Reviewerly/exHarmony huggingface dataset:
Improving Fraud Detection and Concept Drift Adaptation in Credit Card Transactions Using Incremental Gradient Boosting Trees

Due to the increase in the use of credit cards in electronic shopping, card payments for online commerce have rapidly become a popular trend, which also led to the growth in the number of retailers. Because of these various online shopping options, a more frequent variation in the spending behaviors of the customers and purchasing trends of online markets, known as concept drift problem, can be observed over time which also causes an increase in the need for novel fraudulent strategies. This drifting problem may significantly hinder the effective performance of state-of-the-art fraud detection approaches in real credit card transaction data, which also has the imbalanced class distribution problem. In this study, a card-based incremental Gradient Boosting Tree (GBT) is investigated to detect credit card frauds and to adapt in real-time to drifts occurred in online transactions. The card-based incremental learning is achieved in which the transactions of the fraudulent credit cards reported in each day are incrementally learned by the GBT model. Therefore, the card-based incremental GBT model is compared with the regular GBT model, and retraining of a new transaction set formed by combining the previous set and the transactions of the cards reported as fraudulent. The experiments have been carried out on the 4-month real transaction data from December 2019 to March 2020 in which the concept drift problem occurred in December, dramatically affecting the performance of the GBT model. In these experiments, the improvements in the fraud detection performance have been realized in all months, and also the effectiveness of the card-based increment has been verified by comparing it with the transaction-based incremental learning that may cause catastrophic forgetting problem.
A proposal for online analysis and identification of fraudulent financial transactions

Financial institutions handle with hundreds of thousands of wire transactions per day and need to ensure security and quality for their customers. Searching on predefined patterns is insufficient to identify frauds due to continuous evolution of fraudulent methods used by criminals. Systems used for this purpose are based on the application of some methods of Artificial Intelligence, neglect human process analysis and make little use of Visual Analytics (VA) techniques. Frauds detection domain involves time-oriented and multivariate aspects to identify anomalous transactions making fraud detection a difficult task. We propose the creation of a model for each customer based on his/her behavior, using techniques of identification of outliers and conducting analysis through VA to reduce the false positive rate in the identification of fraudulent financial transactions process. We apply this approach to a real Brazilian financial institution with a daily volume of more than 30 million of financial transactions. Our framework includes a hybrid approach: (1) use of unsupervised outlier detection algorithms; and (2) use of VA to support the real time human analysis with the aim of reducing the incidence of false positives. Potential fraudulent information are presented using VA techniques allowing specialists to evaluate suspicious transactions with no increase of the normal processing times. The initial results obtained sign that there are experimental evidence that our approach can overcome the performance of the fraud detection method today used at the Brazilian institution.
New Approach to Dishonest-Majority Secure Multiparty Computation for Malicious Adversaries when n &lt; 2k − 1

In general, the (k, n) threshold secret sharing scheme (SSS) does not allow unconditionally secure multiparty computation (MPC) for n <; 2k - 1 . Therefore, Iwamura research team has been focusing on conditions under which the secure MPC can be performed securely for n <; 2k - 1, and found that their secure MPC protocol for n <; 2k - 1 is allowed under these three conditions against semi-honest adversaries: (1) the secret information does not contain 0 in the secure multiplication; (2) there is a set of distributed values for 1 using random numbers unknown to the adversaries; (3) the position of the values in the set of distributed values handled by each server is fixed during the sequence of computation. However, their protocol is not secure against malicious adversaries while SPDZ, which is representative secure MPC protocol, is secure against malicious adversaries. Therefore, we propose a secure MPC protocol against malicious adversaries based on the protocol of Iwamura research team.
Privacy-Preserving Public Verification of Ethical Cobalt Sourcing

Cobalt is a key ingredient of lithium-ion batteries and therefore is crucial for many modern devices. To ensure ethical sourcing, consumers need a way to verify provenance of their cobalt-based products, including the percentage of artisanally mined (ASM) cobalt. Existing frameworks for provenance and supply chain traceability rely on distributed ledgers. Providing public verifiability via permissionless distributed ledgers is trivial. However, offering public verifiability based on confidential production details seems contradictory. Hence, existing frameworks lack public verifiability of ratios between commodities while ensuring confidentiality of supply chain details. We propose a protocol that allows end consumers to verify the percentage of ASM cobalt in their products. Unlike previous solutions, production details are published and processed entirely in encrypted form by employing homomorphic encryption and proxy re-encryption. Thus, it ensures a high level of confidentiality of supply chain data. It has constant consumer-side complexity, making it suitable for mobile devices.
The Recommendation of a Practical Guide for Doctoral Students Using Recommendation System Algorithms in the Education Field

Recommendation systems provide an approach to facilitate the user’s desire. It is helpful in recommending things from various domains like e-commerce, the service industry, and social networking sites. The most researcher we found is based in the trading domain. However, there is limited information on the impact of recommender systems in other domains like education. Recently recommendation systems have proved to be efficient for the education sector as well. The online recommendation system has become a trend. Nowadays rather than going out and buying items for themselves, online recommendation provides an easier and quicker way to buy items, and transactions are also quick when it is done online. Recommended systems are powerful new technology and it helps users to find items that they want to buy. A recommendation system is broadly used to recommend the most appropriate products to end users. Thus, the objective of this study is to summarize the current knowledge that is available with regard to recommendation systems that have been employed within the education domain to support educational practices. Our results provide some findings regarding how recommendation systems can be used to support main areas in education, what approaches techniques or algorithms recommender systems use, and how they address different issues in the academic world.
Crafting Stories: Smart and Electronic Textile Craftsmanship for Interactive Books

'Crafting Stories' investigates the potentials of smart and electronic textile craftsmanship in the context of interactive books. A handmade interactive eTextile book, copying a handmade interactive textile book my late grandmother made, is presented as a prototypical artefact. The presentation of this practice-based research at the intersection of textile crafts, electronic and computational technology, and storytelling explicitly focuses on the interactive qualities, materials used, and the story experience. It introduces and contextualizes 'The Book My Grandmother Might Have Made', annotates design decisions, and pictures the making of the book. I report on early explorations of users interacting with the crafted stories and close with discussing the possibilities and perspectives of crafting tangible, embedded, and embodied interactions within (eTextile) books.
Pre-training on dynamic graph neural networks

The pre-training of the graph neural network model is to learn the general characteristics of large-scale graphs or the similar type of graphs usually through a self-supervised method, which allows the model to work even when node labels are missing. However, the existing pre-training methods do not take the temporal information of edge generation and the evolution process of graph into consideration. To address this issue, this paper proposes a pre-training method based on dynamic graph neural networks (PT-DGNN), which uses the dynamic graph generation task to simultaneously learn the structure, semantics, and evolution features of the graph. The method mainly includes two steps: 1) dynamic subgraph sampling, and 2) pre-training using two graph generation tasks. The former preserves the local time-aware structure of the original graph by sampling the latest and frequently interacting nodes. The latter uses observed edges to predict unobserved edges to capture the evolutionary characteristics of the network. Comparative experiments on three realistic dynamic network datasets show that the proposed pre-training method achieves the best results on the link prediction fine-tuning task and the ablation study and further verifies the effectiveness of the above two steps.
Applying Gradient Boosting Trees and Stochastic Leaf Evaluation to MCTS on Hearthstone

Collectible card games are an interesting testing ground for artificial intelligence algorithms, mainly because of their stochasticity and high branching factor. In this work, the performance of a monte carlo tree search-based agent, enhanced with a gradient boosting tree classifier on the simulation phase, is investigated on Hearthstone. Furthermore, the impact of the combination of random simulations and the classifier's predictions is studied, as well as its correlation with the action space and the tree's depth. The aforementioned approach has been implemented in the Metastone framework and has been tested against the vanilla approach and the state-of-the-art algorithm, both provided by the framework itself. Over a set of evaluation games, it is demonstrated that the examined methodology significantly outperforms the vanilla-MCTS and is even matched with the heuristic-driven minimax algorithm.
TransCrossCF: Transition-based Cross-Domain Collaborative Filtering

The success of cross-domain recommender systems in capturing user interests across multiple domains has recently brought much attention to them. These recommender systems aim to improve the quality of suggestions and defy the cold-start problem by transferring information from one (or more) source domain(s) to a target domain. However, most cross-domain recommenders ignore the sequential information in user history. They only rely on an aggregate or snapshot of user feedback in the past. Most importantly, they do not explicitly model how users transition from one domain to another domain as users continue to interact with different item domains. In this paper, we argue that between-domain transitions in user sequences are useful in improving recommendation quality, dealing with the cold-start problem, and revealing interesting aspects of how user interests transform from one domain to another. We propose TransCrossCF, transition-based cross-domain collaborative filtering, that can capture both within and between domain transitions of user feedback sequences while understanding the relationship between different item types in different domains. Specifically, we model each purchase of a user as a transition from his/her previous item to the next one, under the effect of item domains and user preferences. Our intensive experiments demonstrate that TransCrossCF outperforms the state-of-the-art methods in recommendation task on three real-world datasets, both in the cold-start and hot-start scenarios. Moreover, according to our context analysis evaluations, the between-domain relations captured by TransCrossCF are interpretable and intuitive.
Learning to Use Adaptive Motion Primitives in Search-Based Planning for Navigation

Heuristic-based graph search algorithms like A* are frequently used to solve motion planning problems in many domains. For most practical applications, it is infeasible and unnecessary to pre-compute the graph representing the whole search space. Instead, these algorithms generate the graph incrementally by applying a fixed set of actions (frequently called motion primitives) to find the successors of every node that they need to evaluate. In many domains, it is possible to define actions (called adaptive motion primitives) that are not pre-computed but generated on the fly. The generation and validation of these adaptive motion primitives is usually quite expensive compared to pre-computed motion primitives. However, they have been shown to drastically speed up search if used judiciously. In prior work, ad hoc techniques like fixed thresholds have been used to limit unsuccessful evaluations of these actions. In this paper, we propose a learning-based approach to make more intelligent decisions about when to evaluate them. We do a thorough empirical evaluation of our model on a 3 degree-of-freedom (dof) motion planning problem for navigation using the Reeds-Shepp path as an adaptive motion primitive. Our experiments show that using our approach in conjunction with search algorithms leads to over 2x speedup in planning time.
Deep Learning Detection for superimposed control signal in LEO-MIMO

Multiple-input multiple-output (MIMO) communication systems using multiple low-Earth orbital (LEO) satellites achieve higher capacity than conventional LEO systems. However, in previous research, control signals are allocated to a different frequency band for each satellite signal in order to estimate its Doppler frequency. The increase in the number of satellites reduces unoccupied MIMO signal bandwidth and hence the overall capacity. This study aimed to prevent such capacity reduction by introducing the superimposition of control signals. Such control signals occupy a frequency bandwidth of the equivalent of only one control signal; therefore, they can prevent the reduction of the overall capacity. The main challenge is estimating the satellites' Doppler frequencies from the waveforms of Doppler-affected superimposed control signals. To overcome this challenge, we propose the adoption of a deep learning technique.
Synergetic control algorithms for a multidimensional biomedical model under conditions of nonrandom and random disturbances using kernel regression

Abstract The purpose is to investigate the applicability of the principles of the synergetic control theory to a primitive object with the initial description in the form of a system of nonlinear differential equations with a delay. In terms of intensionality, control will be here understood as the rule of intake of the substance (agent) into a living organism, which serve as antidotes to a disease, in the form of a pair of variables (time of the substance administration and amount of the administered substance). Based on a new algorithm of a discrete nonlinear analytical synthesis, three systems of control over such an object are designed for the following cases: 1) a control object is functioning under completely determined conditions; 2) a control object is affected by the deterministic unknown constrained disturbances via the control variable; 3) presence of an additive random noise on any variable. The results of a comparative numerical modeling of the designed control systems are presented, which function without any preliminary filtration of the random variable measurements and with a filtration by a kernel regression algorithm. The results can be used in the decision support systems.
Simple, Fast, Accurate Object Detection based on Anchor-Free Method for High Resolution Remote Sensing Images

On object detection of remote sensing images, speed and accuracy are considered equally significant for time-sensitive tasks. Traditional object detection methods of remote sensing images use anchors to regress the bounding boxes and the position of objects which quite limit the speed of detection. In this paper, we make an attempt at employing an anchor-free method named CenterNet to address the problem of object detection for remote sensing images. This model computes the key point to regress the location, local offset and size without enumerating a nearly exhaustive list of potential object locations and size. Thus CenterNet is simpler, faster, more accurate and end-to-end differentiable than other anchor-based methods. We evaluate the performance of Cen-terNet with various backbones on remote sensing images dataset NWPU VHR-10. Among them, CenterNet based on DLA-34 backbone achieves the highest accuracy of 95.7% on the test set, which significantly outperforms most state-of-the-art methods while maintaining a real-time prediction speed of 31 FPS on NVIDIA GTX 1080. Analysis on experimental results demonstrates that this anchor-free network achieves a better balance between accuracy and speed for this task compared to other methods.
Adapting Google Translate using Dictionary and Word Embedding for Arabic-Indonesian Cross-lingual Information Retrieval

The translation has an essential role in Cross-lingual Information Retrieval. Translation using a dictionary is reliable even though it has a limited vocabulary. Translation using google translate, in some cases, using different words used in document target words. The translation process causes word translation to be less accurate to get relevant documents. In this paper, we proposed a new translation approach by adapting google translate using a dictionary and word embedding in Arabic-Indonesian Cross-lingual Information Retrieval. The dictionary is the primary resource used for translation improved by Levenshtein distance and FastText for finding the correct word translation. Google translate is used to complete translation when the word does not exist in the dictionary resource. The proposed method archive a BLEU score of 0.47. This score is higher than the other comparison resource score. The proposed method successfully improves the translated query to retrieve more relevant documents in cross-lingual information retrieval based on this implementation.
Enhancing Multi-factor Friend Recommendation in Location-based Social Networks

Recently, location features have become available by the most popular online social networks such as Facebook, Twitter, and Foursquare. These networks are called location-based social networks (LBSN), which allow users to share their locations and location-related content. One of the services that LBSNs present is friend recommendation. This service recommends new friends to users based on their posts, media, opinions, locations or social ties. Several studies have been conducted in the area of friend recommendation by LBSNs. Nevertheless, addressing users' opinions on different topics, and considering asymmetric (directed) relations as one indirect relation, are factors that have not been extensively reviewed. In this paper, we propose a new recommendation model in LBSNs for recommending friends to users based on social, spatial, and textual features. These features are extracted based on factors to enhance the accuracy of friend recommendation process. We propose a novel factor for the extraction of the textual feature, also, we enhance factors for social and spatial features. The recommendation model was tested and evaluated using two real datasets from Twitter. The results of recommending top-k friends showed an average accuracy of 97.88%, and the experiments also showed the efficiency of the recommendation model against another friend recommendation model.
Recursive Division of Image for Explanation of Shallow CNN Models

In this paper, we propose the research of the recursive division approach to get an explanation for a particular decision of the shallow black box model. The core of the proposed method is the division of the image being classified into separate rectangular parts, followed by the analysis of their influence on the classification result. Such divisions are repeated recursively until the explanation of the classification result is found, or the size of parts is too small. As a result, the pair of images with complement hidden parts is discovered, the first one of which preserves both the most valuable parts and the classification result of the initial image. The second image represents the result of hiding the most valuable parts of an initial image that leads to the different classification for the binary classification problem. Experimental research (applied for Food-5K and concrete crack images datasets) proved that the quality of the proposed method might be close to LIME or even better, while the performance of recursive division is better.
Image Classification with Additional Non-decision Labels using Self-supervised learning and GAN

In recent years, image recognition has been improved by using machine learning to extract high accuracy. However, in most studies, it is assumed that the image input to a model has an answer. But then, when an unanswered image or an image of extremely low quality is entered into real space, the model is forced to answer and often makes a mistake. In this paper, we propose a method to discriminate between unscrutinized images using self-supervised learning and an image generation model using a subtask.
A Hierarchical Knowledge and Interest Propagation Network for Recommender Systems

To address the data sparsity and cold start issues of collaborative filtering, side information, such as social network, knowledge graph, is introduced to recommender systems. Knowledge graph, as a sort of auxiliary and structural data, is full of semantic and logical connections among entities in the world. In this paper, we propose a Hierarchical Knowledge and Interest Propagation Network(HKIPN) for recommendation, where a new heterogeneous propagation method is presented. Specifically, HKIPN propagates knowledge and user interest simultaneously in a unified graph combined by user-item bipartite interaction graph and knowledge graph. During the propagation, a hierarchical method is devised to aggregate a node's high-order neighbors explicitly and concurrently. Besides, an attention mechanism is employed to discriminate the importance of neighbors. Furthermore, due to information decay in the process of propagation, the decay factor, as the weight of each hierarchical representation to compose the final user-and-item representations, is taken into account. We apply the proposed model to three benchmark datasets about movie, book, and music recommendation and compare it with state-of-the-art baselines. The experiment results and further studies demonstrate that our approach outperforms compelling recommender baselines.
Research and Implementation of Robot Path Planning Based on Computer Image Recognition Technology

Abstract This paper focuses on the image processing and recognition module. The path planning module is only a simple algorithm research and implementation, the current system research needs to continue to improve, especially the path planning module. This paper studies the robot path planning based on image recognition, and on the robot contest platform to do simulation experiment, but in the actual application of robot environment is complicated, the robot camera collected images may be part contains several pieces of information, or part of the image and the other robot, the robot path planning of the how to do it. Moreover, the robot may be beyond our set range, and the dynamic path planning algorithm cannot guarantee the absolute acquisition of the optimal strategy. Therefore, how to improve the existing system, improve the accuracy of image recognition and improve the path planning algorithm is one of the important research work in the future.