Reviewerly/exHarmony huggingface dataset:
Consumer Fraud against Older Adults in Digital Society: Examining Victimization and Its Impact

The European population is aging, which means more people aged sixty-five and over are at risk of financial exploitation. However, there is a lack of consensus regarding whether older persons are at greater risk of fraud than younger counterparts due to physical, economic, and social factors or, rather, whether they are slightly protected from fraud in the digital era due to less frequent online activity. Moreover, little is known about the financial, emotional, psychological, and physical impacts of fraud experiences amongst older generations in digital society. We employ multilevel modelling on a sample of EU citizens (n = 26,735) to analyze these issues. The results show that, holding other factors constant, older adults are more likely to suffer fraud in general, but not fraud via online channels. Identity theft in which the offender attempts to trick the victim by impersonating a reputable organization is found to be particularly relevant for citizens aged sixty-five and above. Older persons are less likely to suffer a financial impact but more likely to experience anger, irritation, embarrassment, and negative impacts on their physical health from fraud in general as well as from online fraud. Many organizations aim to help protect older adults from financial crime and its impacts; thus, the results emphasize the need to understand particular fraud categories suffered by older generations and to design support programs that fully take into account the non-financial impacts of this crime.
Presence and Effectiveness of Online Learning Using a Metaverse Platform: Gather.town

Online learning is rapidly developing, and various strategies to effectively operate online learning are being discussed. In particular, online learning using the metaverse is expected to facilitate social communication and high presence. This study examined the presence and effectiveness of online learning using a metaverse platform. A class was conducted for six weeks using the metaverse platform Gather.town. A questionnaire was employed to verify the online presence of the learners and the effectiveness of the online learning, data were collected from students who participated in the class. Analysis of the online presence of the learners and the effectiveness of the online learning before and after the class revealed the following. The online presence of the learners who used the metaverse platform improved compared to before the class. The results, including those reflecting all sub-variables (teaching, social, cognitive), were statistically significant. Similarly, online effectiveness and its sub-variables (confidence in prerequisite skills, general beliefs about online learning, self-direction and initiative, and the desire for interaction with others) were statistically significant.
Protecting the Quantum Interference of Cat States by Phase-Space Compression

Compressing the spectral content of quantum interference features in Schr\"odinger cat states to lower frequencies protects them against photon loss and preserves the most valuable characteristics that enable many quantum technologies.
Instruction Tuning with GPT-4

Prior work has shown that finetuning large language models (LLMs) using machine-generated instruction-following data enables such models to achieve remarkable zero-shot capabilities on new tasks, and no human-written instructions are needed. In this paper, we present the first attempt to use GPT-4 to generate instruction-following data for LLM finetuning. Our early experiments on instruction-tuned LLaMA models show that the 52K English and Chinese instruction-following data generated by GPT-4 leads to superior zero-shot performance on new tasks to the instruction-following data generated by previous state-of-the-art models. We also collect feedback and comparison data from GPT-4 to enable a comprehensive evaluation and reward model training. We make our data generated using GPT-4 as well as our codebase publicly available.
Service-Oriented Computing for Effective Management of Academic Records: In Case of Debre Markos University Burie Campus

It is difficult for higher education institutes to manage the growing volumes of educational data accurately and efficiently using conventional ways.Modern technology is required to deal with this difficulty and ICT is an emerging technology that can solve this problem.In this study, Service-Oriented Computing (SOC) is used to develop a Service-Oriented Registrar System (SORS) for Debre Markos University Burie campus registrar office.Web services are used to develop highly cohesive and loosely coupled subsystems that support location transparency and manage the academic records of students accurately and efficiently.Moreover, the developed system is secured, easily maintainable, expandable and open for either inter or intra-application integration on the campus.
Research on SLAM and Path Planning Method of Inspection Robot in Complex Scenarios

Factory safety inspections are crucial for ensuring safe production. However, manual inspections present issues such as low efficiency and high workload. Inspection robots provide an efficient and reliable solution for completing patrol tasks. The development of robot localization and path planning technologies provides guarantees for factory inspection robots to autonomously complete inspection tasks in complex environments. This paper studies mapping and localization, as well as path planning methods for robots in order to meet the application requirements of factory inspections. Two SLAM application systems based on multiple-line laser radar and vision are designed for different scenarios in consideration of the limitations of cameras and laser sensors in terms of their own characteristics and applicability in different environments. To address the issue of low efficiency in inspection tasks, a hybrid path planning algorithm that integrates the A-star algorithm and time elasticity band algorithm is proposed. This algorithm effectively solves the problem of path planning in complex environments that is prone to falling into local optimal solutions, thereby improving the inspection efficiency of robots. Experimental tests show that the designed SLAM and path planning methods can meet the needs of robot inspection in complex scenes and have good reliability and stability. The code used in this article is open source and can be accessed at https://github.com/Mxiii99/RSPP_CS.git.
Bilaterally Normalized Scale-Consistent Sinkhorn Distance for Few-Shot Image Classification

Few-shot image classification aims at exploring transferable features from base classes to recognize images of the unseen novel classes with only a few labeled images. Existing methods usually compare the support features and query features, which are implemented by either matching the global feature vectors or matching the local feature maps at the same position. However, few labeled images fail to capture all the diverse context and intraclass variations, leading to mismatch issues for existing methods. On one hand, due to the misaligned position and cluttered background, existing methods suffer from the object mismatch issue. On the other hand, due to the scale inconsistency between images, existing methods suffer from the scale mismatch issue. In this article, we propose the bilaterally normalized scale-consistent Sinkhorn distance (BSSD) to solve these issues. First, instead of same-position matching, we use the Sinkhorn distance to find an optimal matching between images, mitigating the object mismatch caused by misaligned position. Meanwhile, we propose the intraimage and interimage attentions as the bilateral normalization on the Sinkhorn distance to suppress the object mismatch caused by background clutter. Second, local feature maps are enhanced with the multiscale pooling strategy, making the Sinkhorn distance possible to find a consistent matching scale between images. Experimental results show the effectiveness of the proposed approach, and we achieve the state-of-the-art on three few-shot benchmarks.
Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients’ data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient’s position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff.
Illegal drug sales in the mirror of the dark web marketplace

Abstract Background Studies of the epidemiology of substance use are problematic by the disorder's nature and the illegal character of the substances' turnover. For safety, the illegal substances' markets had moved to the Internet, to the less transparent darknet part. The objective of this study was to get estimates for the Russian drug market using data collected at dark net market ‘Hydra', get the amounts of drugs offered, the relative frequency of offers, and the regional distribution of the market activities. We analyze the relationship between estimates of the illegal drug market with traditional indicators of drug use disorders on the next stage. Methods We use two types of data: 1) Deep Web Black Market data; 2) Official Russian statistical indicators related to drug use. We use Python program via VPN service to extract data. R software was employed for statistical analysis to build the maps of drug offerings and sales by the regions of Russia. Er use the descriptive statistics and correlation analysis to compare the estimates with the state statistics. Results Using the estimates of sales for the drug classes in grams by region (per 100000 population), we mapped the data and found that there is a correlation with the registered drug users and drug-related deaths. There is no Hydra sales of opiates in some regions indicating that older supply channels are still active. Total drug sales and offers championed by cannabis, opiates/opioids, cocaine, amphetamines and the other drugs in smaller amounts. There is a significant correlation between the supply of opiates in regions of Russia and the official incidence of fatal drug poisoning (r = 0.41; p = 0.003), and the number of HIV positive injected drug users (r = 0.47; p = 0.002). Key messages Analysis of the deep internet sales of drugs helps assess the situation with the illegal drug supply in the regions of Russia. Data on deep internet sales of drugs should not substitute the other sources of data.
CATER: Combined Animal Tracking &amp; Environment Reconstruction

Quantifying the behavior of small animals traversing long distances in complex environments is one of the most difficult tracking scenarios for computer vision. Tiny and low-contrast foreground objects have to be localized in cluttered and dynamic scenes as well as trajectories compensated for camera motion and drift in multiple lengthy recordings. We introduce CATER, a novel methodology combining an unsupervised probabilistic detection mechanism with a globally optimized environment reconstruction pipeline enabling precision behavioral quantification in natural environments. Implemented as an easy to use and highly parallelized tool, we show its application to recover fine-scale motion trajectories, registered to a high-resolution image mosaic reconstruction, of naturally foraging desert ants from unconstrained field recordings. By bridging the gap between laboratory and field experiments, we gain previously unknown insights into ant navigation with respect to motivational states, previous experience, and current environments and provide an appearance-agnostic method applicable to study the behavior of a wide range of terrestrial species under realistic conditions.
A Review of Artificial Intelligence in Embedded Systems

Advancements in artificial intelligence algorithms and models, along with embedded device support, have resulted in the issue of high energy consumption and poor compatibility when deploying artificial intelligence models and networks on embedded devices becoming solvable. In response to these problems, this paper introduces three aspects of methods and applications for deploying artificial intelligence technologies on embedded devices, including artificial intelligence algorithms and models on resource-constrained hardware, acceleration methods for embedded devices, neural network compression, and current application models of embedded AI. This paper compares relevant literature, highlights the strengths and weaknesses, and concludes with future directions for embedded AI and a summary of the article.
MMMLP: Multi-modal Multilayer Perceptron for Sequential Recommendations

Sequential recommendation aims to offer potentially interesting products to users by capturing their historical sequence of interacted items. Although it has facilitated extensive physical scenarios, sequential recommendation for multi-modal sequences has long been neglected. Multi-modal data that depicts a user’s historical interactions exists ubiquitously, such as product pictures, textual descriptions, and interacted item sequences, providing semantic information from multiple perspectives that comprehensively describe a user’s preferences. However, existing sequential recommendation methods either fail to directly handle multi-modality or suffer from high computational complexity. To address this, we propose a novel Multi-Modal Multi-Layer Perceptron (MMMLP) for maintaining multi-modal sequences for sequential recommendation. MMMLP is a purely MLP-based architecture that consists of three modules - the Feature Mixer Layer, Fusion Mixer Layer, and Prediction Layer - and has an edge on both efficacy and efficiency. Extensive experiments show that MMMLP achieves state-of-the-art performance with linear complexity. We also conduct ablating analysis to verify the contribution of each component. Furthermore, compatible experiments are devised, and the results show that the multi-modal representation learned by our proposed model generally benefits other recommendation models, emphasizing our model’s ability to handle multi-modal information. We have made our code available online to ease reproducibility1.
Deterministic Constant-Depth Preparation of the AKLT State on a Quantum Processor Using Fusion Measurements

A class of symmetry-protected topological states relevant for measurement-based quantum computation is shown to be constructible using only constant-depth quantum circuits by leveraging symmetries, midcircuit measurements, and feed forward.
Students’ Attitude Towards E-Learning in Russia after Pandemic

The article presents the results of the second stage of a sociological survey among students of the Russian Technological University (RTU MIREA) about their attitude to distance e-learning, problems and positive experience gained during 2020-2022. The authors examined the impact of distance e-learning through the prism of the individual qualities of students and their tendency to attribute the results of their activities to external or internal factors.
Security Concerns and Solutions for Enterprise Cloud Computing Applications

Computing in the cloud is now one of the most interesting developments in technology owing to the fact that it is both flexible and cost-effective. Despite the enormous stakes, the implementation of cloud computing into an existing business model creates significant safety risks. Enterprises need to foresee and account for possible risks, threats, vulnerabilities, and mitigation strategies before using cloud computing. The concept of cloud computing may be simplified by dividing it into three separate models: "Infrastructure as a Service," "Software as a Service," and "Platform as a Service." It also talks about the current crop of cloud-based security tools. Before using this technology, we think businesses should assess their security risks, threats, and existing defences. We’ve also discussed the advantages and drawbacks of cloud computing as well as places where it might be used for information risk management.Enterprise cloud computing
Exploring the Joint Potential of Blockchain and AI for Securing Internet of Things

The emergence of the Internet of Things (IoT) has revolutionized the way we interact with the physical world.The rapid growth of IoT devices has led to a pressing need for robust security measures.Two promising approaches that can enhance IoT security are blockchain and artificial intelligence (AI).Blockchain can offer a decentralized and tamper-proof framework, ensuring the confidentiality and integrity of IoT data.AI can analyze large volumes of real-time data and detect anomalies in response to security threats in the IoT ecosystem.This paper explores the potential of these technologies and how they complement each other to provide a secured IoT system.Our main argument is that combining blockchain with AI can provide a robust solution for securing IoT networks and safeguarding the privacy of IoT users.This survey paper aims to provide a comprehensive understanding of the potential of these technologies for securing IoT networks and discuss the challenges and opportunities associated with their integration.It also provides a discussion on the current state of research on this topic and presents future research directions in this area.
Semantic similarity metrics for image registration

Image registration aims to find geometric transformations that align images. Most algorithmic and deep learning-based methods solve the registration problem by minimizing a loss function, consisting of a similarity metric comparing the aligned images, and a regularization term ensuring smoothness of the transformation. Existing similarity metrics like Euclidean Distance or Normalized Cross-Correlation focus on aligning pixel intensity values or correlations, giving difficulties with low intensity contrast, noise, and ambiguous matching. We propose a semantic similarity metric for image registration, focusing on aligning image areas based on semantic correspondence instead. Our approach learns dataset-specific features that drive the optimization of a learning-based registration model. We train both an unsupervised approach extracting features with an auto-encoder, and a semi-supervised approach using supplemental segmentation data. We validate the semantic similarity metric using both deep-learning-based and algorithmic image registration methods. Compared to existing methods across four different image modalities and applications, the method achieves consistently high registration accuracy and smooth transformation fields.
OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in Vietnamese

In recent years, visual question answering (VQA) has attracted attention from the research community because of its highly potential applications (such as virtual assistance on intelligent cars, assistant devices for blind people, or information retrieval from document images using natural language as queries) and challenge. The VQA task requires methods that have the ability to fuse the information from questions and images to produce appropriate answers. Neural visual question answering models have achieved tremendous growth on large-scale datasets which are mostly for resource-rich languages such as English. However, available datasets narrow the VQA task as the answers selection task or answer classification task. We argue that this form of VQA is far from human ability and eliminates the challenge of the answering aspect in the VQA task by just selecting answers rather than generating them. In this paper, we introduce the OpenViVQA (Open-domain Vietnamese Visual Question Answering) dataset, the first large-scale dataset for VQA with open-ended answers in Vietnamese, consists of 11,000+ images associated with 37,000+ question-answer pairs (QAs). Moreover, we proposed FST, QuMLAG, and MLPAG which fuse information from images and answers, then use these fused features to construct answers as humans iteratively. Our proposed methods achieve results that are competitive with SOTA models such as SAAA, MCAN, LORA, and M4C. The dataset is available to encourage the research community to develop more generalized algorithms including transformers for low-resource languages such as Vietnamese.
“My Precious!”: A Values-Affordances Perspective on the Adoption of Bitcoin

Given the growing pervasiveness of information systems (IS) in everyday life, recent research has acknowledged that IS technologies are often not value free but are instead infused with fundamental personal values. However, little is known about how such values explain why people assimilate these technologies and their affordances. In the intriguing case of Bitcoin, personal values—especially libertarian political values—played an essential role in clarifying the ideological underpinnings of Bitcoin and its early adoption. Consequently, we draw on research on personal values and affordance theory to develop and test a model explicating how these personal values guide individuals toward using IS applications with salient affordances that address their values. Specifically, we hypothesize and test how individuals’ personal values (i.e., libertarian political values) influence their attitudes toward Bitcoin affordances and their Bitcoin use behavior using data from a multiple administration survey of 236 users and nonusers of Bitcoin. Our results indicate that libertarian political values affect individuals’ attitudes toward Bitcoin affordances, which in turn mediate the effects of these values on actual Bitcoin use. Our findings advance the field by demonstrating the importance of integrating values into the conceptualization of IS technology affordances.
Pemodelan Machine Learning : Analisis Sentimen Masyarakat Terhadap Kebijakan PPKM Menggunakan Data Twitter

Abstract— In this pandemic era, the government is forced to implement policies that can reduce the daily positive rate of COVID-19. One of these policies is known as PPKM. It is unclear when the pandemic will end, causing data phenomena to be scattered on social media, one of which is Twitter. Therefore, in this study, we conducted an analysis of sentiment originating from tweets from Twitter social media users in the Jakarta area regarding the government's policy, namely PPKM in the face of the COVID-19 pandemic. In this research, we use a Machine Learning approach, namely LSTM. This modeling produces a classification of positive and negative sentiments. The dataset used is 3000 tweets with a time period of September - November 2021. At the preprocessing stage, the data that are ready to be used for modeling are 2176. The results of this study get an accuracy of 0.943. So the model that we propose, namely LSTM, has succeeded in classifying a satisfactory sentiment with a positive number of 92% and a negative 8% of 2176 sentiments, so it can be concluded that the PPKM policy implemented by the Indonesian government in the DKI Jakarta area is said to be effective or positive.