Reviewerly/exHarmony huggingface dataset:
Pruning Strategy on Adaptive Rule Model by Sorting Utility Items

The adaptive Rule Model is an association rule development that formulates a minimum threshold value according to the data characteristics.The formulation process is based on item frequency and utility for other considerations, which requires high runtime and memory usage.For better performance, it is necessary to develop a pruning strategy that can minimize runtime and memory usage without reducing the quality of the rule produced.Therefore, this research aims to develop 2 pruning strategies, namely the Adaptive Rule with Average Utility (ADR-AU) and the Adaptive Rule with Sorting Utility (ADR-SU) to improve the adaptive rule model.The ADR-AU changes the minimum threshold using the average utility item.The ADR-SU model proposes an additional step in the frequent itemset determination process.It is used for sorting utility items in descending order before the iteration process is carried out to compare whether the values are greater than the minimum threshold.Based on the experiments on 8 datasets, the number of frequent itemset in the ADR-AU and ADR-SU models was significantly less in the adaptive rule model.The results showed that all datasets experienced a decrease in runtime between the adaptive rule model and ADR-AU.Meanwhile, for the Zoo and Sco datasets, there was an increase in runtime between the ADR-AU and ADR-SU models.In terms of memory usage, the connect and retail datasets experienced a decrease, while the other 4 were increased at the ADR-AU and ADR-SU stages.
Backpack: A Backpropagable Adversarial Embedding Scheme

A min max protocol offers a general method to automatically optimize steganographic algorithm against a wide class of steganalytic detectors. The quality of the resulting steganograhic algorithm depends on the ability to find an “adversarial” stego image undetectable by a set of detectors while communicating a given message. Despite min max protocol instantiated with ADV-EMB scheme leading to unexpectedly good results, we show it suffers a significant flaw and we present a theoretically sound solution called Backpack. Extensive experimental verification of min max protocol with Backpack shows superior performance to ADV-EMB, the generality of the tool by targeting a new JPEG QF100 compatibility attack and further improves the security of steganographic algorithms.
Outlier Explanation Through Masking Models

Given a database and one single anomalous data point, the Outlying Aspect Mining problem consists in explaining the abnormality of that data point w.r.t. the data population stored in the input database. Thus, the problem requires the discovery of the sets of attributes and associated values that account for the abnormality of a data point within a given data set. In this setting, the abnormality of the data point at hand is stated beforehand, e.g., as the result of some outlier detection techniques (which, for the most part, do not provide information about why the selected data points are actually anomalous). This paper proposes a solution to the OAM problem exploiting a deep learning architecture. Besides explaining the input data point abnormality by singling out the smallest set of pairs attribute-value justifying it, our technique also provides new values for those attributes that would transform the input outlier into an inlier. Several experiments are also presented that assess the effectiveness of our approach.
Prisoners’ Dilemma in a Spatially Separated System Based on Spin–Photon Interactions

Having access to ideal quantum mechanical resources, the prisoners’ dilemma can be ceased. Here, we propose a distributed quantum circuit to allow spatially separated prisoners to play the prisoners’ dilemma game. Decomposing the circuit into controlled-Z and single-qubit gates only, we design a corresponding spin–photon-interaction-based physical setup within the reach of current technology. In our setup, spins are considered to be the players’ logical qubits, which can be realized via nitrogen-vacancy centers in diamond or quantum dots coupled to optical cavities, and the game is played via a flying photon realizing logic operations by interacting with the spatially separated optical cavities to which the spin qubits are coupled. We also analyze the effect of the imperfect realization of two-qubit gates on the game, and discuss the revival of the dilemma and the emergence of new Nash equilibria.
Basketball Object Extraction Method Based on Image Segmentation Algorithm

Finding your favorite videos from massive sports video data has become a big demand for users, accurate sports videos can better help people learn sports content, and the traditional data management and retrieval methods using text identifiers are difficult to meet the needs of users, so the research on the extraction of sports objects in sports videos is of great significance. This paper mainly studies and proposes the basketball object extraction method based on image segmentation algorithm and can accurately analyze the trajectory of the basketball target. By modeling the video frame of basketball game, the basketball object is selected for segmentation and extraction. The extracted basketball object can be used for tracking the target in the basketball video clip retrieval system. At the same time, the segmentation and extraction of the basketball object are also the core part in the basketball video clip retrieval framework. Combined with the characteristics of basketball video images in the database, the algorithm extracts the image block variance and contrast to form the training feature vector, and the correct segmentation rate on the database is higher than 95.2%. The results show that this method has a good effect on the segmentation and extraction of basketball objects in basketball videos.
Acquisition of Chinese Verb Separation by Adult L2 Learners

Chinese verb separation is a grammatical construction in which a bimorphemic verb compound separates and expands into a verb phrase. For example, to say “sang one song”, one must insert the aspect marker le and classifier phrase yī shǒu between the verb chàng gē ‘sing-song’ as in chàng le yī shǒu gē (sing-LE-one-classifier-song). Adult second language (L2) learners face at least three problems related to acquiring verb separation: separation patterns are complex and numerous, classroom oral input is limited, and L1 transfer often fails. To better understand L2 learners’ acquisition of verb separation, this study tested 28 adult L2-Chinese classroom learners. Three tasks were administered online: verb decomposition, grammaticality judgment, and oral translation. On average, only 37% of verbs were accurately decomposed, 63% of verbs were accurately judged to be grammatical, and 19% of verbs were orally produced with the correct separation pattern. Chinese verb separation, particularly decomposition and oral production, is thus extremely challenging for L2 learners to acquire—even for advanced learners with a relatively large vocabulary size. The results are discussed in terms of the Unified Competition Model and L2 Chinese pedagogy.
ViCT—Virtual Campus Tour Environment with Spherical Panorama: A Preliminary Exploration

ViCT or spelled "VISIT", is the name of a web-based application that is being developed to display various points and locations from the campus environment that can be accessed virtually. ViCT aims to help users, students, and the public explore campus with digital freedom of movement anytime and anywhere, educating users about campus facilities and services with an immersive technology experience that is eco-friendly and fun. The application development concept is unlimited visits and easily accessible via desktop and mobile, saving resources and costs. In this chapter, the focus of the researchers discusses the initial digital mapping of the campus environment, which is the object or study area of the development of virtual campus applications with 360-degree panoramic image techniques. Spherical panorama images are the primary basis of the virtual reality environment in this application. Each panoramic image is processed and merged into a unified whole on a web page. Spherical panorama photos captured using ultra-wide-angle lenses at the front and rear of the camera (dual fisheye) generate a visual field with a horizontal and vertical angle of view close to 360 degrees.
DIGITAL GAME-BASED LEARNING MODEL AS AN EDUCATIONAL APPROACH

Today, the rapid development of technology has also affected the existing learning processes. In addition, this development has made it necessary for children to develop skills by the needs of the 21st century. In this research, an alternative to the question of how we can support and update learning activities suitable for our age is presented. Digital game-based learning concepts are explained in the light of the literature and suggestions on how to use them from early childhood are presented. Game is the most important supportive activity for development areas in early childhood. It also similarly affects learning processes. Learning with play makes the child more active and makes him excited about learning. This increases the motivation to learn. According to the research, when DGBL is used effectively, it has been seen that children support many 21st century skills such as critical thinking, problem-solving, and cooperative learning. This review demonstrated the importance of combining learning and game of digital technology used as an education model. In addition, Practical recommendations are presented for using technology in preschool children.
Scheduling Scheme Design of Hotel Service Robot: A Heuristic Algorithm to Provide Personalized Scheme

With the rapid development of machine learning and artificial intelligence, hotel service robots are widely used, but there are many problems to be solved in the scheduling scheme of hotel service robots. In this study, the Pareto optimal definition is used to model the problem, and a nondominated sorting heuristic method including genetic algorithm and differential evolution algorithm is designed to solve this problem. Experimental results show the effectiveness and stability of our algorithm. In addition, compared with the previous methods, the method proposed in this paper can provide a more personalized and reasonable service robot scheduling scheme for hotels. Finally, the hotel can optimize its management and operation and further deepen the degree of hotel intelligence.
One-Shot Federated Learning-based Model-Free Reinforcement Learning

The Federated Learning (FL) paradigm is emerging as a way to train machine learning (ML) models in distributed systems. A large population of interconnected devices (i.e. Internet of Things (IoT)) acting as local learners optimize the model parameters collectively (e.g., neural networks’ weights), rather than sharing and disclosing the training data set with the server. FL approaches assume each participant has enough training data for the tasks of interest. Realistically, data collected by IoT devices may be insufficient and often unlabeled. In particular, each IoT device may only contain one or a few samples of every relevant data category, and may not have the time or interest to label them. In realistic applications, this severely limits FL’s practicality and usability. In this paper, we propose a One-Shot Federated Learning (OSFL) framework considering a FL scenario wherein the local training is carried out on IoT devices and the global aggregation is done at the level of an edge server. Moreover, we combine model-free reinforcement learning with OSFL to design a more intelligent IoT device to infer whether to label a sample automatically or request the true label for the one-shot learning set-up. We validate our system on the SODA10M dataset. Experiments show that our solution achieves better performance than DQN and RS benchmark approaches.
Integrating with Segmentation by Using Multi-Task Learning Improves Classification Performance in Medical Image Analysis

Diagnosis of tumors is an important direction of computer-aided diagnosis (CAD). The shape, size, and boundary of the tumor are widely-used diagnostic evidence, and the corresponding segmentation annotated by the radiologists is a vital expert knowledge, which can be used as supervision to guide feature extraction. Therefore, this study firstly introduces a multi-task learning (MTL) network integrating segmentation task for predicting grading of pancreatic neuroendocrine neoplasms (pNENs) and the microvascular invasion (MVI) of hepatocellular carcinoma (HCC). The proposed network combines a powerful split-attention-based encoder and a U-net decoder, and achieves the best performance in comparisons of other popular networks and previous studies. In addition, feature map visualization suggests that the reason for the improved classification performance may be that MTL makes the encoder pay more attention to lesions and extract more semantic information.
Unsupervised Similarity Based Convolutions for Handwritten Digit Classification

Effective training of filters in Convolutional Neural Networks (CNN) ensures their success. In order to achieve good classification results in CNNs, filters must be carefully initialized, trained and fine-tuned. We propose an unsupervised method that allows the discovery of filters from the given dataset in a single epoch without specifying the number of filters hyper-parameter in convolutional layers. Our proposed method gradually builds the convolutional layers by a discovery routine that extracts a number of features that adequately represent the complexity of the input domain. The discovered filters represent the patterns in the domain, so they do not require any initialization method or backpropagation training for fine tuning purposes. Our method achieves 99.03% accuracy on MNIST dataset without applying any data augmentation techniques.
Location-aware convolutional neural networks for graph classification

Graph patterns play a critical role in various graph classification tasks, e.g., chemical patterns often determine the properties of molecular graphs. Researchers devote themselves to adapting Convolutional Neural Networks (CNNs) to graph classification due to their powerful capability in pattern learning. The varying numbers of neighbor nodes and the lack of canonical order of nodes on graphs pose challenges in constructing receptive fields for CNNs. Existing methods generally follow a heuristic ranking-based framework, which constructs receptive fields by selecting a fixed number of nodes and dropping the others according to predetermined rules. However, such methods may lose important structure information through dropping nodes, and they also cannot learn task-oriented graph patterns. In this paper, we propose a Location learning-based Convolutional Neural Networks (LCNN) for graph classification. LCNN constructs receptive fields by learning the location of each node according to its embedding that contains structures and features information, then standard CNNs are applied to capture graph patterns. Such a location learning mechanism not only retains the information of all nodes, but also provides the ability for task-oriented pattern learning. Experimental results show the effectiveness of the proposed LCNN, and visualization results further illustrate the valid pattern learning ability of our method for graph classification.
Hetero Layer Fusion Based Architecture Design and Implementation for of Deep Learning Accelerator

In this paper, we propose a neural network accelerator suitable for Yolo Like network. The accelerator is adopted at 72PE and works at a clock rate of 100Mhz. The IFMap (input feature map), Kernel, and OFMap (output feature map) are all quantized to uint8 to reduce the use of hardware resources and maintain a certain degree of accuracy. In addition, to reduce the DRAM bandwidth usage, we provide the Hetero Layer Fusion architecture to merge neighbor Layers' operations, which can avoid unnecessary access to the DRAM. To verify the proposed hardware architecture, we implement it on the Xilinx Zynq UltraScale + MPSoC ZCU102 platform. Experiments results show that the proposed architecture can achieve a reduction of bandwidth by nearly 33%.
Measuring the impact of anonymization on real-world consolidated health datasets engineered for secondary research use: Experiments in the context of MODELHealth project

Introduction Electronic Health Records (EHRs) are essential data structures, enabling the sharing of valuable medical care information for a diverse patient population and being reused as input to predictive models for clinical research. However, issues such as the heterogeneity of EHR data and the potential compromisation of patient privacy inhibit the secondary use of EHR data in clinical research. Objectives This study aims to present the main elements of the MODELHealth project implementation and the evaluation method that was followed to assess the efficiency of its mechanism. Methods The MODELHealth project was implemented as an Extract-Transform-Load system that collects data from the hospital databases, performs harmonization to the HL7 FHIR standard and anonymization using the k-anonymity method, before loading the transformed data to a central repository. The integrity of the anonymization process was validated by developing a database query tool. The information loss occurring due to the anonymization was estimated with the metrics of generalized information loss, discernibility and average equivalence class size for various values of k. Results The average values of generalized information loss, discernibility and average equivalence class size obtained across all tested datasets and k values were 0.008473 ± 0.006216252886, 115,145,464.3 ± 79,724,196.11 and 12.1346 ± 6.76096647, correspondingly. The values of those metrics appear correlated with factors such as the k value and the dataset characteristics, as expected. Conclusion The experimental results of the study demonstrate that it is feasible to perform effective harmonization and anonymization on EHR data while preserving essential patient information.
Coordination of Global Approach for Blockchain Supply Chains

Decreased exchange costs have prodded contemporary globalization, from the acquaintance of the steel trailer with composed exchange advancement. In this review, we take a gander at how blockchain might be utilized to bring down the exchange expenses of making and organizing solid data along supply chains. Customers, makers, and legislatures are progressively requesting superb data. Exchanged things' characteristics and provenance This data is costly to make and keep up with among dissipated parties, to a limited extent in view of the dangers of mistake and misrepresentation. Late endeavors, like paperless exchange plans, have endeavored to bypass these costs by means of the utilization of new innovation. Our emphasis is on how blockchain innovation might be utilized to make another decentralized monetary framework for supply chains by controlling decentralized unique records containing data about items as they move.Before diving into strategy, we examine the conceivable financial consequences of blockchain supply chains. Successful reception faces various approach obstacles, including administrative acknowledgment and cross-jurisdictional interoperability. We propose an AsiaPacific significant level strategy discussion to organize concerns like open guidelines and administrative similarity.
Explaining Machine Learning Predictions of Decision Support Systems in Healthcare

Abstract Artificial Intelligence (AI) methods, which are often based on Machine Learning (ML) algorithms, are also applied in the healthcare domain to provide predictions to physicians and patients based on electronic health records (EHRs), such as history of laboratory values, applied procedures and diagnoses. The question about these predictions “Why Should I Trust You?” encapsulates the issue with ML black boxes. Therefore, explaining the reasons for these ML predictions to physicians and patients is crucial to allow them to decide whether the prediction is applicable or not. In this paper, we explained and evaluated two prediction explanation methods for healthcare professionals (physicians and nurses). We compared two model-agnostic explanation methods based on global feature importance and local feature importance. We evaluated the user trust and reliance (UTR) for the explanation results of each method in a user study based on real patients’ electronic health records (EHR) and the feedback of healthcare professionals. Based on the user study, we observed that both methods have strengths and weaknesses according to the patients’ data, especially based on the data size of the patient. When the amount of data is small, global feature importance is enough to use. However, when the patient’s data size is big, using a local feature importance method makes more sense. As future work, we will develop a hybrid explanation method (by combining these methods automatically with a smart setting) to obtain higher and more stable performance results in terms of user trust and reliance.
GE vs GM: Efficient side-channel security evaluations on full cryptographic keys

Security evaluations for full cryptographic keys is a very important research topic since the past decade. An efficient rank estimation algorithm was proposed at FSE 2015 to approximate the empirical guessing entropy remaining after a side-channel attack on a full AES key, by combining information from attacks on each byte of he key independently. However, these could not easily scale to very large keys over 1024 bits. Hence, at CHES 2017, it was proposed a new approach for scalable security evaluations based on Massey’s guessing entropy, which was shown tight and scalable to very large keys, even beyond 8192 bits. Then, at CHES 2020, it was proposed a new method for estimating the empirical guessing entropy for the case of full-key evaluations, showing also important divergences between the empirical guessing entropy and Massey’s guessing entropy. However, there has been some confusion in recent publications of side-channel evaluation methods relying on these two variants of the guessing entropy. Furthermore, it remained an open problem to decide which of these methods should be used and in which context, particularly given the wide acceptance of the empirical guessing entropy in the side-channel community and the relatively little use of the other.In this paper, we tackle this open problem through several contributions. First of all, we provide an unitary presentation of both versions of the guessing entropy, allowing an easy comparison of the two metrics. Secondly, we compare the two metrics using a set of common and relevant indicators, as well as three different datasets for side-channel evaluations (simulated, AVR XMEGA 8-bit microcontroller and a 32-bit device). We used these indicators and datasets also to compare the three full-key evaluation methods from FSE 2015, CHES 2017 and CHES 2020, allowing us to provide a clear overview of the usefulness and limitations of each method. Furthermore, our analysis has enabled us to find a new method for verifying the soundness of a leakage model, by comparing both versions of the guessing entropy. This method can be easily extended to full-key evaluations, hence leading to a new useful method for side-channel evaluations.