Reviewerly/exHarmony huggingface dataset:
Engineering the Metaverse for Innovating the Electronic Business: A Socio-technological Perspective

The metaverse is a hybrid world, mixing real, augmented, and virtual spaces, in which people (as users) can be represented in a three-dimensional way, with an avatar, activating services, interacting with other users/avatars, and exploiting/exploring innovative experiences. The difference with other virtual reality worlds as known so far is essentially the feeling of living spaces with other avatars, with almost infinite possible applications, through immersive access, emersive configurations, and continuous interactions among real, augmented, and virtual dimensions. Several companies have been trying to explore the metaverse for their business, and naturally, Facebook/Meta most of all. This study proposes an analysis about definition, conceptualisation, and activation, from a socio-technological point of view, of the metaverse, providing a SWOT analysis for its evaluation in a business perspective.
RAPid-Learn: A Framework for Learning to Recover for Handling Novelties in Open-World Environments

We propose RAPid-Learn (Learning to Recover and Plan Again), a hybrid planning and learning method, to tackle the problem of adapting to sudden and unexpected changes in an agent’s environment (i.e., novelties). RAPid-Learn is designed to formulate and solve modifications to a task’s Markov Decision Process (MDPs) on-the-fly. It is capable of exploiting the domain knowledge to learn action executors which can be further used to resolve execution impasses, leading to a successful plan execution. We demonstrate its efficacy by introducing a wide variety of novelties in a gridworld environment inspired by Minecraft, and compare our algorithm with transfer learning baselines from the literature. Our method is (1) effective even in the presence of multiple novelties, (2) more sample efficient than transfer learning RL baselines, and (3) robust to incomplete model information, as opposed to pure symbolic planning approaches.
Construction and implementation of Shandong University entrance guard system based on face recognition

Due to the feature of special uniqueness and stability, face recognition has been widely used in more and more industries. As a typical biometric technology, it's quite suitable for the normalized epidemic prevention and control. At present, face recognition has become a popular research in the door security system. This paper introduces the current development of face recognition at home and abroad, and draws the conclusion that the door security construction in Shandong University can strengthen the campus security and refined management as well as releasing manpower and reducing management costs. Based on analysis of different situations, this paper briefly describes the authentication process of door security system based on face recognition and campus card, elaborates the system structure consisted of the face recognition terminal, ID registration terminal, door security management platform, appointment platform and original third-party door security system. Finally, it's concluded that dynamic scene detection and face tracking and recognition at different points can provide effective new concepts and approaches for the public health in the future.
EARLY PREDICTION OF CERVICAL CANCER USING MACHINE LEARNING TECHNIQUES

According to recent studies and statistics, Cervical Cancer (CC) is one of the most common causes of death worldwide, and mainly in the developing countries. CC has a mortality rate around 60%, in less developing countries and the percentages could go even higher, due to poor screening processes, lack of sensitization, and several other reasons. Therefore, this paper aims to utilize the high capabilities of machine learning techniques in the early prediction of CC. In specific, three well-known feature selection and ranking methods have been used to identify the most significant features that help in the diagnosis process. Also, eighteen different classifiers that belong to six learning strategies have been trained and extensively evaluated against a primary data which consists of five hundred images. Moreover, an investigation regarding the problem of imbalance class distribution which is common in medical dataset is conducted. The results revealed that LWNB and RandomForest classifiers showed the best performance in general, and considering four different evaluation metrics. Also, LWNB and Logistic classifiers were the best choices to handle the problem of imbalance class distribution which is common in medical diagnosis task. The final conclusion could be made is that using an ensemble model which consists of several classifiers such as LWNB, RandomForest, and Logistic is the best solution to handle this type of problems.
Homophily Learning-Based Federated Intelligence: A Case Study on Industrial IoT Equipment Failure Prediction

Federated learning is an emerging distributed machine learning paradigm that can break through data silos and make use of data from different clients in a secure way. However, for deep neural networks in federated learning, the models on clients may learn the same pattern with different weight distributions despite the same data distribution of local data sets, which limits the performance of neural networks after weight fusions. Therefore, in this article, we propose a homophily learning-based federated intelligence (HLFI) approach, where hierarchical federated learning strategy and dynamic elimination learning strategy are designed to alleviate the problem. The experiments on equipment failure prediction show that the proposed approach can improve the failure prediction F1-score up to 9.32%. Our approach also has good generalization capabilities and can be applied in other federated learning methods to improve the model performance.
Transformer Based Line Segment Classifier with Image Context for Real-Time Vanishing Point Detection in Manhattan World

Previous works on vanishing point detection usually use geometric prior for line segment clustering. We find that image context can also contribute to accurate line classification. Based on this observation, we propose to classify line segments into three groups according to three unknown-but-sought vanishing points with Manhattan world assumption, using both geometric information and image context in this work. To achieve this goal, we propose a novel Transformer based Line segment Classifier (TLC) that can group line segments in images and estimate the corresponding vanishing points. In TLC, we design a line segment descriptor to represent line segments using their positions, directions and local image contexts. Transformer based feature fusion module is used to capture global features from all line segments, which is proved to improve the classification performance significantly in our experiments. By using a network to score line segments for outlier rejection, vanishing points can be got by Singular Value Decomposition (SVD) from the classified lines. The proposed method runs at 25 fps on one NVIDIA 2080Ti card for vanishing point detection. Experimental results on synthetic and real-world datasets demonstrate that our method is superior to other state-of-the-art methods on the balance between accuracy and efficiency, while keeping stronger generalization capability when trained and evaluated on different datasets.
Augmented Reality in Education and Remote Sensing

Augmented Reality (AR) is a technology that has attracted a lot of attention in recent times, especially, after PokemonGo and quite recently with the gaining popularity of metaverse. AR is not a new technology but its development to maturity has always been dependent on many other technologies like computer vision, mobile processing, optics, etc. We are at that stage in the development cycle of AR where all other technologies not only support but can benefit from its use. This research presents one such innovation in the domain of education - for school children or enthusiasts about geospatial technologies such as remote sensing. In this paper, we have discussed the idea, methodology, and demonstration of one such application which establishes a future trend for use of AR in the education of Geospatial Technology. Different types of remote sensing data, their acquisition method, satellite technology, and applications area are explained using AR for a more interactive experience. Learning about geospatial technology will be helpful as the new frontier of Geoinformation is enhancing everyone's day-to-day life and will help in boosting interest in this field.
CLIP meets GamePhysics

Gameplay videos contain rich information about how players interact with the game and how the game responds. Sharing gameplay videos on social media platforms, such as Reddit, has become a common practice for many players. Often, players will share game-play videos that showcase video game bugs. Such gameplay videos are software artifacts that can be utilized for game testing, as they provide insight for bug analysis. Although large repositories of gameplay videos exist, parsing and mining them in an effective and structured fashion has still remained a big challenge. In this paper, we propose a search method that accepts any English text query as input to retrieve relevant videos from large repositories of gameplay videos. Our approach does not rely on any external information (such as video metadata); it works solely based on the content of the video. By leveraging the zero-shot transfer capabilities of the Contrastive Language-Image Pre-Training (CLIP) model, our approach does not require any data labeling or training. To evaluate our approach, we present the GamePhysics dataset consisting of 26,954 videos from 1,873 games, that were collected from the GamePhysics section on the Reddit website. Our approach shows promising results in our extensive analysis of simple queries, compound queries, and bug queries, indicating that our approach is useful for object and event detection in gameplay videos. An example application of our approach is as a gameplay video search engine to aid in reproducing video game bugs. Please visit the following link for the code and the data: https://asgaardlab.github.io/CLIPxGamePhysics/
Weak Signal Detection Based on Lifting Wavelet Threshold Denoising and Multi-Layer Autocorrelation Method

To solve the problem that the weak signal is difficult to detect under a strong background noise, a detection method based on lifting wavelet threshold denoising and multi-layer autocorrelation method is proposed. Firstly, the original signal is denoised by lifting wavelet threshold to improve the signal-to-noise ratio. Secondly, the multi-layer autocorrelation function of the noise-reconstructed signal is calculated, and its time-frequency signature are analyzed. Finally, the combined algorithm is used on weak signals with low signal-to-noise ratio to extract weak signal features. Simulation and experimental results demonstrate that the proposed method can detect weak signal features buried in the heavy noise effectively. The proposed method is compared with the traditional noise reduction method, which reflects its effectiveness and superiority.
Opportunity Cost of Action Bias in Cybersecurity Incident Response

The hours and days immediately following the discovery of a cyber intrusion can be stressful and chaotic for victims. Without a documented and well-rehearsed incident response plan, people are prone to costly fear-based reactions. Action bias is the human tendency to favor action over inaction. It feels better for victims to do something even if rushed decisions are suboptimal to thoughtful, careful alternatives. Furthermore, the null baseline of doing nothing or watchful waiting can sometimes be advantageous. This paper describes an application of opportunity cost to action bias. While these insights are not yet backed by empirical data, this is the first work to examine the intersection of opportunity cost with action bias in cybersecurity incident response. Using Sony Pictures Entertainment as a case study, we discuss the implications of opportunity costs from acting prematurely and, conversely, the opportunity costs of waiting to act.
Instance Segmentation with Cross-Modal Consistency

Segmenting object instances is a key task in machine perception, with safety-critical applications in robotics and autonomous driving. We introduce a novel approach to instance segmentation that jointly leverages measurements from multiple sensor modalities, such as cameras and LiDAR. Our method learns to predict embeddings for each pixel or point that give rise to a dense segmentation of the scene. Specifically, our technique applies contrastive learning to points in the scene both across sensor modalities and the temporal domain. We demonstrate that this formulation encourages the models to learn embeddings that are invariant to viewpoint variations and consistent across sensor modalities. We further demonstrate that the embeddings are stable over time as objects move around the scene. This not only provides stable instance masks, but can also provide valuable signals to downstream tasks, such as object tracking. We evaluate our method on the Cityscapes and KITTI-360 datasets. We further conduct a number of ablation studies, demonstrating benefits when applying additional inputs for the contrastive loss.
ETFPOS-IDF: A Novel Term Weighting Scheme for Examination Question Classification Based on Bloom’s Taxonomy

Numerous earlier studies focused on the term weighting scheme to increase examination question classification accuracy based on Bloom’s Taxonomy (BT). While determining the cognitive level of the examination question, all the terms present in the question are not equally significant. Verbs are the most important parts of speech while assigning weights to the terms. However, two types of verbs may be present in the questions: BT and supporting. BT verbs have a higher impact on determining the cognitive level of a question than supporting verbs. Nevertheless, the proposed schemes of past studies assigned equal weight to both types of verbs. Therefore, this study aims to introduce the term weighting scheme ETFPOS-IDF, which assigns BT a higher weight than supporting verbs. The BT verbs were identified based on their position in the questions. Three datasets and three classifiers: Support Vector Machine, Artificial Neural Network, and Random Forest, were used in this study. Two evaluation metrics: accuracy and F1 score, were used to evaluate the performance of the proposed model. The experiment results showed that the proposed ETFPOS-IDF outperformed all the schemes introduced by earlier studies in examination question classification and achieved 0.749 in accuracy and 0.746 in F1 score. The finding of this study demonstrates that distinguishing between different verb types is significant in reducing the misclassification of examination questions. This research contributed by introducing a novel term weighting scheme in classifying examination questions based on BT. Future work may involve identifying the optimal weight for both types of verbs, evaluating the proposed scheme with a larger dataset, and comparing the performance with deep learning.
CUAD-Mo: Continuos Unsupervised Anomaly Detection on Machining Operations

Computer Numerical Control (CNC) machine tools have become essential elements in manufacturing industries because they allow optimizing time and effort during production. Monitoring and diagnostic of such machines is a particularly important task to limit the number of defective products and to meet the required quality goals. In the context of Industry 4.0, Machine Learning approaches for Anomaly Detection have proven to be successfully used to detect abnormal events in industrial environments. When consumed by human operators in Decision Support Systems, it is desirable for AD technologies to indicate what particular feature triggered the abnormal prediction, thus, the usage of explainable methods is of fundamental importance to enable Root Cause Analysis and to allow timely and adequate interventions. In this work, we propose an approach, named CUAD-Mo (Continuos Unsupervised Anomaly Detection on Machining Operations), an Anomaly Detection pipeline that, from a set of features extracted from the time series obtained during the execution of a CNC machine, obtains an anomaly score using the Isolation Forest model and provide post-hoc explanations about the score obtained. CUAD-Mo was evaluated using real time data from a CNC machine used in production and achieved satisfactory both qualitative and quantitative results.
Digital Trust and Cooperation with an Integrative Digital Social Contract

I argue for the role of trust and cooperation as part of the foundation of digital commerce by expanding the reach of the Integrative Social Contract Theory (ISCT) of Donaldson and Dunfee (Ties that Bind, 1999). I propose that a digital business community can be a community in the morally relevant ways that Donaldson and Dunfee describe, and that the basic framework of ISCT can apply to the digital business world similarly to its application in the offline business world. I then analyze the roles of trust and cooperation in e-commerce, showing how they are important to the digital business community and explaining their moral relevance under a digital form of ISCT. I use the ISCT framework to show that trust and cooperation are an instantiation of the hypernorm of necessary social efficiency, and that authentic microsocial norms developed for the ends of trust and cooperation carry moral responsibility.
Anti-retroactive Interference for Lifelong Learning

Humans can continuously learn new knowledge. However, machine learning models suffer from drastic dropping in performance on previous tasks after learning new tasks. Cognitive science points out that the competition of similar knowledge is an important cause of forgetting. In this paper, we design a paradigm for lifelong learning based on meta-learning and associative mechanism of the brain. It tackles the problem from two aspects: extracting knowledge and memorizing knowledge. First, we disrupt the sample’s background distribution through a background attack, which strengthens the model to extract the key features of each task. Second, according to the similarity between incremental knowledge and base knowledge, we design an adaptive fusion of incremental knowledge, which helps the model allocate capacity to the knowledge of different difficulties. It is theoretically analyzed that the proposed learning paradigm can make the models of different tasks converge to the same optimum. The proposed method is validated on the MNIST, CIFAR100, CUB200 and ImageNet100 datasets. The code is available at https://github.com/bhrqw/ARI .
Transforming Tertiary Education Environments for Sustainable Development of the Sakhalin Region in Russia

The authors investigate the interests and needs of different groups of students: migrants, representatives of ethnic diasporas and indigenous population. Research methods include in-depth interview with the representatives of diverse students’ population and the university administration. The results showed that different groups of students have different needs and perspectives. The authors study the universities’ responds to the demands, needs and aspirations of the various ethnic groups living on Sakhalin Island. Sakhalin is Asia Pacific island region in Russian Federation which is regarded as a multicultural place. Historically, the population of the region has formed as diverse and multiethnic. International Oil projects caused the increase of trans-national migration. Sakhalin State University is trying to respond to the demands, needs and aspirations of the various ethnic groups living on the island. The paper observes courses and practices that incorporate cultural proficiency elements and educational activities identified as multicultural that develop intercultural skills enabled students to live in a multicultural world.
Exposure Map Fusion for Precise 3-D Reconstruction of High Dynamic Range Surfaces

Due to the insufficient dynamic measurement range of cameras, precise three-dimensional (3D) reconstruction of high dynamic range surfaces is a challenging problem for structured light techniques. The existing high dynamic range measurement techniques directly use image intensity for 3D reconstruction; however, image intensity is affected by intensity saturation and the low signal-to-noise ratio, which degrades the 3D reconstruction performances. To circumvent the problem, this paper presents the exposure map fusion method for precise 3D reconstruction of high dynamic range surfaces. Different from existing methods that use image intensity, the proposed method uses exposure maps for 3D reconstruction. The exposure map is proposed to circumvent the problem of intensity saturation caused by the insufficient measurement range of cameras. The frequency component is used to fuse multiple exposure maps to improve the signal-to-noise ratio. To evaluate the performance of the method, experiments are conducted by the proposed exposure map fusion method and the other existing methods to compare measurement accuracy. The experimental results show that the proposed exposure map fusion method improves the 3D reconstruction accuracy of high dynamic range surfaces, although the processing time of the proposed method increases due to its computational complexity.
Differential Cryptanalysis of K-Cipher

K-Cipher is an ultra low latency block cipher with variable-length parameters designed by Intel Labs. In this work, we analyze the security of K-Cipher and propose a differential cryptanalysis attack with the complexity of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{29.7}$</tex> for a variant of K-Cipher with state size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n=24$</tex> bits state and block size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m=8$</tex> bits. Our attack recovers the secret key and secret randomizer values with a total length of 240 bits in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim 30$</tex> minutes on a standard desktop machine. We show that it is possible to extend the same attack for an arbitrary set of parameters.
Application of Artificial Intelligence and Virtual Reality Technology in Online Course Education

A personalized English teaching system based on big data is studied and tested. Through the teacher student matching degree algorithm based on fuzzy control matrix algorithm, the teacher recommendation mechanism of English teaching system is innovated, so that students can match teachers who are more suitable for their learning habits. At the same time, students’ learning process is depicted and weak knowledge points are personalized studied, This paper uses portrait technology to estimate and evaluate the passing rate of students in routine English examination. In the internal test, the evaluation of student users on the software is much higher than that of teacher users. Therefore, in the subsequent development, incentive policies for teacher users should be developed to increase the stickiness of teacher users.