and preserves them as electronic documents. In this paper, a novel approach is proposed for recognizing the inscription characters from the ancient Tamil stone inscriptions based on two recently developed models of Convolutional Neural Networks and Recurrent Neural Network (RNN). Camera captured stone inscriptions script images are taken as input and enhanced for clarity through various image enhancement techniques like filtering, luminous, erosion, dilation and blurring. Project profile based character segmentation is done for extracting individual characters out of script image. Over segmentation reduction is done for eliminating touching and broken characters. The character recognition is done in twofold. (i) Recognition of single characters (vowels and consonants) using Convolutional Neural Network (ii) Recognition of compound characters using Recurrent Neural Networks-BLSTM model. A meticulous test on large datasets has been performed to evaluate the performance of the proposed approach. Experimental results show that the proposed CNN based system achieved training accuracy of 88% and validation accuracy of 94% is obtained (Included RNN accuracy). The system performance is evaluated on various test cases in each phase and the limitations have been identified.
Speeding up Machine Learning Inference on Edge Devices by Improving Memory Access Patterns using Coroutines

We demonstrate a novel method of speeding up large iterative tasks such as machine learning inference. Our approach is to improve the memory access pattern, taking advantage of coroutines as a programming language feature to minimise the developer effort and reduce code complexity. We evaluate our approach using a comprehensive set of bench-marks run on three hardware platforms (one ARM and two Intel CPUs). The best observed performance boosts were 65% for scanning the nodes in a B+ tree, 34% for support vector machine inference, 12% for image pixel normalisation, and 15.5% for two dimensional convolution. Performance varied with data size, numeric type, and other factors, but overall the method is practical and can lead to significant improvements for edge computing.
A Usability-focused Method to Design a Cybersecurity MOOC for Children

Teaching cybersecurity to children is not a trivial task due to the complexity of explaining online-threat-related topics such as sexting or cyberbullying. Moreover, most of the courses designed to raise awareness in these controversial subjects has been mainly produced in English. However, during the Covid-19 pandemic, getting access to Spanish-based training for protecting children online became a priority for parents in countries such as Ecuador, which was not prepared to have children studying at home whilst being exposed to such threats. In this paper, we show that it is feasible to design a virtual course (MOOC) to teach cybersecurity to Spanish-speaking children with acceptable usability levels and minor usability issues. Our proposed methodology combines ADDIE, DPIPE and UDL for developing the course, and uses both SUS and Nielsen as methods to assess the MOOC from an usability point of view. For enhancing user engagement, our course uses novel 3D anthropomorphic characters and storyboards to aid the learning process. Regarding usability results, our proposed MOOC has very high and acceptable scores, with around 86/100 using the SUS scale, and less than 3 in the Nielsen score.
Recommendation Algorithm Based on Deep Light Graph Convolution Network in Knowledge Graph

Recently, recommendation algorithms based on Graph Convolution Network (GCN) have achieved many surprising results thanks to the ability of GCN to learn more efficient node embeddings. However, although GCN shows powerful feature extraction capability in user-item bipartite graphs, the GCN-based methods appear powerless for knowledge graph (KG) with complex structures and rich information. In addition, all of the existing GCN-based recommendation systems suffer from the over-smoothing problem, which results in the models not being able to utilize higher-order neighborhood information, and thus these models always achieve their best performance at shallower layers. In this paper, we propose a Deep Light Graph Convolution Network for Knowledge Graph (KDL-GCN) to alleviate the above limitations. Firstly, the User-Entity Bipartite Graph approach (UE-BP) is proposed to simplify knowledge graph, which leverages entity information by constructing multiple interaction graphs. Secondly, a Deep Light Graph Convolution Network (DLGCN) is designed to make full use of higher-order neighborhood information. Finally, experiments on three real-world datasets show that the KDL-GCN proposed in this paper achieves substantial improvement compared to the state-of-the-art methods.
Dialogue-to-Video Retrieval

Recent years have witnessed an increasing amount of dialogue/conversation on the web especially on social media. That inspires the development of dialogue-based retrieval, in which retrieving videos based on dialogue is of increasing interest for recommendation systems. Different from other video retrieval tasks, dialogue-to-video retrieval uses structured queries in the form of user-generated dialogue as the search descriptor. We present a novel dialogue-to-video retrieval system, incorporating structured conversational information. Experiments conducted on the AVSD dataset show that our proposed approach using plain-text queries improves over the previous counterpart model by 15.8% on R@1. Furthermore, our approach using dialogue as a query, improves retrieval performance by 4.2%, 6.2%, 8.6% on R@1, R@5 and R@10 and outperforms the state-of-the-art model by 0.7%, 3.6% and 6.0% on R@1, R@5 and R@10 respectively.
Intention-Aware Neural Networks for Question Paraphrase Identification

We tackle Question Paraphrasing Identification (QPI), a task of determining whether a pair of interrogative sentences (i.e., questions) are paraphrases of each other, which is widely applied in information retrieval and question answering. It is challenging to identify the distinctive instances which are similar in semantics though holding different intentions. In this paper, we propose an intention-aware neural model for QPI. Question words (e.g., “when”) and blocks (e.g., “what time”) are extracted as features for revealing intentions. They are utilized to regulate pairwise question encoding explicitly and implicitly, within Conditional Variational AutoEncoder (CVAE) and multi-task VAE frameworks, respectively. We conduct experiments on the benchmark corpora QQP, LCQMC and BQ, towards both English and Chinese QPI tasks. Experimental results show that our method yields generally significant improvements compared to a variety of PLM-based baselines (BERT, RoBERTa and ERNIE), and it outperforms the state-of-the-art QPI models. It is also proven that our method doesn’t severely reduce the overall efficiency, which merely extends the training time by 12.5% on a RTX3090. All the models and source codes will be made publicly available to support reproducible research.
HADA: A Graph-Based Amalgamation Framework in Image-text Retrieval

Many models have been proposed for vision and language tasks, especially the image-text retrieval task. State-of-the-art (SOTA) models in this challenge contain hundreds of millions of parameters. They also were pretrained on large external datasets that have been proven to significantly improve overall performance. However, it is not easy to propose a new model with a novel architecture and intensively train it on a massive dataset with many GPUs to surpass many SOTA models already available to use on the Internet. In this paper, we propose a compact graph-based framework named HADA, which can combine pretrained models to produce a better result rather than starting from scratch. Firstly, we created a graph structure in which the nodes were the features extracted from the pretrained models and the edges connecting them. The graph structure was employed to capture and fuse the information from every pretrained model. Then a graph neural network was applied to update the connection between the nodes to get the representative embedding vector for an image and text. Finally, we employed cosine similarity to match images with their relevant texts and vice versa to ensure a low inference time. Our experiments show that, although HADA contained a tiny number of trainable parameters, it could increase baseline performance by more than $$3.6\%$$ in terms of evaluation metrics on the Flickr30k dataset. Additionally, the proposed model did not train on any external dataset and only required a single GPU to train due to the small number of parameters required. The source code is available at https://github.com/m2man/HADA .
Temporal Natural Language Inference: Evidence-Based Evaluation of Temporal Text Validity

It is important to learn whether text information remains valid or not for various applications including story comprehension, information retrieval, and user state tracking on microblogs and via chatbot conversations. It is also beneficial to deeply understand the story. However, this kind of inference is still difficult for computers as it requires temporal commonsense. We propose a novel task, Temporal Natural Language Inference, inspired by traditional natural language reasoning to determine the temporal validity of text content. The task requires inference and judgment whether an action expressed in a sentence is still ongoing or rather completed, hence, whether the sentence still remains valid, given its supplementary content. We first construct our own dataset for this task and train several machine learning models. Then we propose an effective method for learning information from an external knowledge base that gives hints on temporal commonsense knowledge. Using prepared dataset, we introduce a new machine learning model that incorporates the information from the knowledge base and demonstrate that our model outperforms state-of-the-art approaches in the proposed task.