Reviewerly/exHarmony huggingface dataset:
Effectiveness Evaluation of Basic Bitstream Features in Surveillance Video Recompression Forensics Using VVC

Surveillance videos have been widely used in criminal investigation, judicial evidence collection, and other fields. The authenticity of surveillance video is a key prerequisite for whether the surveillance video is used legally. Video recompression forensics (VRF) is a widely recognized technique for video authenticity authentication. And basic bitstream feature (BBF) extraction is the primary step in VRF, which significantly affects the accuracy of VRF. The current VRF methods have not systematically focused on the different impact of different types of BBF on VRF. Most VRF methods directly extract the BBF from video bitstream to construct the advanced bitstream features, which implies that all BBF have an equal impact on the accuracy of VRF. This operation is obviously unreasonable. This paper focuses on VVC encoded surveillance videos, and an experimental scheme is first designed to evaluate the effectiveness of BBF. Based on the experimental data, the impact of different types of BBF on the accuracy of VRF is further analyzed and evaluated, and some important conclusions are drawn. These conclusions have important guiding significance for the subsequent development of efficient VRF methods suitable for surveillance videos using VVC.
Combining Embedding-Based and Semantic-Based Models for Post-Hoc Explanations in Recommender Systems

In today's data-rich environment, recommender systems play a crucial role in decision support systems. They provide to users personalized recommendations and explanations about these recommendations. Embedding-based models, despite their widespread use, often suffer from a lack of interpretability, which can undermine trust and user engagement. This paper presents an approach that combines embedding-based and semantic-based models to generate post-hoc explanations in recommender systems, leveraging ontology-based knowledge graphs to improve interpretability and explainability. By organizing data within a structured framework, ontologies enable the modeling of intricate relationships between entities, which is essential for generating explanations. By combining embedding-based and semantic based models for post-hoc explanations in recommender systems, the framework we defined aims at producing meaningful and easy-to-understand explanations, enhancing user trust and satisfaction, and potentially promoting the adoption of recommender systems across the e-commerce sector.
Identify and Characterize Fall-Risk in Older Adults: A Data-Driven Approach

In this study we introduce a precise fall-risk screening method for large-scale older population. Based on a dataset including 7084 older adults across 30 provinces in China, we developed a data-driven method to identify the fall-risk group and determine the major characteristics in older adults. First, the entire sample were divided into two groups by gender based on analysis of Cluster Feature Tree. Extreme Gradient Boosting models confirmed that patient clustering can improve the performance of fall-risk prediction, and pinpointed the common and different important features for different patient groups. The findings provide evidence for future behavioral trait indicators for geriatric rehabilitation and have potential to enhance geriatric health management in primary care.
A Pareto-Optimality-Based Approach for Selecting the Best Machine Learning Models in Mild Cognitive Impairment Prediction

Mild Cognitive Impairment (MCI) is a syndrome charac-terized by cognitive impairment that is greater than expected for a subject's age and level of education. Nevertheless, it does not interfere with daily activity. Prevalence in epidemiological and population-based studies ranges from 3% to 19% in adults older than 65 years. A very interesting approach in this area is related to the identification of an Artificial Intelligence (Al)-based model and a subset of relevant features to predict the MCI clinical outcome. In our study, we propose a Pareto-optimality-based approach to identify the best model for predicting MCI. In fact, the best model achieves an Accuracy and Recall on Yes MCI of 71 % and 80% respectively. With this approach, it is possible to select the best model in order to predict Yes MCI (highest risk class). Our study presents a new best model selection approach that can be applied in identifying the best model that can be applied in various disease classification problems.
Measurement-Device-Independent Semi-Quantum Digital Signature for Multi-Classical-Users

We design a measurement-device-independent semi-quantum digital signature (MDI-SQDS) protocol that enables communication between multiple classical users. Our protocol benefits from the fact that the MDI component can remain secure when the measurement device is under the control of an eavesdropper. In addition, our protocol enables the classical participants with low technical capacity to enjoy the advantages of quantum cryptography in practice. Moreover, optical delay devices play an important role in enhancing the security of communication by encrypting information. Furthermore, based on security analysis, our protocol is robust against denial and forgery attacks.
An Analysis of Solutions to the Frame Consistency Problem in Video style Transfer based on GAN

Video style transfer has emerged as a significant area within the realm of neural style transfer, holding promise for diverse applications. In this context, GANs have gained traction for their advantages in addressing temporal consistency concerns. This paper explores various strategies to manage frame consistency within GAN-based video-style conversion. It delves into techniques such as Recurrent Neural Networks (RNNs), 3D convolutions, inter-frame continuity in the discriminator, and time loss functions. The research highlights key studies that employ RNNs to complement GAN-based frameworks for improved temporal consistency. The study also evaluates a range of other approaches, such as introducing inter-frame continuity in the discriminator and applying time loss functions to minimize visual discrepancies. Through this analysis, the paper contributes insights into the evolving landscape of video style transfer techniques, guiding researchers towards effective strategies for achieving frame consistency and advancing the field.
Data interoperability technology based on chain-network convergence

With the rapid development of industrial Internet services, the era of the Internet of everything has arrived, which puts forward higher requirements for the network security of the industrial Internet. In order to guarantee the safe and trustworthy flow of industrial Internet data, this paper proposes a chain network connector architecture that integrates blockchain technology and industrial Internet technology, which utilizes the characteristics of blockchain traceability, tampering, collective maintenance, and openness and transparency to guarantee that the identification data in the industrial Internet is more secure and trustworthy. In addition, the architecture adopts quantum identity authentication technology in the identity authentication part to reduce the problem of illegal user access due to identity forgery. Meanwhile, for the problem of low efficiency of large-capacity and high-concurrency data processing generated by actual enterprises in the production process, this paper proposes to use analytical hierarchy process and Hungarian algorithm to co-optimize the chain network connector in order to make the resource allocation more reasonable and the transmission efficiency higher. The experimental results show that different algorithms are used for task allocation in different scenarios to maximize the data transmission efficiency, and also greatly improve the security of industrial Internet identification data.
Optimalisasi Penerapan Literasi Digital pada Pendidikan Sepanjang Hayat

This research aims to describe the stages of learning in the concept of lifelong education, the role of education in lifelong education, building interest in digital literacy in learning, and realizing lifelong education through digital literacy skills. This research uses a qualitative descriptive method. The data used were the results of observations and interviews taken randomly in several primary and secondary schools in the East Lombok district. Data triangulation: observation, interviews, and documentation were used in data collection and data verification. The interactive model was used to analyze the data using data collection, data compaction, data presentation, and conclusion drawing. The results of this study are that the stages of the concept of lifelong education do not only occur in formal environments but in informal and non-formal environments. The role of language education in lifelong education is that the more a person studies and learns a language well, the better the cultural principles that will be held. Building interest in digital literacy in language learning by getting used to reading in assignments using electronic equipment. Efforts to realize lifelong education through digital literacy skills by promoting learning independence among fellow community members as a parameter of sustainable social development.&#x0D; Keywords: digital literacy, interest in learning, lifelong education
A Modified Learning Method for First-order Takagi-Sugeno Fuzzy System: Gradient-based Neuro-fuzzy Algorithm Employing Smoothing Group Lasso Regularization

The first-order Takagi-Sugeno (TS1) fuzzy system harbors an assemblage of IF-THEN fuzzy rules and is capable of approximating nonlinear systems of arbitrary complexity via discerning suitable affiliation functions and rules. As a natural extension of the lasso, group lasso adeptly trims superfluous connections among neurons and disposes the variable selection issue at the group stratum. Nonetheless, a fundamental shortcomings arises when the group lasso regularizer is directly applied during network training, particularly evident as weights converge towards zero, that is numerical oscillations. In addressing this concern, a smoothing function is introduced to approximate the original group lasso regularization (OGLR). Building upon the aforementioned content, a novel gradient-based neuro-fuzzy learning algorithm with smoothing group lasso regularization (SGLR) for the TS1 fuzzy system is proposed. Ultimately, the efficacy of the proposed algorithm is substantiated through three simulations that validate its sparse performance, approximation capability, and generalization potential.
Forensic crime scene investigation in daylight conditions for the detection of time-sensitive, low-level fluorescent evidence

We present a lock-in detection machine vision NDT system that overcomes one of the biggest problems of forensic crime and fire scene investigation ‐ the detection of low-level fluorescence from evidence without the need for darkroom/night-time conditions. This system is backed by software that automatically detects, sizes and records even the weakest of fluorescent indications and alerts the user to their presence under conditions which would make it extremely difficult ‐ if not impossible- for a human investigator to detect. Fluorescent light sources (FLS) are widely used in investigations of crime scenes and sites of fires to detect the fluorescent signatures of body fluids, bone/teeth fragments, accelerants, and narcotics. Currently, due to the low levels of fluorescence involved, it is often necessary to perform such investigations under dark room conditions whilst indoors and at night whilst outdoors. This can lead to investigation delays in the detection of time-sensitive evidence due to the requirement for dark conditions, which can also necessitate the construction of tents and awnings and reduction of working area. Moreover, reliance on human investigators, also means that attention span and skill levels are subjective and variable, resulting in quality standard inconsistencies and sometimes the failure to detect evidence altogether. The system contains a range of inbuilt, self-contained light sources (365, 455, 530, 590, 400,505 and 625) nm and in-built digital filtering. Moreover, there is no need to resort to multiple colour filters as digital RGB colour-space is implemented. (It also provides the unique advantage of not always having to sift through ashes at fire-scene investigations to detect bone fragments as they can be detected in-situ with the system). All scans of a scene under bright light conditions, have a photographic map of the fluorescent indications overlaid upon them, for a 100% QA and evidence recording purposes.
A Method for Labeling Flaking Disease on Ancient Murals Based on Gradient Trainable Gabor and Channel Attention Mechanism

Disease labeling is usually the first step to protect and repair mural relics scientifically and effectively. The most common method for disease labeling at present is manual mapping of disease regions on orthophoto image by humancomputer interaction. However, this method is not only time consuming, but also has different labeling result due to different expert experience. Recently, with the development of artificial intelligence, machine learning and other technologies, automatic labeling can be achieved by deep learning and so on. However, due to the wide variety and varying manifestations of mural diseases, targeted research is needed on each type of disease. For typical flaking in mural relics, this paper presents a gradient trainable Gabor and channel attention convolution network (GTGCAC-Net) to extract texture information of flaking in order to label it efficiently and accurately. Gradient trainable Gabor integrates the characteristics of gradient information and Gabor filter to obtain rich edge information and texture information and other shallow features. The decoder part uses the channel attention module to establish the dependency relationship between each feature graph in the network and enhance the ability of the network to obtain remote context information. Residual skip connections are used to better integrate the texture features extracted by shallow convolution with the deeper abstract features of the encoder. Experiments on the murals of Fengguo Temple in Yi County, Jinzhou, Liaoning Province, China, show that the method proposed in this paper can achieve automatic labeling of flaking of the mural relics.
ANALISIS SISTEM INFORMASI AKUNTANSI PENJUALAN KAMAR PADA HOTEL CITRA III KOTA METRO

This study aims to determine the accounting information system for room sales, cash receipts and disbursements at Hotel Citra III Metro City. Data obtained from observations, documentation and interview results. Research data analysis was carried out using qualitative descriptive data analysis. The results showed that the room sales accounting information system at Citra III Hotel Metro City was not yet effective, because there were still weaknesses in its internal control, namely supervision of the accounting information system, especially in the room sales accounting information system.
Support Is Only a #Hashtag Away

Early in my teaching career, developing a lesson plan or museum program could be a lonely experience. For most of my five years in the classroom I was on my own — and it wasn’t pretty. Planning usually involved binders of lessons and lots of trial and error. Thankfully, social media and web-based, shared documents have made this a much more collaborative experience. The challenge is staying focused on what you are looking for, and not getting overwhelmed in the process.
SWAP: Exploiting Second-Ranked Logits for Adversarial Attacks on Time Series

Time series classification (TSC) has emerged as a critical task in various domains, and deep neural networks(DNN) have shown superior performance in TSC tasks. However, these models are vulnerable to adversarial attacks, where subtle perturbations can significantly impact the prediction results. Existing adversarial methods often suffer from over-parameterization or random logit perturbation, hindering their effectiveness. Additionally, increasing the attack success rate (ASR) typically involves generating more noise, making the attack more easily detectable. To address these limitations, we propose SWAP, a novel attacking method for TSC models. SWAP focuses on enhancing the confidence of the second-ranked logits while minimizing the manipulation of other logits. This is achieved by minimizing the KL-divergence between the target logit distribution and the predictive logit distribution. Experimental results demonstrate that SWAP achieves state-of-the-art performance, with an ASR exceeding 50 % and an 18 % increase compared to existing methods.
Personality Type Analysis through Handwriting Characteristics Mapping using Invariant Moment Descriptors

Handwriting patterns are unique to each individual and can offer valuable insights into their mental health conditions, personality traits, behavioral tendencies, mindsets, and more. To effectively analyze someone's personality or solve a problem using their handwriting, it is crucial to employ suitable descriptors that accurately represent the essential information it contains. Therefore, this study aims to explore the application of invariant moments as descriptors to map personality types using the psychological technique of enneagrams in conjunction with handwriting patterns. The main procedures in this research involve pre-processing, texture-based feature extraction utilizing seven invariant moment values, and applying the chi-square similarity measure. Through testing with 49 handwriting samples and 120 reference data points, it was discovered that 42 writings were successfully and accurately mapped to their corresponding personalities, achieving an impressive accuracy rate of 85.7%. This research also reaffirms the validity of personality analysis through a system that utilizes graphological techniques, as demonstrated by a 4.1% increase in accuracy through the inclusion of invariant moment descriptors when compared to psychologist analysis.
Facemask Detection using the YOLO-v5 Algorithm: Assessing Dataset Variation and R esolutions

The Covid-19 pandemic has made it imperative to prioritize health standards in companies and public areas with a large number of people. Typically, officers oversee the usage of masks in public spaces; however, computer vision can be employed to facilitate this process. This study focuses on the detection of facemask usage utilizing the YOLO-v5 algorithm across various datasets and resolutions. Three datasets were employed: the face with mask dataset (M dataset), the synthetic dataset (S dataset), and the combined dataset (G dataset), with image resolutions of 320 pixels and 640 pixels, respectively. The objective of this study is to assess the accuracy of the YOLO-v5 algorithm in detecting whether an individual is wearing a mask or not. In addition, the algorithm was tested on a dataset comprising individuals wearing masks and a synthetic dataset. The training results indicate that higher resolutions lead to longer training times, but yield excellent prediction outcomes. The system test results demonstrate that face image detection using the YOLO-v5 method performs exceptionally well at a resolution of 640 pixels, achieving a detection rate of 99.2 percent for the G dataset, 98.5 percent for the S dataset, and 98.9 percent for the M dataset. These test results provide evidence that the YOLO-v5 algorithm is highly recommended for accurate detection of facemask usage.
Editorial Special Issue on Causality: Fundamental Limits and Applications

Causal determinism, is deeply ingrained with our ability to understand the physical sciences and their explanatory ambitions. Besides understanding phenomena, identifying causal networks is important for effective policy design in nearly any avenue of interest, be it epidemiology, financial regulation, management of climate, etc. This special issue covers several areas where causal inference research intersects with information theory and machine learning.
BEHAVIORAL FACTORS IN DETERMINING INTENTION TOWARDS INDEBTEDNESS AMONG GOVERNMENT SERVANTS

Malaysian households’ indebtedness has nearly RM1.38 trillion worth which is a symptom as opposed to a cause. Undoubtedly, Malaysia's household debt-to-GDP ratio of 89% is among the highest. Indeed, worsen debt trend among the government servants are significantly pop up this issue of borrowing. The purpose of this research is to look into the influence of behavioral factors on intention towards indebtedness by using Theory of Planned Behavior. Using the partial least square (SmartPLS) technique, a survey of 377 government employees in Northern Malaysia was conducted using the exploratory method. Respondents completed a self-administered questionnaire regarding behavioural factors. The findings validated two out of the three proposed hypotheses. Among those variables, intention towards indebtedness had an extremely significant relationship with both attitude towards indebtedness and subjective norm. The results may assist the government agencies in formulating saving strategy that will lessen Malaysian government servants’ risk of financial ruin. Previous studies on indebtedness have been conducted using survey approach among individuals by using self-constructed model. Meanwhile, this study focuses on the government servant by applying the theory of Planned Behaviour.
Self-supervised Pre-training for Robust and Generic Spatial-Temporal Representations

Advancements in mobile sensing, data mining, and artificial intelligence have revolutionized the collection and analysis of Human-generated Spatial-Temporal Data (HSTD), paving the way for diverse applications across multiple domains. However, previous works have primarily focused on designing task-specific models for different problems, which lack transferability and generalizability when confronted with diverse HSTD. Additionally, these models often require a large amount of labeled data for optimal performance. While pre-trained models in Natural Language Processing (NLP) and Computer Vision (CV) domains have showcased impressive transferability and generalizability, similar efforts in the spatial-temporal data domain have been limited. In this paper, we take the lead and introduce the Spatial-Temporal Pre-Training model, $i.e$., STPT, which is connected with a self-supervised learning task, to address these limitations. STPT enables the creation of robust and versatile representations of HSTD. We validate our framework using real-world data and demonstrate its efficacy through two downstream tasks, $i.e$., trajectory classification and driving activity identification $(e.g$., identifying seeking $vs$. serving behaviors in taxi trajectories). Our results achieve an accuracy of 83.125% (16.2% higher than the average baseline) for human mobility identification and an accuracy of 77.88% (13.0% higher than the average baseline) for the human activity identification task. These outcomes underscore the potential of our pre-trained model for diverse downstream applications within the spatial-temporal data domain.