Reviewerly/exHarmony huggingface dataset:
Detection System of Promotion Abuse Using Similarity and Risk Scoring Methods

Offering promotion coupons is one of the most popular strategies of online marketing to attract new customers and increase customer loyalty. However, this strategy opens chances for fraud risk as the coupons are being redeemed multiple times using fake accounts. This risk becomes a burden to marketing costs and leads to failure to accomplish the intended strategic value. Therefore, this research focuses on building an automatic detection system of online promotion abuse based on its risk level. The proposed system also must work on live stream and bulk data. Therefore, in live stream data, it could alert the administrator before the transaction finished or the next process started. After conducting an exploratory factor analysis of the 24 attributes collected from four tables of data transaction, there were seven attributes indicating promotion abuse. These attributes were the user IP address, shipping address, mobile number, member email, order email, payment ID, and product name. Then, supervised machine learning of similarity algorithms was used to build models and find the hidden correlation of attributes to indicate the promotion abuse. The result from comparing five similarity methods showed that based on the workflow and performance, the most suitable methods for this case were exact match and Levenshtein edit base. The automatic risk scoring feature of the proposed system used seven attributes of online transactions as their most prominent promotion abuse parameter based on its hidden correlation. From the system performance testing, the result values of precision, recall, and F-measure are 95%, 93%, and 0.94, respectively. These results indicate that the system performance is satisfactory.
Technology-enhanced constructivist learning environment for pharmacy students

Objective: This article focuses on constructive learning theory and demonstrates how digital technologies can develop cognitive and metacognitive abilities in pharmacy students. Method: The mixed methodology was used to analyse pharmacy educators’ (n=10) and students’ (n=26) perceptions of the efficiency of online digital resources for the creation of a constructivist learning environment. The authors adapted Constructivist Multimedia Learning Environment Survey by adding the rubric for self-reflective skills and teachers’ support assessment. The qualitative data was collected by interviewing teachers and through self-reflective open discussions. After the session of webinars and self-paced MOOC on the constructivism approach, teachers transformed video lectures into interactive video lessons, conventional theoretic-oriented tasks into web cases and web quests. Result: Comparing the results of the initial students’ evaluation of traditional learning materials and the updated ones, there were positive dynamics in all six domains (average item mean): learning to communicate (5.4 points), learning to investigate (7.8 points), learning to think (4.2 points), relevance (8.6 points), challenges (0.9 points), ease of use (1.2 points), and quality of support (7.2 points). Conclusion: Experts observed teaching activities for critical thinking and inquiry skills development, personalised communication, and integration of digital technologies. The results of the study indicated that there were positive transformations toward the constructivist learning environment.
Advertising Click-Through Rate Prediction Model Based on an Attention Mechanism and a Neural Network

With the rapid development of the Internet and the rapid expansion of the advertising market, display advertising has become the most popular means of publicity. Accurate advertising recommendation is the guarantee of Internet platform revenue, and accurate advertising click-through rate prediction is the premise of accurate recommendation. According to the requirements of the update rate and real-time of the advertising platform, the coupling relationship of the advertising business module can be divided into two categories: offline business module and online business module. The offline advertising management module is mainly based on the construction of the mathematical model, which is used to mine the complex relationship between users and commodity characteristics; the online advertising management module is mainly based on the real-time feedback of users and changes the recommendation strategy in real time by collecting feedback information. For the offline advertising management module, an advertising click-through rate prediction model based on attention mechanism and neural network is proposed, which is called CAN. This method provides richer feature interaction information for a neural network layer. For the online advertising management module, a real-time recommendation algorithm based on Gaussian process is proposed. This method can solve the universality of specific function assumptions in various environments. Through the coupling of the advertising click-through rate prediction model constructed by the offline advertising management module and the real-time recommendation algorithm of the online advertising management module, the accurate delivery of advertising can be realized.
Towards Human–Machine Recognition Alignment: An Adversarilly Robust Multimodal Retrieval Hashing Framework

The multimodality nature of web data has necessitated complex multimodal information retrieval for a wide range of web applications. Deep neural networks (DNNs) have been widely employed to extract semantic features from raw samples to improve retrieval accuracy. In addition, hashing is widely used to improve computational and storage efficiency. As such, deep hashing frameworks have been applied for multimodal retrieval tasks. However, there is still a great recognitive gap between primate brain structure-inspired DNNs and humans. On computer vision tasks, well-crafted DNN models can be easily defeated by invisible small attacks, and this phenomenon indicates a large recognition gap between DNN models and humans. Recently, adversarial defense methods have been shown to improve the human&#x2013;machine recognition alignment in several classification tasks. However, the robustness problem on the retrieval tasks, especially on the deep hashing-based multimodal retrieval models, is still not well studied. Therefore, in this article, we present an adversarially robust training mechanism to improve model robustness for the purpose of human&#x2013;machine recognition alignment on retrieval tasks. Through extensive experimental results on several social multimodal retrieval benchmarks, we show that the robust training hashing framework proposed can mitigate the recognition gap on retrieval tasks. Our study highlights the necessity of robustness enhancement on deep hashing models.
SKELETON-BASED ACTION RECOGNITION USING FEATURE FUSION FOR SPATIAL-TEMPORAL GRAPH CONVOLUTIONAL NETWORKS

Human action recognition (HAR) has been used in a variety of applications such as gaming, healthcare, surveillance, and robotics. Research on utilizing data such as color, depth, and skeletal data has been extensively conducted to achieve high-performance HAR. Compared with color and depth data, skeletal data are more compact, therefore, they are more efficient for computation and storage. Moreover, skeletal data are invariant to clothing textures, background, and lighting conditions. With the booming of deep learning, HAR has received a lot of attention. Spatial-Temporal Graph Convolution Networks (ST-GCN) have proved to be state-of-the-art architecture for HAR using skeleton data. However, this does not hold when working with challenging datasets that contain incomplete and noisy skeletal data. In this paper, a new method is proposed for HAR by adding a Feature Fusion module and applying hyperparameter optimization. The performance of the proposed method is evaluated on the challenging dataset CMDFALL and the newly-built MICA-Action3D dataset. Experimental results show that the proposed method significantly improves the performance of ST-GCN on these challenging datasets.
High Dimensional Data Processing Based on Optimized DPC Algorithm

With the rapid growth of science and technology, machine learning and big data analysis have developed more and more difficult. Similarly, data also become difficult to process and classify due to the fact that the data dimension is becoming larger and larger. Furthermore, aiming at the defect due to the amount of data the clustering using speedy examine, search, and discovery of density peaks (also known as DPC) does not adjust to data sets with large dimensions (high-dimensional). Therefore, in this paper, we suggest an optimization procedure, which we pronounce as t-dpc, and is founded on the t-sne dimension lessening technique, and which can also optimize the technique for estimation of the Gaussian kernel function, using unified measurement criteria in solving density. In the simulation based experiments, using two different data sets i.e., the UCI standard data set, and the artificial data set, the proposed DPC procedure is associated with the classical t-dpc algorithm. The empirical evaluation and investigational outcomes illustrate that the proposed method of t-dpc not merely acclimatizes to the high-dimensional data sets, nevertheless it also increases the effectiveness of the classical DPC technique.
ExpertosLF: dynamic late fusion of CBIR systems using online learning with relevance feedback

One of the main challenges in CBIR systems is to choose discriminative and compact features, among dozens, to represent the images under comparison. Over the years, a great effort has been made to combine multiple features, mainly using early, late, and hierarchical fusion techniques. Unveiling the perfect combination of features is highly domain-specific and dependent on the type of image. Thus, the process of designing a CBIR system for new datasets or domains involves a huge experimentation overhead, leading to multiple fine-tuned CBIR systems. It would be desirable to dynamically find the best combination of CBIR systems without needing to go through such extensive experimentation and without requiring previous domain knowledge. In this paper, we propose ExpertosLF, a model-agnostic interpretable late fusion technique based on online learning with expert advice, which dynamically combines CBIR systems without knowing a priori which ones are the best for a given domain. At each query, ExpertosLF takes advantage of user’s feedback to determine each CBIR contribution in the ensemble for the following queries. ExpertosLF produces an interpretable ensemble that is independent of the dataset and domain. Moreover, ExpertosLF is designed to be modular, and scalable. Experiments on 13 benchmark datasets from the Biomedical, Real, and Sketch domains revealed that: (i) ExpertosLF surpasses the performance of state of the art late-fusion techniques; (ii) it successfully and quickly converges to the performance of the best CBIR sets across domains without any previous domain knowledge (in most cases, fewer than 25 queries need to receive human feedback).
Using Word Embeddings in Detection of Temporal Expressions in Turkish Texts

Developing systems for automatically detection of date, time, duration and set expressions containing time information in texts is within the scope of Natural Language Processing research field. When studies for Turkish in the literature are reviewed, it is observed that only date and time expressions are included in the expressions detected by the models developed within the scope of Named Entity Recognition. There are studies to develop only rule-based systems on the subject of detection of temporal expressions in Turkish. Within the scope of this study, first Artificial Neural Networks based model for the detection of temporal expressions in Turkish texts is developed. The input of the developed model is word embeddings. In this study, the developed model success with using word embeddings built by different methods is measured on a dataset consisting of Turkish complaint texts collected from internet websites. By comparing the success of word embeddings on the detection of temporal expressions with the coverage percentages of word embeddings on the dataset, it is concluded that there is no correlation between them.
Semantic Segmentation with the Mixup Data Augmentation Method

The mixup data augmentation method is a method that creates new images via a linear function from multiple images. In this paper, it is examined whether the mixup data augmentation method improves the U-Net model&#x2019;s segmentation capability. In this study, artifact segmentation was performed with histopathological images. The dataset used was examined into three different groups: (1) images that are produced through traditional data augmentation methods like flipping and rotation; (2) images that are produced through only the mixup method; and (3) images that are produced through both the traditional and mixup methods. According to the findings, the use of the mixup method in combination with the traditional data augmentation methods improved the model&#x2019;s average Dice coefficient value for artifact segmentation of histopathological images.
Object Recognition with Sequential Decision Reinforcement of Deep Learning

The great success of deep learning methods for object detection rendered such methods the fundamental choice in related applications. Popular choices for multiple object detection in video sequences include convolutional neural networks, such as YOLO, MobileNet-SSD and Faster R-CNN, which typically split image frames to small rectangular regions and attempts to find bounding boxes of sought&#x2013;after objects. Current research of such methods mostly focus on speeding&#x2013;up the implementations or improving the network layers&#x2019; learning properties. As a new approach, this work appends a simple post processing stage at the end of such networks to reinforce decision robustness using a sequential decision process through sequential video frames. The sequential frames provide a better confidence on the existence of an object, when a probable object was also estimated in the previous frame. Once the confidence level overshoots a predetermined threshold, objects that are difficult to be detected in a single frame get accurately detected.
Bottleneck Sharing Generative Adversarial Networks for Unified Multi-Contrast MR Image Synthesis

Magnetic Resonance Imaging (MRI) is the favored modality in multi-modal medical imaging due to its safety and ability to acquire various different contrasts of the anatomy. Availability of multiple contrasts accumulates diagnostic information and, therefore, can improve radiological observations. In some scenarios, acquiring all contrasts might be challenging due to reluctant patients and increased costs associated with additional scans. That said, synthetically obtaining missing MRI pulse sequences from the acquired sequences might prove to be useful for further analyses. Recently introduced Generative Adversarial Network (GAN) models offer state-of-the-art performance in learning MRI synthesis. However, the proposed generative approaches learn a distinct model for each conditional contrast to contrast mapping. Learning a distinct synthesis model for each individual task increases the time and memory demands due to the increased number of parameters and training time. To mitigate this issue, we propose a novel unified synthesis model, bottleneck sharing GAN (bsGAN), to consolidate learning of synthesis tasks in multi-contrast MRI. bsGAN comprises distinct convolutional encoders and decoders for each contrast to increase synthesis performance. A central information bottleneck is employed to distill hidden representations. The bottleneck, based on residual convolutional layers, is shared across contrasts to avoid introducing many learnable parameters. Qualitative and quantitative comparisons on a multi-contrast brain MRI dataset show the effectiveness of the proposed method against existing unified synthesis methods.
A Quasi-Greedy Strategy based on Reinforcement Learning in Intelligent Control Technology

In solving intelligent control problems, reinforcement learning will have great power. We can make sequential decisions based on the value state function or the value action state function obtained by learning improvements. However, this assumption is often based on the finite nature of the action space. If each state corresponds to an action with extremely high computational complexity, implementing a greedy strategy becomes extremely difficult, and there is no guarantee that advanced learning can quickly complete the iteration of the strategy. Based on this, this paper proposes a quasi-greedy strategy based on an improved grey wolf algorithm. First of all, the original gray wolf algorithm has been modified, and a variable neighborhood search operator is added to it, which increases the global search capability of the gray wolf algorithm. It can be used at high computational complexity of an action set, fast optimization, significantly reduce the technical complexity of the transit process, speed up the convergence process of reinforcement learning.
Experimental Research and Analysis of Immersive Virtual Reality Technology on Children's Language Teaching

Immersive virtual reality (IVR) technology has proved its positive results in the field of education and is developing in the environment of education and training. The purpose of this study is to explore the specific role of IVR technology as a teaching assistant tool, the improvement of classroom effect, and the functional perfection and accuracy of the voice evaluation system in IVR. In this study, children were randomly divided into two groups: traditional teaching and IVR technology teaching. The results show that, in general, the language learning effect under IVR is better, and the result score of speech evaluation is higher.
The Golden Ratio of Instructive Content to Entertaining Content in Mobile Augmented Reality Games

Mobile augmented reality games are more effective for teaching and learning, and the impact of different proportions of learning content in mobile augmented reality games on preschoolers' learning outcomes is significant. For preschoolers there is a golden ratio that makes them learn better. This paper will discuss the impact of the proportion of game content on learning outcomes in mobile augmented reality games. The results show that the right proportion of instructional content in mobile augmented reality games enhances teaching effectiveness, satisfaction, entertainment and motivation. However, the proportion of learning content in play varies significantly across the age range. This study focuses on improving teaching effectiveness for teachers and designers by adjusting the proportion of instructional content in mobile augmented reality games when the content is similar.
Counterfeit Luxury Handbag Materials Image Classification using Deep learning and Local Binary Pattern

—Implementation of Deep Learning Techniques for image recognition to classify data in pixels results with high accuracy. At present, reusing of goods and reduction of goods disposal for saving the environment is a prominent trend, and affects growth of second-handed goods. However, counterfeit in second-handed goods is still a problem to economy. Although there are several methods to detect genuineness of the goods, such methods are required conduct by professionals. Moreover, luxury fashion goods, such as handbags, are difficult and inconvenient for counterfeit detection. This research thus studies and presents implementation of deep learning comprising of Convolutional Neural Networks (CNN) to classify images into pixels, together with texture feature extraction by Local Binary Pattern (LBP). The process is to study models by using pretrained VGG16 and DenseNet121 with stratified 5-Fold cross validation for evaluate model, by comparison between base models (without LBP) and base models with LBP. The models DenseNet121 with LBP resulted with 95% accuracy from classification of handbags for genuineness, counterfeit, and irrelevant materials, which is multiclassification. Moreover, implementation of LBP with the models resulted with more accuracy.
Programmable central bank digital currency for monetary circuits of production

Central bank digital currency (CBDC) is now a certainty. While Ecuador's CBDC was the first of the modern era, China's DC/EP deployment was the first of a major economy. Most analyses on CBDCs have focused on the payment system dynamics and the financial stability impacts on the commercial banking system. I extend circuit-theory based approaches with respect to the monetary theory of production and explore the characteristics that CBDCs should have with an activist central bank in mind, especially one low in the international hierarchy of money and one sympathetic to development concerns of its country.¬†The embedded characteristics together with the digital currency itself are contemporarily called "programmable money".¬†A successful CBDC that can guarantee real-time gross settlement, by definition, should rest on a unique ledger. While digital currencies with self-executable smart contracts may sound exotic, it helps to frame the discussion in terms of better-known quasi-monetary instruments: mobile-phone airtime, airline miles or food stamps. I present a viable accounting innovation for fungibility between bank money and central bank money: the contingent liability on the CBDC ledger.
Bifurcation Oscillator as an Advanced Sensor for Quantum State Control

We study bifurcation behavior of a high-quality (high-Q) Josephson oscillator coupled to a superconducting qubit. It is shown that the probability of capture into the state of dynamic equilibrium is sensitive to qubit states. On this basis we present a new measurement method for the superposition state of a qubit due to its influence on transition probabilities between oscillator levels located in the energy region near the classical separatrix. The quantum-mechanical behavior of a bifurcation oscillator is also studied, which makes it possible to understand the mechanism of "entanglement" of oscillator and qubit states during the measurement process. The optimal parameters of the driving current and the state of the oscillator are found for performing one-qubit gates with the required precision, when the influence on the qubit from measurement back-action is minimal. A measurement protocol for state populations of the qubit entangled with the oscillator is presented.
Cryptocurrencies: a revolutionary innovation or a scam?

A cryptocurrency is a digital or virtual currency that is secured by cryptography, which makes it nearly impossible to counterfeit or double-spend. The underlying idea has been met with enthusiasm because it promises to replace trust in long-standing institutions, such as commercial and central banks, with trust in a new, fully decentralised system founded on the blockchain and related distributed ledger technology. Interest in cryptocurrencies can be traced back the 2013 financial crisis in Cyprus, which led to the confiscation of domestic bank accounts. It is suggested that cryptocurrenices, including bitcoin, cannot perform the functions of money (hence they are not currencies), they have no intrinsic value whatsoever, and that they cannot be used as inflation hedge.