Reviewerly/exHarmony huggingface dataset:
Locality-preserving minimal perfect hashing of <i>k</i>-mers

Minimal perfect hashing is the problem of mapping a static set of n distinct keys into the address space {1,…,n} bijectively. It is well-known that n log 2(e) bits are necessary to specify a minimal perfect hash function (MPHF) f, when no additional knowledge of the input keys is to be used. However, it is often the case in practice that the input keys have intrinsic relationships that we can exploit to lower the bit complexity of f. For example, consider a string and the set of all its distinct k-mers as input keys: since two consecutive k-mers share an overlap of k-1 symbols, it seems possible to beat the classic log 2(e) bits/key barrier in this case. Moreover, we would like f to map consecutive k-mers to consecutive addresses, as to also preserve as much as possible their relationship in the codomain. This is a useful feature in practice as it guarantees a certain degree of locality of reference for f, resulting in a better evaluation time when querying consecutive k-mers.Motivated by these premises, we initiate the study of a new type of locality-preserving MPHF designed for k-mers extracted consecutively from a collection of strings. We design a construction whose space usage decreases for growing k and discuss experiments with a practical implementation of the method: in practice, the functions built with our method can be several times smaller and even faster to query than the most efficient MPHFs in the literature.
Gaussian Mixture Cardinalized Probability Hypothesis Density(GM-CPHD): A Distributed Filter Based on the Intersection of Parallel Inverse Covariances

A distributed GM-CPHD filter based on parallel inverse covariance crossover is designed to attenuate the local filtering and uncertain time-varying noise affecting the accuracy of sensor signals. First, the GM-CPHD filter is identified as the module for subsystem filtering and estimation due to its high stability under Gaussian distribution. Second, the signals of each subsystem are fused by invoking the inverse covariance cross-fusion algorithm, and the convex optimization problem with high-dimensional weight coefficients is solved. At the same time, the algorithm reduces the burden of data computation, and data fusion time is saved. Finally, the GM-CPHD filter is added to the conventional ICI structure, and the generalization capability of the parallel inverse covariance intersection Gaussian mixture cardinalized probability hypothesis density (PICI-GM-CPHD) algorithm reduces the nonlinear complexity of the system. An experiment on the stability of Gaussian fusion models is organized and linear and nonlinear signals are compared by simulating the metrics of different algorithms, and the results show that the improved algorithm has a smaller metric OSPA error than other mainstream algorithms. Compared with other algorithms, the improved algorithm improves the signal processing accuracy and reduces the running time. The improved algorithm is practical and advanced in terms of multisensor data processing.
Immersive Learning Experience Design (ILXD): Augmented Reality Mobile Application for Placing and Interacting with 3D Learning Objects in Engineering Education

The Computer Numerical Control (CNC) machine operates based on a numerical control program generated semi-automatically by a Computer Aided Manufacturing (CAM) system or manually by the operator. Repeated practice is required to operate a CNC machine. For an inexperienced operator (student), the practicum requires extensive use of materials and cutting instruments. In the meantime, the number of laboratory equipment for the CNC programming practicum remains limited, and the 3-axis CNC milling machine training unit is inadequate for the number of students. For this reason, we need a medium that can simulate the machining process to aid students in their early stages of learning. This research aims to create a mobile application for milling machine visualization based on augmented reality. This augmented reality (AR) application, "MM: CTU 3-Axis," or Machine Milling CNC Training Unit 3-Axis, was designed with 3D virtual objects to provide students with knowledge and practical experience. The application development model used Multimedia Development Life Cycle (MDLC). This research yields an Android-based augmented reality (AR) application called "MM: CTU 3-Axis" that does not require markers. The development and verification results demonstrated that this application could aid in learning CNC programming and machining practicum, as well as reduce the cost of using cutting tools and the cost of using materials due to repeated experiments on CNC machines.
Human intelligence-based metaverse for co-learning of students and smart machines

This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge Information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students' learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software Engineering and learning English.
Attention-Enhanced Graph Neural Networks With Global Context for Session-Based Recommendation

Session-based recommendation is a crucial task aiming to predict users' interested items based only on anonymous user behaviors.Most recent solutions for session-based recommendation comprehensively consider the interactive information of all sessions but bring the problem of imbalanced positive and negative samples on model training.In this paper, we propose a novel approach, named Attentionenhanced Graph Neural Networks with Global Context for Session-based Recommendation (AGNN-GC), to learn and merge item transitions of all sessions in a cleverer way to enhance the recommendation effects.AGNN-GC first constructs global and local graphs based on all training sequences.Next, it uses graph convolutional networks with a session-aware attention mechanism to learn global-level item embedding in all sessions.Then it employs a graph attention networks module to learn local-level item embedding in the current sessions.After that, it fuses the learned two-level item embedding to enhance the feature presentations of items in the current session by a novel attention mechanism.Finally, applying the focal loss to balance positive and negative samples on model training accomplishes the prediction.Our experiments on three real-world datasets consistently show the superior performance of AGNN-GC over state-of-the-art methods.
Out for In!

Service robots have increasingly been investigated in retailing. Previous studies mainly focused on the effectiveness of recommendation with regard to a single robot, and whether and how the use of two robots combined can achieve better performance remain unclear. In this study, we address this by exploring the combination power of two service robots for product recommendation in a bakery. We placed one robot inside the store for product recommendation and the other robot outside to promote the inside robot. Particularly, we are interested in the effects of the outside robot on the inside robot's performance in product recommendation. Our results indicate that using the outside robot to promote the inside robot achieved more purchases over using the inside robot alone. Particularly, we discovered that the outside robot increased the attention of customers toward the inside robot; hence, more customers checked and purchased the products. Based on the findings, we discuss the important points for the effective use of service robots.
A novel use of an artificially intelligent Chatbot and a live, synchronous virtual question-and answer session for fellowship recruitment

Academic departments universally communicate information about their programs using static websites. In addition to websites, some programs have even ventured out into social media (SM). These bidirectional forms of SM interaction show great promise; even hosting a live Question and Answer (Q&A) session has the potential for program branding. Artificial Intelligence (AI) usage in the form of a chatbot has expanded on websites and in SM. The potential use of chatbots, for the purposes of trainee recruitment, is novel and underutilized. With this pilot study, we aimed to answer the question; can the use of an Artificially Intelligent Chatbot and a Virtual Question-and-Answer Session aid in recruitment in a Post-COVID-19 era?We held three structured Question-and-Answer Sessions over a period of 2 weeks. This preliminary study was performed after completion of the three Q&A sessions, in March-May, 2021. All 258 applicants to the pain fellowship program were invited via email to participate in the survey after attending one of the Q&A sessions. A 16-item survey assessing participants' perception of the chatbot was administered.Forty-eight pain fellowship applicants completed the survey, for an average response rate of 18.6%. In all, 35 (73%) of survey respondents had used the website chatbot, and 84% indicated that it had found them the information they were seeking.We employed an artificially intelligent chatbot on the department website to engage in a bidirectional exchange with users to adapt to changes brought on by the pandemic. SM engagement via chatbot and Q&A sessions can leave a favorable impression and improve the perception of a program.
Parallel multi-head attention and term-weighted question embedding for medical visual question answering

The goal of medical visual question answering (Med-VQA) is to correctly answer a clinical question posed by a medical image. Medical images are fundamentally different from images in the general domain. As a result, using general domain Visual Question Answering (VQA) models to the medical domain is impossible. Furthermore, the large-scale data required by VQA models is rarely available in the medical arena. Existing approaches of medical visual question answering often rely on transfer learning with external data to generate good image feature representation and use cross-modal fusion of visual and language features to acclimate to the lack of labelled data. This research provides a new parallel multi-head attention framework (MaMVQA) for dealing with Med-VQA without the use of external data. The proposed framework addresses image feature extraction using the unsupervised Denoising Auto-Encoder (DAE) and language feature extraction using term-weighted question embedding. In addition, we present qf-MI, a unique supervised term-weighting (STW) scheme based on the concept of mutual information (MI) between the word and the corresponding class label. Extensive experimental findings on the VQA-RAD public medical VQA benchmark show that the proposed methodology outperforms previous state-of-the-art methods in terms of accuracy while requiring no external data to train the model. Remarkably, the presented MaMVQA model achieved significantly increased accuracy in predicting answers to both close-ended (78.68%) and open-ended (55.31%) questions. Also, an extensive set of ablations are studied to demonstrate the significance of individual components of the system.
BANS: Evaluation of Bystander Awareness Notification Systems for Productivity in VR

VR Head-Mounted Displays (HMDs) provide unlimited and personalized virtual workspaces and will enable working anytime and anywhere.However, if HMDs are to become ubiquitous, VR users are at risk of being observed, which can threaten their privacy.We examine six Bystander Awareness Notification Systems (BANS) to enhance VR users' bystander awareness whilst immersed in VR.In a user study (N=28), we explore how future HMDs equipped with BANS might enable users to maintain their privacy while contributing towards enjoyable and productive travels.Results indicate that BANS increase VR users' bystander awareness without affecting presence and productivity.Users prefer BANS that extract and present the most details of reality to facilitate their bystander awareness.We conclude by synthesizing four recommendations, such as providing VR users with control over BANS and considering how VR users can best transition between realities, to inform the design of privacy-preserving HMDs.
Artificial intelligence for dementia research methods optimization

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.
Editorial for the Special Issue on Advances in Optoelectronic Devices

Optoelectronic devices are fabricated based on an optoelectronic conversion effect, which is a developing research field of modern optoelectronic technology and microelectronics technology [...].
An NLP-assisted Bayesian time-series analysis for prevalence of Twitter cyberbullying during the COVID-19 pandemic

COVID-19 has brought about many changes in social dynamics. Stay-at-home orders and disruptions in school teaching can influence bullying behavior in-person and online, both of which leading to negative outcomes in victims. To study cyberbullying specifically, 1 million tweets containing keywords associated with abuse were collected from the beginning of 2019 to the end of 2021 with the Twitter API search endpoint. A natural language processing model pre-trained on a Twitter corpus generated probabilities for the tweets being offensive and hateful. To overcome limitations of sampling, data were also collected using the count endpoint. The fraction of tweets from a given daily sample marked as abusive is multiplied to the number reported by the count endpoint. Once these adjusted counts are assembled, a Bayesian autoregressive Poisson model allows one to study the mean trend and lag functions of the data and how they vary over time. The results reveal strong weekly and yearly seasonality in hateful speech but with slight differences across years that may be attributed to COVID-19.
Follow the Money: Analyzing Darknet Activity Using Cryptocurrency and the Bitcoin Blockchain

Transactions on the darknet are notoriously difficult to examine. Prior criminological research has generally used web scraping and qualitative text analysis to examine illegal darknet markets. One disadvantage of this process is that individuals can lie. Fortunately for researchers, the currency used for transactions on the darknet, cryptocurrency, is designed to be tracked. In this article, we examine transactions from a former darknet marketplace, AlphaBay. Using the blockchain, we examine the interconnectedness of both legal and illegal cryptocurrencies. In addition, we provide a structured approach to quantitatively examine the Bitcoin blockchain ledger, offering both the tools and our own experiences for other researchers interested in such approaches. While cybersecurity, information technology, accounting, and other disciplines can examine the financial data itself, we believe that criminologists can provide additional benefits in pattern analysis and organizing the context and theory around the transactions. Our results show that cryptocurrency transactions are generally identifiable (90%) and involve likely illegal transactions, transactions that attempt to obfuscate other transactions, and legal transactions. We end with a discussion of newer cryptocurrencies and related technology and how they will likely shape future work.
An Optimized Approach For Detection and Classification of Spam Email’s Using Ensemble Methods

Abstract Since the advent of email services, spam emails are a major concern because users’ security depends on the classification of emails as ham or spam. It’s a malware attack that has been used for spear phishing, whaling, clone phishing, website forgery, and other harmful activities. However, various ensemble Machine Learning (ML) algorithms used for the detection and filtering of spam emails have been less explored. In this research, we offer a ML based optimized algorithm for detecting spam emails that have been enhanced using Hyper-parameter tuning approaches. The proposed approach uses two feature extraction modules, namely Count-Vectorizer and TFIDF-Vectorizer that provide the most effective classification results when we applied them to three different publicly available email data sets: Ling Spam, UCI SMS Spam, and Proposed dataset. Moreover, to extend the performance of classifiers we used various ML methods such as Naive Bayes (NB), Logistic Regression (LR), Extra Tree, Stochastic Gradient Descent (SGD), XG-Boost, Support Vector Machine (SVM), Random Forest (RF), Multi Layer Perception (MLP), and parameter optimization approaches such as Manual search, Random search, Grid search, and Genetic algorithm. For all three data sets, the SGD outperformed other algorithms. All of the other ensembles (Extra Tree, RF), linear models (LR, Linear-SVC), and MLP performed admirably, with relatively high precision, recall, accuracies and F1-Score.
Fringe Projection Profilometry Based on Saturated Fringe Restoration in High Dynamic Range Scenes

In high dynamic scenes, fringe projection profilometry (FPP) may encounter fringe saturation, and the phase calculated will also be affected to produce errors. This paper proposes a saturated fringe restoration method to solve this problem, taking the four-step phase shift as an example. Firstly, according to the saturation of the fringe group, the concepts of reliable area, shallow saturated area, and deep saturated area are proposed. Then, the parameter A related to the reflectivity of the object in the reliable area is calculated to interpolate A in the shallow and deep saturated areas. The theoretically shallow and deep saturated areas are not known in actual experiments. However, morphological operations can be used to dilate and erode reliable areas to produce cubic spline interpolation (CSI) areas and biharmonic spline interpolation (BSI) areas, which roughly correspond to shallow and deep saturated areas. After A is restored, it can be used as a known quantity to restore the saturated fringe using the unsaturated fringe in the same position, the remaining unrecoverable part of the fringe can be completed using CSI, and then the same part of the symmetrical fringe can be further restored. To further reduce the influence of nonlinear error, the Hilbert transform is also used in the phase calculation process of the actual experiment. The simulation and experimental results validate that the proposed method can still obtain correct results without adding additional equipment or increasing projection number, which proves the feasibility and robustness of the method.
A Multi-Scale Channel Attention Network for Prostate Segmentation

Prostate cancer is one of the most common malignant tumors in men. Magnetic resonance imaging (MRI) has evolved to an important tool for the diagnosis of prostate cancer. Targeted biopsy is required for accurate diagnosis. This often requires MRI-ultrasound (MRI-US) fusion, as the biopsy is usually performed using transrectal ultrasound. Accurate prostate segmentation on MRI is essential for MRI-US fusion biopsy. However, the variation in prostate shape, appearance, and size makes the automatic segmentation challenging, given the limit of the annotated data. In this brief, we propose a method using multi-scale and Channel-wise Self-Attention (CSA) to re-calibrate the feature maps from multiple layers. By embedding the multi-scale CSA on the skip-connection in a UNet structure, called as UCAnet, we show the consistent improvement of the prostate segmentation in Dice, IoU and ASSD. For comparison, we also investigate the single-scale CSA in the networks, and incorporate the vision transformer to test if a transformer would boost the performance. Experiments on a public dataset with 204 prostate MRI scans show that UCAnet achieves the best performance and outperforms the state-of-the-art methods for prostate segmentation such as ENet, UNet, USE-Net and TransUNet.
ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

This paper presents prompt design techniques for software engineering, in the form of patterns, to solve common problems when using large language models (LLMs), such as ChatGPT to automate common software engineering activities, such as ensuring code is decoupled from third-party libraries and simulating a web application API before it is implemented. This paper provides two contributions to research on using LLMs for software engineering. First, it provides a catalog of patterns for software engineering that classifies patterns according to the types of problems they solve. Second, it explores several prompt patterns that have been applied to improve requirements elicitation, rapid prototyping, code quality, refactoring, and system design.
Text-to-image Diffusion Models in Generative AI: A Survey

This survey reviews text-to-image diffusion models in the context that diffusion models have emerged to be popular for a wide range of generative tasks. As a self-contained work, this survey starts with a brief introduction of how a basic diffusion model works for image synthesis, followed by how condition or guidance improves learning. Based on that, we present a review of state-of-the-art methods on text-conditioned image synthesis, i.e., text-to-image. We further summarize applications beyond text-to-image generation: text-guided creative generation and text-guided image editing. Beyond the progress made so far, we discuss existing challenges and promising future directions.