Reviewerly/exHarmony huggingface dataset:
Design of Weapon Detection System

Every year, a great number of people across the world are working to end gun violence. As a result, the "Design of Weapon Detection Using AI" was proposed. Baggage checking with X-ray screening could be a high-need task that assists in bringing down the opportunity for wrongdoing and fear-based criminal acts. Manual discovery of dangerous things is tedious in light of the fact that a little level of sacks incorporates dangers, and the procedure requires an elevated degree of concentration. This article proposes a method for detecting threats using a single X-ray picture; a hazard object may be detected automatically. The technology is based on implicit form models, and it was originally developed for recognizing things in pictures. The detection approach employs a visual lexicon and event structure derived from a preparation dataset including agent X-beam pictures of the perilous object to be identified. Single grayscale X-ray images obtained using a single piece of energy gathering equipment can be processed using our technology. Object location and distinguishing proof is a significant piece of the PC vision work process. This examination paper offers a component-based strategy that uses "Speeded up Robust Features" to distinguish an article in a jumbled scene and a bag-of-words model to recognize the item progressively.
Small Object Detection Algorithm for Railway Scene

Small object detection plays an important role in computer vision. However, in the existing methods, the detections of small objects are prone to error detection and missing detection, so the results of the methods are not ideal. When the train is running, it is necessary to detect the obstacle object at a distance. But the distant obstacle objects in the image are small and occupy fewer pixels, leading to the lack of sufficient information to achieve accurate and effective detection. Therefore, we propose a YOLOv5-based small object detection algorithm for railway scene, which adds a detection scale with quarter down-sampling. At this scale, the feature map size is larger and more favorable for small object detection. In data preprocessing, we use copy-paste small objects to increase the number of small objects, while the original images are retained during the pasting process to expand the dataset. In the experiment, our method is compared with the YOLOv5s algorithm in the railway scene dataset, and the mAP is improved by 8.4%, which verifies the effectiveness of our method.
Frax: A Fractional-Algorithmic Stablecoin Protocol

A 2-token, fully autonomous blockchain protocol which transitions a fully collateralized stablecoin (FRAX) to fully algorithmic, moving through a fractional-collateral phase is proposed and implemented. FRAX is backed 100% by collateral at genesis. As usage of FRAX as a medium of exchange grows, so does its monetary premium and demand. This allows the backing to be incrementally lowered as long as the price target of $1/FRAX holds. A second token in the system, Frax Shares (FXS), captures the seigniorage value when minting non-collateralized value. This creates a protocol where FRAX is mintable with decreasing ratios of collateralization and FXS tokens capture the non-collateralized value as the stablecoin slowly transitions to a predominantly algorithmic model from its original collateralized state.
Sequential Pattern Mining Approach for Personalized Fraudulent Transaction Detection in Online Banking

Financial institutions face challenges of fraud due to an increased number of online transactions and sophisticated fraud techniques. Although fraud detection systems have been implemented to detect fraudulent transactions in online banking, many systems just use conventional rule-based approaches. Rule-based detection systems have a difficulty in updating and managing their rules and conditions manually. Additionally, generated from the few fraud cases, the rules are general rather than specific to each user. In this paper, we propose a personalized alarm model to detect frauds in online banking transactions using sequence pattern mining on each user’s normal transaction log. We assumed that a personalized fraud detection model is more effective in responding to the rapid increase in online banking users and diversified fraud patterns. Moreover, we focused on the fact that fraudulent transactions are very different from each user’s usual transactions. Our proposed model divides each user’s log into transactions, extracts a set of sequence patterns, and uses it to determine whether a new incoming transaction is fraudulent. The incoming transaction is divided into multiple windows, and if the normal patterns are not found in the consecutive windows, an alarm is sounded. We applied the model to a real-world dataset and showed that our model outperforms the rule-based model and the Markov chain model. Although more experiments on additional datasets are needed, our personalized alarm model can be applied to real-world systems.
Explainable Fuzzy AI Challenge 2022: Winner’s Approach to a Computationally Efficient and Explainable Solution

An explainable artificial intelligence (XAI) agent is an autonomous agent that uses a fundamental XAI model at its core to perceive its environment and suggests actions to be performed. One of the significant challenges for these XAI agents is performing their operation efficiently, which is governed by the underlying inference and optimization system. Along similar lines, an Explainable Fuzzy AI Challenge (XFC 2022) competition was launched, whose principal objective was to develop a fully autonomous and optimized XAI algorithm that could play the Python arcade game “Asteroid Smasher”. This research first investigates inference models to implement an efficient (XAI) agent using rule-based fuzzy systems. We also discuss the proposed approach (which won the competition) to attain efficiency in the XAI algorithm. We have explored the potential of the widely used Mamdani- and TSK-based fuzzy inference systems and investigated which model might have a more optimized implementation. Even though the TSK-based model outperforms Mamdani in several applications, no empirical evidence suggests this will also be applicable in implementing an XAI agent. The experimentations are then performed to find a better-performing inference system in a fast-paced environment. The thorough analysis recommends more robust and efficient TSK-based XAI agents than Mamdani-based fuzzy inference systems.
Gaining Impact with Mixed Reality in Industry – A Sustainable Approach

Mixed reality (augmented and virtual reality) technology continues to find its way into the industry. Nevertheless, prototypes and island solutions are developed in companies, which are not embedded in the corporate strategy. This paper presents a systematic approach for finding and developing use cases accompanied by a strategic implementation and evaluation process to integrate mixed reality in the industry. Sustainability aspects like energy and resource efficiency, possible reduction of the ecological footprint, and sustainable solutions for lasting and vision-based use in companies are considered. Within several use cases with 20 industry partners in the following areas are developed: 1) novel forms of space-independent collaboration (e.g., collaborative work by integrating real-time 3D depth information of the real environment and visualization of and interaction with real-time production data) and 2) XR-supported training and learning methods (e.g., parameterizable and adaptive training scenarios, roll-out of training content for several participants and integration of gamification mechanisms). Additionally, the methodology for a sustainability assessment, technology acceptance, and a multi-criteria evaluation are shown, and first results are discussed.
Toward Cooperative Level Generation in Multiplayer Games: A User Study in Overcooked!

Multiplayer contents play an essential role in extending the lifespan of the game and positively affect the player’s experience. However, previous studies on procedural content generation focused on single-player games and few for multiplayer games. Multiplayer games have different ethics and mechanics associated with competition or cooperation. Thus, content designers are concerned about this interaction. In this paper, we propose a new method for generating multiplayer levels that encourage diverse cooperation among game players. Our contributions are summarized as follows: 1) We designed four cooperation patterns usable in controllable generation literature. 2) We proposed a controllable level generator to deploy the proposed patterns in the two-player cooperative cooking game, Overcooked!. 3) We discussed effective methods for leading players’ cooperation experience. Consequently, we found that the players’ interaction is most fundamental at the multiplayer level. The results of our study will lead to diverse cooperation experiences in future multiplayer game content generation studies.
Effects of extended reality on language learning: A meta-analysis

In recent years, there has been increasing use of extended reality (XR) in language learning. Many scholars have conducted empirical research on the relationship between the two, but conclusions have been inconsistent, which calls for an organization and reanalysis of relevant literature. Articles published between 2000 and 2022 on the impact of XR on language learning were retrieved from the Web of Science and Scopus databases, and 17 of them (including 21 independent samples and 993 subjects) were included in this meta-analysis. The findings indicate that XR could promote language learning (effect size = 0.825). The moderating effects of education level, target language, and technology type were also tested, and the results indicate that the target language type significantly moderated the effect of XR technology on language learning (Q = 30.563, p < 0.001). Moreover, based on the subgroup analysis, several research questions worthy of further exploration in this field are discussed. Some suggestions are provided, noting that these technologies should be personally designed for learners and learning objects when applied in order to improve the effects of language learning.
Long Short-Term Memory Based Photoplethysmography Biometric Authentication

Spoofing attacks remain one of the most inherent problems with traditional biometrics. Therefore, the investigation into other solutions for subject recognition is warranted. Physiological signals are recently employed as new biometrics traits that are not readily visible, like Electroencephalography (EEG), Electrocardiography (ECG), and Photoplethysmography (PPG). In particular, we are interested in PPG since its simple acquisition and its liveness clue. In this study, we have proposed an effective ppg-based biometric model compromising between minimizing the complexity of the model as regards trainable parameters while keeping high performance by using a long short-term memory (LSTM) network for the classification of ppg waveforms by modeling time series sequences. The proposed model relies on three sequential - LSTM layers to capture the sequential feature of ppg recordings. Our proposed model outperforms previous state-of-the-art studies.
AI, Blockchain, and IOT

Artificial intelligence and blockchain technologies are widely used in the Internet of Things (IoT). IoT to collect and present data, blockchain to provide the infrastructure to define operational rules, and AI optimization processes and rules are all possible connections between these technologies. These technologies converge, enabling new autonomous business models and the innovative impacts of this triad on increasing the competitiveness of companies. This chapter delves into the basic understanding of each of the triads and how they complement each other’s drawbacks in possibly revolutionizing the entire financial domain.
Sexually Transmitted Disease–Related Reddit Posts During the COVID-19 Pandemic: Latent Dirichlet Allocation Analysis

Sexually transmitted diseases (STDs) are common and costly, impacting approximately 1 in 5 people annually. Reddit, the sixth most used internet site in the world, is a user-generated social media discussion platform that may be useful in monitoring discussion about STD symptoms and exposure.This study sought to define and identify patterns and insights into STD-related discussions on Reddit over the course of the COVID-19 pandemic.We extracted posts from Reddit from March 2019 through July 2021. We used a topic modeling method, Latent Dirichlet Allocation, to identify the most common topics discussed in the Reddit posts. We then used word clouds, qualitative topic labeling, and spline regression to characterize the content and distribution of the topics observed.Our extraction resulted in 24,311 total posts. Latent Dirichlet Allocation topic modeling showed that with 8 topics for each time period, we achieved high coherence values (pre-COVID-19=0.41, prevaccination=0.42, and postvaccination=0.44). Although most topic categories remained the same over time, the relative proportion of topics changed and new topics emerged. Spline regression revealed that some key terms had variability in the percentage of posts that coincided with pre-COVID-19 and post-COVID-19 periods, whereas others were uniform across the study periods.Our study's use of Reddit is a novel way to gain insights into STD symptoms experienced, potential exposures, testing decisions, common questions, and behavior patterns (eg, during lockdown periods). For example, reduction in STD screening may result in observed negative health outcomes due to missed cases, which also impacts onward transmission. As Reddit use is anonymous, users may discuss sensitive topics with greater detail and more freely than in clinical encounters. Data from anonymous Reddit posts may be leveraged to enhance the understanding of the distribution of disease and need for targeted outreach or screening programs. This study provides evidence in favor of establishing Reddit as having feasibility and utility to enhance the understanding of sexual behaviors, STD experiences, and needed health engagement with the public.
Co-located Immersive Gaming: A Comparison between Augmented and Virtual Reality

Despite the surge in commercially available multiplayer mixed reality (XR) devices and applications, few studies have focused on the player experience during co-located, multiplayer gameplay in XR environments. To address this gap, we designed and developed Escape from Kyle-Earth, a co-located, multiplayer XR game that can be played in both AR and VR using a head-mounted display. We then conducted a user study with 26 participants, in which each participant played both versions of the game. Our results indicate that VR evoked a stronger sense of presence while its AR counterpart increased co-presence between players. However, there was no significant difference in game enjoyment between the two platforms. Our work contributes to the burgeoning literature on co-located immersive gaming.
DouZero+: Improving DouDizhu AI by Opponent Modeling and Coach-guided Learning

Recent years have witnessed the great breakthrough of deep reinforcement learning (DRL) in various perfect and imperfect information games. Among these games, DouDizhu, a popular card game in China, is very challenging due to the imperfect information, large state and action space as well as elements of collaboration. Recently, a DouDizhu AI system called DouZero has been proposed. Trained using traditional Monte Carlo method with deep neural networks and self-play procedure without the abstraction of human prior knowledge, DouZero has achieved the best performance among all the existing DouDizhu AI programs. In this work, we propose to enhance DouZero by introducing opponent modeling into DouZero. Besides, we propose a novel coach network to further boost the performance of DouZero and accelerate its training process. With the integration of the above two techniques into DouZero, our DouDizhu AI system achieves better performance and ranks top in the Botzone leaderboard among more than 400 AI agents, including DouZero.
Cross stage partial connections based weighted Bi-directional feature pyramid and enhanced spatial transformation network for robust object detection

Structural information is an essential component for efficient object detection. In many visual detection tasks, the objects with large structural deformation usually make up a large proportion. The shape, contour, and internal structure of the objects tend toward dramatic change, which easily causes troubles for efficient object detection. Therefore, how to detect these objects robustly and accurately is one of the significant challenges. To address this issue, we introduce a Cross Stage Partial connections-based weighted Bi-directional Feature Pyramid Network (CSP-BiFPN), which allows easy and efficient multi-scale feature fusion by cross-stage partial connections. Second, to enhance the model's spatial transformation capacity, the multi-scale feature maps extracted from the YOLO backbone network are processed by an enhanced spatial transformation network (ESTN) for spatial deformations. Based on these architectural modifications and optimizations, we further develop a novel real-time robust object detection model called Bi-STN-YOLO. We evaluate the performance of the proposed method on four image datasets. The experimental results demonstrate that the proposed approach achieves significant improvements compared with the typical YOLO families and competitive performance compared to the state-of-the-arts in detection tasks.
EA-Net: Edge-aware network for brain structure segmentation via decoupled high and low frequency features

Automatic brain structure segmentation in Magnetic Resonance Image (MRI) plays an important role in the diagnosis of various neuropsychiatric diseases. However, most existing methods yield unsatisfactory results due to blurred boundaries and complex structures. Improving the segmentation ability requires the model to be explicit about the spatial localization and shape appearance of targets, which correspond to the low-frequency content features and the high-frequency edge features, respectively. Therefore, in this paper, to extract rich edge and content feature representations, we focus on the composition of the feature and utilize a frequency decoupling (FD) block to separate the low-frequency and high-frequency parts of the feature. Further, a novel edge-aware network (EA-Net) is proposed for jointly learning to segment brain structures and detect object edges. First, an encoder-decoder sub-network is utilized to obtain multi-level information from the input MRI, which is then sent to the FD block to complete the frequency separation. Further, we use different mechanisms to optimize both the low-frequency and high-frequency features. Finally, these two parts are fused to generate the final prediction. In particular, we extract the content mask and the edge mask from the optimization feature with different supervisions, which forces the network to learn the boundary features of the object. Extensive experiments are performed on two public brain MRI T1 scan datasets (the IBSR dataset and the MALC dataset) to evaluate the effectiveness of the proposed algorithm. The experiments show that the EA-Net achieves the best performance compared with the state-of-the-art methods, and improves the segmentation DSC score by 1.31% at most compared with the U-Net model and its variants. Moreover, we evaluate the EA-Net under different noise disturbances, and the results demonstrate the robustness and superiority of our method under low-quality noisy MRI. Code is available at https://github.com/huqian999/EA-Net.
Solving Diversity-Aware Maximum Inner Product Search Efficiently and Effectively

Maximum inner product search (or k-MIPS) is a fundamental operation in recommender systems that infer preferable items for users. To support large-scale recommender systems, existing studies designed scalable k-MIPS algorithms. However, these studies do not consider diversity, although recommending diverse items is important to improve user satisfaction. We therefore formulate a new problem, namely diversity-aware k-MIPS. In this problem, users can control the degree of diversity in their recommendation lists through a parameter. However, exactly solving this problem is unfortunately NP-hard, so it is challenging to devise an efficient, effective, and practical algorithm for the diversity-aware k-MIPS problem. This paper overcomes this challenge and proposes IP-Greedy, which incorporates new early termination and skipping techniques into a greedy algorithm. We conduct extensive experiments on real datasets, and the results demonstrate the efficiency and effectiveness of our algorithm. Also, we conduct a case study of the diversity-aware k-MIPS problem on a real dataset. We confirm that this problem can make recommendation lists diverse while preserving high inner products of user and item vectors in the lists.
Tutorial on Offline Evaluation for Group Recommender Systems

Group Recommender Systems (GRSs), unlike recommendations for individuals, provide suggestions for groups of people. Clearly, many activities are often experienced by a group rather than an individual (visiting a restaurant, traveling, watching a movie, etc.) hence the requirement for such systems. The topic is gradually receiving more and more attention, with an increased number of papers published at significant venues, which is enabled by the predominance of online social platforms that allow their users to interact in groups, as well as to plan group activities. However, the research area lacks certain ground rules, such as basic evaluation agreements. We believe this is one of the main obstacles to make advances in the research area, and to enable researchers to compare and continue each others' works. In other words, setting the basic evaluation agreements is a stepping-stone towards reproducible Group Recommenders research. The goal of this tutorial is to tackle this problem, by providing the basic principles of the GRSs offline evaluation approaches.
Multi-Modal Dialog State Tracking for Interactive Fashion Recommendation

Multi-modal interactive recommendation is a type of task that allows users to receive visual recommendations and express natural-language feedback about the recommended items across multiple iterations of interactions. However, such multi-modal dialog sequences (i.e. turns consisting of the system's visual recommendations and the user's natural-language feedback) make it challenging to correctly incorporate the users' preferences across multiple turns. Indeed, the existing formulations of interactive recommender systems suffer from their inability to capture the multi-modal sequential dependencies of textual feedback and visual recommendations because of their use of recurrent neural network-based (i.e., RNN-based) or transformer-based models. To alleviate the multi-modal sequential dependency issue, we propose a novel multi-modal recurrent attention network (MMRAN) model to effectively incorporate the users' preferences over the long visual dialog sequences of the users' natural-language feedback and the system's visual recommendations. Specifically, we leverage a gated recurrent network (GRN) with a feedback gate to separately process the textual and visual representations of natural-language feedback and visual recommendations into hidden states (i.e. representations of the past interactions) for multi-modal sequence combination. In addition, we apply a multi-head attention network (MAN) to refine the hidden states generated by the GRN and to further enhance the model's ability in dynamic state tracking. Following previous work, we conduct extensive experiments on the Fashion IQ Dresses, Shirts, and Tops & Tees datasets to assess the effectiveness of our proposed model by using a vision-language transformer-based user simulator as a surrogate for real human users. Our results show that our proposed MMRAN model can significantly outperform several existing state-of-the-art baseline models.