Reviewerly/exHarmony huggingface dataset:
SANCTIONS EVASION AND VIRTUAL ASSETS: IMPLICATIONS FOR NATIONAL SECURITY

2022 was the year marking a significant increase in the use of virtual assets for illicit activities such as sanctions evasion. Most of the entities conducting these activities are linked to Russia, North Korea and Iran, which are subject to international sanctions imposed by the international community. The paper presents key elements about the use of virtual assets in illicit activities by sanctioned entities and highlights the necessity to increase defence and intelligence resources for better data analysis on this type of entities. Analyzing data about virtual assets transactions requires strong collaboration between public and private organizations, with a focus on an intelligence-led approach, considering the growing links between cybercrime, money laundering, terrorist financing, special operations conducted by adversaries. In order to support this collaboration, it is essential to prioritize the education of decision-makers on the necessity to focus on technical data.
Double Logistic Regression Approach to Biased Positive-Unlabeled Data

Positive and unlabelled learning is an important non-standard inference problem which arises naturally in many applications. The significant limitation of almost all existing methods addressing it lies in assuming that the propensity score function is constant and does not depend on features (Selected Completely at Random assumption), which is unrealistic in many practical situations. Avoiding this assumption, we consider parametric approach to the problem of joint estimation of posterior probability and propensity score functions. We show that if both these functions are logistic with different parameters (double logistic model) then the corresponding parameters are identifiable. Motivated by this, we propose two approaches to their estimation: a joint maximum likelihood method and the second approach based on an alternating maximization of two Fisher consistent approximations. Our experimental results show that the proposed methods perform on par or better than the existing methods based on Expectation-Maximisation scheme.
Deep Reinforcement Learning with Implicit Imitation for Lane-Free Autonomous Driving

Implicit imitation assumes that learning agents observe only the state transitions of an agent they use as a mentor, and try to recreate them based on their own abilities and knowledge of their environment. In this paper, we put forward a deep implicit imitation Q-network (DIIQN) model, which incorporates ideas from three well-known Deep Q-Network (DQN) variants. As such, we enable a novel implicit imitation method for online, model-free deep reinforcement learning. Our thorough experimentation in the complex environment of the emerging lane-free traffic paradigm, verifies the benefits of our approach. Specifically, we show that deep implicit imitation RL dramatically accelerates the learning process when compared to a “vanilla” DQN method; and, unlike explicit imitation reinforcement learning, it is able to outperform mentor performance without resorting to additional information, such as the mentor’s actions.
Cognitive Effects in Large Language Models

Large Language Models (LLMs) such as ChatGPT have received enormous attention over the past year and are now used by hundreds of millions of people every day. The rapid adoption of this technology naturally raises questions about the possible biases such models might exhibit. In this work, we tested one of these models (GPT-3) on a range of cognitive effects, which are systematic patterns that are usually found in human cognitive tasks. We found that LLMs are indeed prone to several human cognitive effects. Specifically, we show that the priming, distance, SNARC, and size congruity effects were presented with GPT-3, while the anchoring effect is absent. We describe our methodology, and specifically the way we converted real-world experiments to text-based experiments. Finally, we speculate on the possible reasons why GPT-3 exhibits these effects and discuss whether they are imitated or reinvented.
Semantic-Aware Dual Contrastive Learning for Multi-Label Image Classification

Extracting image semantics effectively and assigning corresponding labels to multiple objects or attributes for natural images is challenging due to the complex scene contents and confusing label dependencies. Recent works have focused on modeling label relationships with graph and understanding object regions using class activation maps (CAM). However, these methods ignore the complex intra- and inter-category relationships among specific semantic features, and CAM is prone to generate noisy information. To this end, we propose a novel semantic-aware dual contrastive learning framework that incorporates sample-to-sample contrastive learning (SSCL) as well as prototype-to-sample contrastive learning (PSCL). Specifically, we leverage semantic-aware representation learning to extract category-related local discriminative features and construct category prototypes. Then based on SSCL, label-level visual representations of the same category are aggregated together, and features belonging to distinct categories are separated. Meanwhile, we construct a novel PSCL module to narrow the distance between positive samples and category prototypes and push negative samples away from the corresponding category prototypes. Finally, the discriminative label-level features related to the image content are accurately captured by the joint training of the above three parts. Experiments on five challenging large-scale public datasets demonstrate that our proposed method is effective and outperforms the state-of-the-art methods. Code and supplementary materials are released on https://github.com/yu-gi-oh-leilei/SADCL.
Iterative Reward Shaping Using Human Feedback for Correcting Reward Misspecification

A well-defined reward function is crucial for successful training of an reinforcement learning (RL) agent. However, defining a suitable reward function is a notoriously challenging task, especially in complex, multi-objective environments. Developers often have to resort to starting with an initial, potentially misspecified reward function, and iteratively adjusting its parameters, based on observed learned behavior. In this work, we aim to automate this process by proposing ITERS, an iterative reward shaping approach using human feedback for mitigating the effects of a misspecified reward function. Our approach allows the user to provide trajectory-level feedback on agent’s behavior during training, which can be integrated as a reward shaping signal in the following training iteration. We also allow the user to provide explanations of their feedback, which are used to augment the feedback and reduce user effort and feedback frequency. We evaluate ITERS in three environments and show that it can successfully correct misspecified reward functions.
Do Topic and Causal Consistency Affect Emotion Cognition? A Graph Interactive Network for Conversational Emotion Detection

Emotion recognition in conversations (ERC) typically requires modeling both intra- and inter-speaker context dependencies. However, when modeling inter-speaker dependencies, it may not capture differences among other participants in the conversation. Recent ERC research has attempted to improve utterance representations by utilizing speakers’ commonsense knowledge. Nonetheless, these studies ignore the causal consistency in knowledge between the two participants, which contradicts the above modeling of speaker-sensitive context dependencies. Additionally, it is observed that historical utterances from various topics are blindly leveraged in context modeling, which fails the inter- and intra-topic coherence. To address these issues, we propose the topic- and causal-aware interactive graph network (TCA-IGN). Specifically, we suggest a graph encoder to model topic-level context dependencies, achieving inter- and intra-topic coherence. The topics of utterances are derived from a context-sensitive neural topic model. Then, we present a causal-aware graph attention to keep the speaker’s causal consistency in commonsense knowledge, improving speaker-level context modeling. Finally, considering the defect of modeling inter-speaker or inter-topic context dependencies, we employ supervised contrastive learning to sweeten it. Experimental results show that TCA-IGN outperforms state-of-the-art methods on three public conversational datasets.
Analysis and Design of Low Complexity Receiver for UWB Transmitted Reference Based on Wavelets Transform

The Rake receiver has become very popular in wireless communication in recent years, but it has a number of drawbacks, including complexity, high noise, poor performance, and a slow transmission rate. As a result, a system with high performance, low complexity, low noise, and high efficiency is required. In this article, the idea of a new discrete wavelet transform (DWT)-based system is called Low Complexity (LC) Transmitted Reference (TR) Ultra Wideband (UWB) that achieves high performance, low complexity, higher transmission rate, and lower noise than previous discrete wavelet transform systems. High frequencies that caused noise in the system were cut off using DWT. To remove these frequencies, DWT uses two types of FIR filters. To get a better result, use three levels in this system. The simulation results show that when three levels of noise reduction are used, the effect is to remove high frequencies that cause noise in the TR UWB received signal.
Image classification of Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning

Abstract. The development of information technology in facial recognition is influenced by a faster and more accurate authentication system. This allows the computer system to identify a person's face.&#x0D; Purpose: Similar to fingerprints and the retina of the human eye, each person's face has a different shape and contour. Since it is known that the human face provides a lot of information, as well as topics that attract attention make it studied intensively.&#x0D; Methods/Study design/approach: Several studies examining information from human faces are facial recognition. One of the approaches used to recognize facial imagery is through the use of a Convolutional Neural Network (CNN). CNN is a method in the field of Deep Learning that can be used to recognize and classify objects in digital images. In this study, the method used to implement facial image classification is the Xception architecture CNN algorithm with a transfer learning approach.&#x0D; Result/Findings: The dataset used in this study was obtained from Kaggle, namely the Face Shape Dataset which contains 5000 data. After testing, an accuracy rate of 96.2% was obtained in the training process and 81.125% in the validation process. This study also uses new data to test the model that has been made, and the results show an accuracy rate of 85.1% in classifying facial imagery.&#x0D; Novelty/Originality/Value: Therefore, it can be said that the model created in this study has the ability to classify images of facial shapes Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning.
PENGAPLIKASIAN BIM 5D UNTUK PEKERJAAN ARSITEKTUR PADA PROYEK GEDUNG IGD RSUD WARAS WIRIS BOYOLALI

&lt;p&gt;Building Information Modelling (BIM) is a system for digitizing data processing that can help work in the construction industry run more effectively and efficiently. One type of BIM is 5D BIM. 5D BIM has the function of determining the take-off quantities and analyzing the costs of a building. An example of 5D BIM is Cubicost. Cubicost BIM is a BIM from the Glodon company that allows you to calculate work volumes in a detailed and precise way. In this research, the case study presented is an architectural work on the Waras Wiris Boyolali Hospital Emergency Building Construction Project. The architectural section was selected because architectural works play an important role in nominal terms, so the determination of volume greatly influences construction costs. This research uses Cubicost TAS and TRB software. Cubicost Software's volume results have also been validated so that they show a 99% correlation figure with volume obtained from conventional calculations. Based on the research carried out, the cost of using Cubicost software for architectural works in the Waras Wiris Boyolali Emergency Hospital Building Construction Project is Rp. 1.695.645.165,47. Using the Cubicost software also produces an efficiency of 11.550% on RAB from the consultant.&lt;/p&gt;
Handling of Past and Future with Phenesthe+

Writing temporal logic formulae for properties that combine instantaneous events with overlapping temporal phenomena of some duration is difficult in classical temporal logics. To address this issue, in previous work we introduced a new temporal logic with intuitive temporal modalities specifically tailored for the representation of both instantaneous and durative phenomena. We also provided an implementation of a complex event processing system, Phenesthe, based on this logic, that has been applied and tested on a real maritime surveillance scenario. In this work, we extend our temporal logic with two extra modalities to increase its expressive power for handling future formulae. We compare the expressive power of different fragments of our logic with Linear Temporal Logic and dyadic first-order logic. Furthermore, we define correctness criteria for stream processors that use our language. Last but not least, we evaluate empirically the performance of Phenesthe+, our extended implementation, and show that the increased expressive power does not affect efficiency significantly.
Making the MetAverse A Reality: Challenges And Opportunities

In the evolving landscape of digital reality, the concept of the Metaverse has rapidly gained traction in industry and academia alike. The Metaverse - a richly interconnected blend of digital and virtual spaces where people can interact, work, learn, and socialize - signifies a new era of the Internet, characterized by immersive interaction and a profound sense of presence. No longer confined to the realms of science fiction, the Metaverse is becoming an increasingly tangible reality, accelerated by rapid advancements in communication technologies, virtual reality (VR), augmented reality (AR), blockchain, artificial intelligence (AI), and more.
Yoga Pose Estimation with Machine Learning

Yoga pose estimation involves the use of computer vision algorithms to automatically identify and track yoga poses from images or videos. This study focuses on improving the accuracy and performance of pose estimation systems through the application of OpenPose keypoint detection, SMOTE, and LightGBM classification. OpenPose is utilized for keypoint detection, enabling the identification of specific points on the body and resulting in more precise pose estimation. To address class imbalance issues, SMOTE is employed to ensure a balanced representation of poses by oversampling minority classes. Additionally, LightGBM classification is utilized to enhance model performance, benefiting from its ability to handle large datasets, faster training speed, and high accuracy. The research utilizes two datasets: the Yoga Pose Image Classification dataset and a self-collected dataset, consisting of 5994 and 5431 images, respectively. The proposed method achieved an accuracy of 56.18% on the Yoga Pose Image Classification dataset with 107 classes and 71.47% on the self-collected dataset with 50 classes, outperforming existing models.
A hardware acceleration architecture design for histogram equalization with locking features

Histogram equalization has been widely used in image processing, which can significantly improve image contrast and enhance the effect of display. In many imaging fields, such as handheld imaging, to achieve real-time imaging function, many FPGA-based hardware acceleration designs for histogram equalization have been proposed. However, all of the aforementioned studies employ methods for real-time histogram calculation and update, which cannot be applied to dynamic imaging applications with occlusion, overexposure, and other factors in some areas. In this paper, a histogram equalization hardware acceleration structure design with locking features is proposed, which can effectively solve the above problems. In this paper, the architecture design of hardware acceleration is first proposed, including the histogram statistics module, the histogram locking and selection module, the histogram equalization calculation module, and other core circuit modules. Then, calculation accuracy tests were conducted on each logic circuit module. Finally, the overall performance of the system was tested using a publicly available vascular imaging dataset. The experimental results show that this design can avoid computational errors in dynamic imaging applications compared with existing hardware acceleration schemes. This design can be widely applied in dynamic imaging, industrial monitoring, and other related application scenarios, promoting the development of real-time imaging technology.
Reading Recognition of Automobile Dashboard

A recognition scheme based on RGB color space image processing is proposed to recognize the representation number of car pointer. The color characteristics of the center and pointer of the car dashboard are obvious. After analyzing the histograms of R, G and B components, the Euclidean distance method of the three components is used to divide the center and pointer of the car dashboard. Then, using the thinning method to extract the skeleton of the center of the circle and obtain the coordinates of the center of the circle. Taking the coordinates of the center of the circle as the origin to establish a new coordinate system. Finally, the coordinates of the centroid of the pointer are calculated by using the method of minimum external rectangle. The centroid and the center of the circle are connected to determine a straight line. The reading of the pointer is identified by the Angle between the line and the reference line. The method is applied to the reliability experiment of automobile instrument, and the results show that the accuracy of identifying pointer indicator is relatively high and the working efficiency can be improved.
Does the Type of Recommender System Impact Users' Trust? Exploring Context-Aware Recommender Systems in Education

Educational recommender systems (RS) have become widely popular with the paradigm shift to online learning and the availability of a wide variety of learning resources. Educational RS in various education platforms use a wide variety of filtering techniques. This has led to the development of multiple types of RS. Context-aware recommender systems (CARS) are identified as an emerging type of RS that uses users' context for filtering recommendations, which makes recommendations more relevant to the user's current situation. CARS may face initial distrust compared to other RS due to the additional automation layer of context awareness and the use of more user data. Therefore, we conduct a survey-based study to find differences in user trust and perception between CARS and other RS. In the study, users viewed examples of CARS and RS. The results show that users have significantly lower trust in CARS compared to RS.
Static Code Analysis on The Effect of Virtual Secure Mode on Memory Acquisition with IDA

Memory acquisition process is one of digital forensics act. There are several tools that support memory acquisition process. At this time, there is a feature named secure mode that can caused crash or error in memory acquisition tools system and caused the tools to be unusable, also the loss of the computer memory. This research is focusing on analyzing the acquisition tools that has error or crash when the device that is being used for memory acquisition is in secure mode. The analysis is being carried out using static code analysis method, which is one of the techniques of reverse engineering, using IDA. This study aims to find the cause of the crash or error in memory acquisition tools. The purpose of this study is to be useful for digital forensic tester in understanding the potential risk of the secure mode impact in acquisition process. The results of this study indicate that different operating system and different kernel which runs in the device are the reasons that memory acquisition tools cannot run properly on VSM environment being turned on.
Online Shows Recommendations using Machine Learning in Big Data Environment

The use of machine learning has grown into a potent tool to handle enormous quantities of information in present-day information-driven surroundings. We are currently concentrating on developing systems based on artificial intelligence capable of suggesting videos on the internet for viewers of websites that stream content. We have uncovered essential components of these websites from our research, including movie information and user tastes, that could be used utilized to determine audience tastes while offering appropriate content that corresponds therewith these. In recent years, systems of recommendations have increased in favor of an approach. These types of systems provide choices for viewers for seeing movies they like according to a review of their behaviors and interests. Combining Big Data, Machine Learning, and engines for the recommendation, this research proposes an innovative approach to suggesting movies. We used an innovative approach termed Matric Factorising (MF), which may benefit form data mining techniques including segmentation and reducing dimensionality. This was applied to the Recruits-recommendation of the engine itself. Television series have moved into an entirely novel phase because of digital streaming platforms like Netflix as well as Amazon Prime. Company vast databases allow them to arrive at accurate projections regarding what information is going to be common, increasing their bottom lines and helping users to identify possibly fascinating fresh programming. In our study, algorithms for learning were implemented in an extensive data environment to analyze the popularity and achievement of different platforms. We observed that for forecasting the rating rates for programs in addition to additional variables like the style and given, classifiers based on random forests fared exceptionally well. The research presented here emphasizes just how important it is for utilizing algorithmic learning in big data events for one to learn about online entertainment and pick which shows to take in!
Internet Financial Data Mining and Analysis Based on Internet Data

Based on the financial data in the Internet, the system uses data mining technology to collect and mine the specified target financial data from the financial website page, provide corresponding financial information according to the needs of different groups, and create a financial business operation law and financial business information for today's college students and financial practitioners. The statistical analysis system of financial risk dynamics and other information completes the real-time collection of financial data with time series in Internet financial websites, so that those in need can know the real-time information and data analysis of the financial industry at any time. The system also provides personalized customized services for practitioners, complete financial screening plans, and realize the dynamic visualization of financial information. A service platform for online communication and information sharing between employees and social people. In most cases, financial data is expressed in the form of time series. This project proposes the relevant financial data mining and analysis system platform, which can efficiently mine the potential high-value data in the massive financial time series data, and is also conducive to the research of financial data analysis system in data mining. The research of this project has a certain degree of theoretical and practical significance.