Reviewerly/exHarmony huggingface dataset:
Point of Interest Recommendation Method Based on Bidirectional Matrix and Deep Belief Network

Abstract Point‐of‐interest (POI) recommendation has always faced serious data sparsity problem. Most of the existing POI recommendation methods do not consider the bidirectional relationship between the user‐preference for locations and the location‐ attractiveness to users in the original dataset when solving the sparsity. For this problem, a point‐of‐interest recommendation method based on bidirectional matrix and deep belief network (POI_DBNBM) is proposed in this paper. Specially, from the perspectives of users and locations, we first construct two different matrices with bidirectional relationship by mapping the original dataset, and we use deep belief network (DBN) with strong learning ability to predict the two matrices. Then, a local–global weight optimization method is developed to weight and fuse the two result matrices obtained by DBN, so as to achieve the more precise user's rating prediction matrix for locations. The abundant experimental studies and evaluations are conducted on FourSquare dataset. Results are encouraging and better than those previously reported on the dataset. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Visual Domain Adaptation through Locality Information

Traditional machine learning methods have always performed the learning tasks solely. The models of these conventional methods have to be built from scratch. The idea of domain adaptation can overcome this learning procedure by adapting the knowledge gained from one domain to another. With datasets from several domains, this research proposes a novel strategy for unsupervised visual domain adaptation. Existing transfer learning approaches try to reduce the domain shift using maximum mean discrepancy metric. We propose a combined framework termed Visual Domain Adaptation through Locality Information (DALI) that lowers the geometrical distance between the domains while probabilistically addressing the shortcomings. The proposed approach employs the input data features to develop a technique that assists in inducing the transfer of information from one domain to another. Extensive experimental findings demonstrate that the proposed method DALI excels over traditional domain adaptation algorithms as well as CNN architectures on a variety of cross-domain object, facial, and digit recognition tasks including Office-Caltech, Office-Home, PIE and COIL datasets. Our proposed model has improved the mean accuracy to 69.69%, exceeding the most recent state-of-the-art techniques when performed a comprehensive analysis on the Office-Home data.
Neural age screening on question answering communities

For online social networks, demographic analysis is absolutely essential for improving their services in many ways. It is instrumental in understanding their different audiences, members and competitors. As well as that, it is pivotal in designing effective personalization and contextualization strategies, especially for displaying and creating better content. There is, for this reason, a great bulk of research into how demographic variables are characterized and how they impact online platforms such as Facebook and Twitter. But surprisingly, only a handful of works delve into their characterization and effect on community Question-Answering (cQA) websites. In this particular context, the subject of age demographics remains largely unexplored. This paper takes the lead on interpreting automatic age recognition on CQAs (a.k.a. age screening) as a regression task. To this effect, it compares state-of-the-art graph-based neural network regression and embedding models on a massive activity-graph encompassing ca. 16 and 837 million nodes (members) and edges, respectively. For this study, a large-scale subset of ca. 657,000 community fellows was automatically associated with their age via aligning their profile texts with a limited number of linguistic patterns. In short, our results show that Node2vec significantly outperforms other embeddings regardless of the regression model used for casting predictions. When this embedding is combined with Artificial Neural Network Regressions, we obtained our best configuration scoring a Root Mean Square Error (RMSE) of 8.39. An interesting qualitative feature of this embedding space is that age-based centroid vectors tend to form a trail ordered by age. Lastly, our outcomes also signal that activity graph based models can rival its counterparts based on image and textual inputs, paving the way for constructing effective multi-modal approaches.
Understanding User Experience in the Metaverse

Despite its growing popularity, the understanding of user experience in the metaverse is limited. In-depth interviews were conducted in a comparative context between gamers and metaverse users to understand their experience in the medium, and NVivo software was used to analyze the findings with Csikszentmihalyi's flow theory as a framework. While most of the flow antecedents and flow outcomes were intact, the ‘skill' aspect of flow seems to be different in metaverse, compared to gaming. Aspects such as ease of use, speed, vividness were felt highly by those familiar with metaverse, whereas the gamers more frequently used the medium and found it stimulating and trustworthy. The study offers rich perspectives for metaverse designers, ad agencies, marketing practitioners, and media personnel.
Internet of Vehicles (IoV) based Framework for Vehicle Degradation using Multidimensional Dynamic Time Warping (MDTW)

The anomalies on highways impose wear-tear which may cause degradation in prolonging of the vehicles. Although, past research has played an important role in detecting potholes and speed bumps with high accuracy. This study proposes an Internet of Vehicles (IoV) based framework for detecting road anomalies using Multidimensional Dynamic Time Warping (MDTW) for improving the F1 score and sensitivity. Furthermore, the vehicle degradation index caused by road anomalies is established. The system employs a Three-layer architecture comprising IoV, Fog, and cloud computing, utilizing their inherent capabilities as needed for the specific task at hand. The data is gathered using the IoV system, which includes an Arduino MEGA 2560 equipped with an ESP8266, M8N GPS, and MPU-6050. The fog layer computes the vehicle's jerk impact using onboard capabilities and a complementary filter for bandwidth optimization. The cloud layer, with powerful computing capabilities, evaluates the proposed MDTW method against existing techniques for detecting potholes and speed bumps, serving as a potential backbone for the model. The proposed approach achieved an F1 score of 0.8934 and 0.8852 for the studied datasets. A correlation was found between prolonging the lifespan of a vehicle and the frequency of jerks experienced during two trips, as indicated by the Vehicle Degradation Index with an average score of 0.7212. To summarize, the proposed framework can be used not only in the automobile industry, where production can be customized based on demographic factors but also in low-cost automatic monitoring and repair of roads.
Towards the Integration of Security Practices in Agile Software Development: A Systematic Mapping Review

Software development must be based on more than just the experience and capabilities of your programmers and your team. The importance of obtaining a quality product lies in the risks that can be exploited by software vulnerabilities, which can jeopardize organizational assets, consumer confidence, operations, and a broad spectrum of applications. Several methods, techniques, and models have been suggested and developed to address software security. However, only a few have solid evidence for creating secure software applications. The main objective of this paper is to survey the literature for methods or models suitable for considering the integration of security in all or some of the phases of the software development life cycle and which ones are most considered or neglected. This study represents the beginning of research to generate a methodology that integrates security practices in agile software development, allowing inexperienced developers to create more secure applications.
Vehicle logo detection using an IoAverage loss on dataset VLD100K-61

Abstract Vehicle Logo Detection (VLD) is of great significance to Intelligent Transportation Systems (ITS). Although many methods have been proposed for VLD, it remains a challenging problem. To improve the VLD accuracy, an Intersection over Average (IoAverage) loss is proposed for enhancing the bounding box regression. The IoAverage loss accelerates the convergence of bounding box regression than using the Intersection over Union (IoU) loss. In the experiments, IoAverage loss has been incorporated into the state-of-the-art object detection framework YOLOV5s, namely YOLOV5s-IoAv in this paper. The advantages of the IoAverage loss are verified on the PASCAL VOC2007 datasets. The results of using the IoAverage loss show performance gains of + 15.27% mAP0.5 and + 30.87% mAP0.5:0.95 higher than that of the Complete IoU (CIoU) loss. The application of YOLOV5s-IoAv is implemented to VLD on dataset VLD100K-61. VLD100K-61 is a self-collected dataset containing 100,041 images supplied by traffic surveillance cameras in the real world from 61 categories. YOLOV5s-IoAv achieves performance gains as + 15.27% mAP0.5:0.95 for VLD than YOLOV5s-CIoU. The proposed method yields the mAP0.5 value of up to 0.992 on the dataset VLD100K-61, providing a promising solution to vehicle logo recognition applications.
Digital traces and physical activities: opportunities, challenges and pitfalls

The strong integration of consumer electronics in everyday life offers many new investigative opportunities. In particular, digital traces from smartphones, smartwatches and activity trackers can now increasingly be used to infer information about actions performed by their users in the physical world that might not be obtainable from any other types of forensic evidence. While potentially very valuable from an investigative perspective, making forensically justifiable statements about such traces can sometimes be more difficult than expected. Requirements for this have not yet received much attention in the digital forensic literature. To help filling this gap, we describe the principles we use in determining the evidential value of such traces, which emphasize the need for experimental verification. For such research, aimed at determining the evidential value of these traces, we coin the term data2activity. In this paper, we devote attention to the potential and limitations of data2activity traces, focusing on challenges and giving two examples to illustrate potential pitfalls in interpreting data. Finally, future research directions into data2activity traces are indicated that, in our opinion, should be given attention. These include development of future-proof data acquisition and storage methodology, enabling division-of-effort and sharing of information, as well as development of labeling methodology for free-living experiments.
The Critical Success Factors of Metaverse Adoption in Education

Metaverse is considered one of the platforms that can be used for teaching and learning in higher education. In order to develop, adopt, and use an innovative application or system, the critical success factors are crucial. Consumer behaviours and adoption are crucial to the development of a proposed system based on the metaverse in order to improve the system's usefulness. Consequently, the goal of this study is to evaluate various theories, models, and previous research related to the innovation adoption in order to draw conclusions about the critical success factors for the adoption of the metaverse in education and consumer intention to use.
In-between worlds: Chilean university lecturers’ experiences of teaching transition between face-to-face and virtual reality contexts during the COVID-19 pandemic

The advent of new technology is breaking the boundaries of traditional teaching and learning patterns with virtual worlds (VW) creating new frontiers in education. Previous research has explored the use of VW within educational settings. However, limited studies have investigated the transition processes that educators experience by adopting VW based online tools during the COVID-19 pandemic. This qualitative exploratory study investigated 18 Chilean lecturers' teaching experiences using a three-dimensional computer-mediated environment: Second Life. Findings suggest that changing from traditional to virtual teaching context is a complex process, which (re)shaped the lecturers' various senses of identity and agency towards different instructional approaches resulting in the sense of in-betweenness with multiple digital competencies. These changes indicated that they taught in an 'in-between' mode mapped by different teaching mediations. The participants' teaching experiences of shaping a sense of in-betweenness could provide a unique theoretical lens to explore instructors' teaching experiences from traditional to a technology-mediated online setting.
A Survey of Full-Cycle Cross-Modal Retrieval: From a Representation Learning Perspective

Cross-modal retrieval aims to elucidate information fusion, imitate human learning, and advance the field. Although previous reviews have primarily focused on binary and real-value coding methods, there is a scarcity of techniques grounded in deep representation learning. In this paper, we concentrated on harmonizing cross-modal representation learning and the full-cycle modeling of high-level semantic associations between vision and language, diverging from traditional statistical methods. We systematically categorized and summarized the challenges and open issues in implementing current technologies and investigated the pipeline of cross-modal retrieval, including pre-processing, feature engineering, pre-training tasks, encoding, cross-modal interaction, decoding, model optimization, and a unified architecture. Furthermore, we propose benchmark datasets and evaluation metrics to assist researchers in keeping pace with cross-modal retrieval advancements. By incorporating recent innovative works, we offer a perspective on potential advancements in cross-modal retrieval.
Density of Some Special Sequences Modulo 1

In this paper, we explicitly describe all the elements of the sequence of fractional parts {af(n)/n}, n=1,2,3,…, where f(x)∈Z[x] is a nonconstant polynomial with positive leading coefficient and a≥2 is an integer. We also show that each value w={af(n)/n}, where n≥nf and nf is the least positive integer such that f(n)≥n/2 for every n≥nf, is attained by infinitely many terms of this sequence. These results combined with some earlier estimates on the gaps between two elements of a subgroup of the multiplicative group Zm* of the residue ring Zm imply that this sequence is everywhere dense in [0,1]. In the case when f(x)=x this was first established by Cilleruelo et al. by a different method. More generally, we show that the sequence {af(n)/nd}, n=1,2,3,…, is everywhere dense in [0,1] if f∈Z[x] is a nonconstant polynomial with positive leading coefficient and a≥2, d≥1 are integers such that d has no prime divisors other than those of a. In particular, this implies that for any integers a≥2 and b≥1 the sequence of fractional parts {an/nb}, n=1,2,3,…, is everywhere dense in [0,1].
Non-interactive and privacy-preserving neural network learning using functional encryption

Fast and reliable modeling of distributed data with sensitive information is a major goal of privacy-preserving machine learning (PPML). Artificial Neural Networks (ANNs) are powerful and scalable, making them suitable to settle large-scale and highly complex machine learning tasks. Since a large number of neurons and various non-linear functions are embedded in ANNs, it is still a huge challenge to train an ANNs model in the encrypted-domain. Majority of existing approaches for PPML require multiple communications among data owners and cloud servers, leading to a substantial overhead of computation and data transmission costs, and are restricted to approximation models by polynomials or piecewise linear functions. Here, to facilitate the integration of any training data from any data owner without violating privacy constraints, we introduce a non-interactive and lossless ANNs training approach that unites mask matrix, function encryption for inner-product and coordination while maintaining confidentiality with the help of Internet Service Providers (ISPs), thereby going beyond those schemes with interactive fashion and using approximation substitutions. To illustrate the feasibility and efficiency of using our method to develop ANNs classifiers using distributed data, we choose two well-known MNIST datasets with a large amount of image data with high dimensionality in PPML field. We show the protocol is feasible to use in practice while achieving high accuracy. Furthermore, the evaluation reveals that the computational efficiency is improved at least 25 times compared with the state-of-the-art privacy-preserving ANNs approach.
FSConv: Flexible and separable convolution for convolutional neural networks compression

Because of limited computation resources, convolutional neural networks (CNNs) are difficult to deploy on mobile devices. To overcome this issue, many methods have successively reduced parameters in CNNs with the idea of removing redundancy among feature maps. We observe similarities between feature maps at the same layer but not complete consistency. Intuitively, the difference between similar feature maps is an essential ingredient for the success of CNNs. Therefore, we propose a flexible and separable convolution (FSConv) in a different perspective to embrace redundancy while requiring less computation, which can implicitly cluster feature maps into different clusters without introducing similarity measurements. Our proposed model extracts intrinsic information from the representative part through ordinary convolution in each cluster and reveals tiny hidden details from the redundant part through groupwise/depthwise convolution. Experimental results demonstrate that FSConv-equipped networks always perform better than previous state-of-the-art CNNs compression algorithms. Code is available at https://github.com/Clarkxielf/FSConv-Flexible-and-Separable-Convolution-for-Convolutional-Neural-Networks-Compression.
Introduction to Metaverse and Consumer Behaviour Change

A virtual digital environment is created using the metaverse, which also connects the real world. The existence of information technology allows us to do new tasks or carry out routine tasks more effectively. The extended reality, or “metaverse,” allows for new kinds of captivating telepresence but may also make mundane activities easier. These technologies help us in our employment, education, healthcare, consumption, and entertainment more and more, but they also present a number of obstacles. The concerns discussed in this chapter are why and will customers adopt the fully immersive territory for various activities like shopping and purchasing any products including bank products.
Understanding Consumer Behavior in Virtual Ecosystems

This chapter outlines the understanding of consumer behavior in the virtual ecosystem and the adoption of immersive technologies in the metaverse among consumers. The metaverse is based on the convergence of technologies that enable multisensory interactions with virtual environments, digital objects, and people such as virtual reality (VR) and augmented reality (AR). This chapter covers the impact of consumer behavior in metaverse followed by the strategies adopted by entrepreneurs to work in metaverse to reach consumers. This chapter outlines the metaverse market size, trends, growth, and forecast. This chapter offers a comprehensive framework that examines the metaverse development under the dimensions of metaverse ecosystem. This chapter further aims at identifying the challenges and issues faced in adoption of immersive technologies in metaverse. The metaverse also poses numerous problems to our customary modes of communication and teamwork. The metaverse is significant for retailers for a number of reasons in addition to remaining on the bleeding edge of technology.
Two-Dimensional Exponential Sparse Discriminant Local Preserving Projections

The two-dimensional discriminant locally preserved projections (2DDLPP) algorithm adds a between-class weighted matrix and a within-class weighted matrix into the objective function of the two-dimensional locally preserved projections (2DLPP) algorithm, which overcomes the disadvantage of 2DLPP, i.e., that it cannot use the discrimination information. However, the small sample size (SSS) problem still exists, and 2DDLPP processes the whole original image, which may contain a large amount of redundant information in the retained features. Therefore, we propose a new algorithm, two-dimensional exponential sparse discriminant local preserving projections (2DESDLPP), to address these problems. This integrates 2DDLPP, matrix exponential function and elastic net regression. Firstly, 2DESDLPP introduces the matrix exponential into the objective function of 2DDLPP, making it positive definite. This is an effective method to solve the SSS problem. Moreover, it uses distance diffusion mapping to convert the original image into a new subspace to further expand the margin between labels. Thus more feature information will be retained for classification. In addition, the elastic net regression method is used to find the optimal sparse projection matrix to reduce redundant information. Finally, through high performance experiments with the ORL, Yale and AR databases, it is proven that the 2DESDLPP algorithm is superior to the other seven mainstream feature extraction algorithms. In particular, its accuracy rate is 3.15%, 2.97% and 4.82% higher than that of 2DDLPP in the three databases, respectively.