Reviewerly/exHarmony huggingface dataset:
User Authentication Model based on Mobile Phone IMEI Number: A Proposed Method Application for Online Banking System

The conventional banking structure is improved with the incorporation of elements of electronic banking, such as transfers over the counter using bank books, where all transfers can now be carried out through the network due to globalization and the development of information and communication technologies. Major risks to this infrastructure, however, often arise at the same time, such as from financial fraud. Therefore, it is important to create trust among banking users by offering security mechanisms. Security frameworks in electronic banking concentrate primarily on demonstrating a safe environment for online transactions, especially the authentication of the user. Several scholars have recommended several methods for the banking industry to authenticate online consumer access. Most of the experiments are based on standard username and password types, but with various password type mechanisms. Password in arbitrary value through a protected channel, for example, as well as biometrics such as retina recognition, speech recognition, or fingerprint recognition. In this paper, we introduced our own model for online user access authentication for the banking industry. The gaps of the current research are identified, and finally, the new authentication model is proposed based on the research gap. The significant difference of the proposed model is that this model provides security to user access without entering a username and password.
Constructing Age for Young Readers

Children's literature studies has been relatively slow in adopting techniques from digital humanities. This article explains a method for digitising, annotating, and analysing texts in xml to investigate the implicit age norms that children's books convey. The case studies are seventeen books by Bart Moeyaert and La Belle Sauvage by Philip Pullman. The analysis of speech distribution, topic modelling, syntactic parsing, and lexical analysis with digital tools adds information about implicit age norms that can support and inspire narrative analyses with close reading.
An evolvable hardware method based on elite Partheno-Genetic Algorithm

Aiming at the shortcomings of traditional genetic algorithm (GA) in the overall evolution of hardware, such as slow speed and small scale, a method of evolvable hardware (EHW) based on elite Partheno-Genetic Algorithm (PGA) is proposed. To improve the evolution efficiency of EHW, the number of chromosomes is reduced, the length of the chromosome is increased, the elite node chain is reserved, and the connection relationship of non-elite nodes is adjusted. The experimental results show that: compared with the existing literature, the evolution speed is increased 144 times on average, the overall scale of evolution is increased 74.7 times, and the evolution efficiency is significantly improved.
Construction and First Testing of the UX Kids Questionnaire (UXKQ)

In this paper, we focus on the construction and first testing of a User Experience questionnaire using semantic differentials for pupils, and the role of children as co-designers in the development of quantitative methods, which has received little or no attention. To examine the appropriateness of the UXKQ tool, two studies were conducted. In the first study, six children of grade seven participated in a workshop to construct and design a questionnaire for a specific learning app. The second study took place in a school context, with 230 pupils from grades six and seven performing user tests to validate the product-specific questionnaire in comparison with an already existing UX questionnaire developed by German UX experts for teenagers. The results suggest that children of grade seven can contribute to the design of a useful tool to examine children’s User Experience during user tests. Data were subjected to a factor analysis which resulted in the development of an additional shorter version of a User Experience Questionnaire.
General Dynamic Neural Networks for the Adaptive Tuning of an Omni-Directional Drive System for Reactive Swarm Robotics

We demonstrate the use of general dynamic neural networks (GDNNs) for the online tuning of an omni-directional drive system for reactive swarm robots. The drive system used in this work consists of four motor-encoder-microcontroller modules each constituting a single-input single-output (SISO) proportional, integral, and differential (PID) control system. For a given target velocity, a neural network generates the parameters for each PID control system. In this paper, we evaluate and compare two different network structures for generating the PID parameters for the control systems using a hardware platform that we also presented in this paper. We analyze the performance of the system with respect to ISO performance indicators, our results show that both network structures are able to learn and tune the parameters for each PID control system to increase the accuracy of the drive system in comparison to fixed untuned PID parameters that are close to the output of a randomly initialized network.
Feature Extraction of Face Image Based on Convolutional Neural Network

In order to overcome the problem of inaccuracy in feature extraction when the corner features are not obvious in traditional corner detection methods, a face feature extraction method based on deep learning convolutional neural network is proposed in this paper. The key point of this method is the construction algorithm of convolution network with single image as input data. In this paper, the convolutional neural network implemented by Python code includes three convolution layers, three lower sampling layers and one full connection layer. The filter structure is designed and the characteristic image is obtained. The convolution layer uses the Reul function as the activation function, while the lower sampling layer uses the maximum pooling and ruul activation functions. The experimental results show that the method based on the different levels of image features extracted by deep learning network as the basis for image matching, and can still achieve good recognition effect when the traditional features such as corners are not clear.
A 10-Year Review of the Methods and Purposes of On-Skin Interface Research in ACM SIGCHI

This paper aims to organize research from Association for Computing Machinery Special Interest Group on Computer–Human Interaction (ACM SIGCHI) associated conferences that have significant impact on on-skin interfaces. We performed a systematic review of the ACM database and identified 68 on-skin related publications between January 2010 to May 2021. The publications are categorized, evaluated, and compared over time and by conference. Citation impact of each paper is assessed revealing trends in the field and reflected onto the general community. Through extensive data collection, we present a thorough analysis of on-skin interfaces' development in the past decade.
DSMN: A Personalized Information Retrieval Algorithm Based on Improved DSSM

Due to rarely considering document popularity and personalization issues at the same time in the extraction of semantic features based semantic matching algorithms, the accuracy is low in the field of information retrieval. To solve the problem, an improved personalized information retrieval algorithm DSMN is proposed. DSMN bases on the deep struct semantic model (DSSM), uses independent recurrent neural network (IndRNN) to extract semantic features, and process long sequences. In DSMN, the self-attention mechanism is used to further extract the features, and the semantic similarity is calculated. By combining the semantic similarity with the processed user characteristics and document popularity, the relevance score of the query and the document is calculated to improve the efficiency of information retrieval. Experiments are done on four datasets, and the results show that the performance of DSMN is significantly better than the other state-of-the-art information retrieval algorithms based on semantic matching.
A Scenario Adaptive Model for Predicting Error Rates in Moving Target Selection on Smartphones

Modeling error rates in moving target selection is critical in guiding the design and improving user performance in the user interface with dynamic contents. Despite the high accuracy of existing models in predicting error rates for various inputs, they need to fit a large number of samples for a specific interaction scenario. This paper presents a scenario adaptive model that can quickly learn the characterizes of endpoints from small data in a specific scenario, and then accurately predict the error rates in this scenario. We report on two studies evaluating the adaptability of the model in specific users (children and the older user) and pointing postures (walking and one-handed) with a smartphone. Significant improvements in error rates prediction of our model were observed compared to the previous non-adaptive one. We conclude with the findings from our results for insights into future interface design.
Pruning the Unimportant or Redundant Filters? Synergy Makes Better

Filter pruning is a hot topic in convolutional neural network compression due to its friendliness to hardware implementation. Most pruning methods prune filters according to their importance, i.e., removing the filters that have little effect on the final performance of the network. While from another perspective, some recent works propose to prune upon the redundancy of filters. Filters pruned in this way usually have non-negligible effects on the final performance, whereas those effects could be compensated by the remaining filters. Since importance and redundancy pruning respectively captures the local and global information of a convolutional layer, which are mutually complementary to some extent, we propose a new pruning criterion that synergies both of those previous pruning criteria to make full use of the filter information. Comprehensive experiments on benchmark image classification datasets show the effectiveness of our proposed pruning criterion.
Learning Augmentation for GNNs With Consistency Regularization

Graph neural networks (GNNs) have demonstrated superior performance in various tasks on graphs. However, existing GNNs often suffer from weak-generalization due to sparsely labeled datasets. Here we propose a novel framework that learns to augment the input features using topological information and automatically controls the strength of augmentation. Our framework learns the augmentor to minimize GNNs’ loss on unseen labeled data while maximizing the consistency of GNNs’ predictions on unlabeled data. This can be formulated as a meta-learning problem and our framework alternately optimizes the augmentor and GNNs for a target task. Our extensive experiments demonstrate that the proposed framework is applicable to any GNNs and significantly improves the performance of graph neural networks on node classification. In particular, our method provides 5.78% improvement with Graph convolutional network (GCN) on average across five benchmark datasets.
Transition flight simulation of a hybrid VTOL fixed-wing drone

Aerospace Engineering Department, Institut Teknologi Bandung, recently develops a medical transport drone for distributing medical supplies to remote areas. A hybrid vertical take-off landing (VTOL) fixed-wing drone type is used as the basic design. In the early development process, a commercial of the self (COTS) drone product is used as the platform. This paper discusses the simulation of transition flight maneuver of the drone, specifically in the longitudinal mode. Two different transition scenarios are designed, simulated, and analyzed to obtain the energy requirement for each scenario. The energy requirement is defined as the propulsive energy spent during the transition. Furthermore, the dynamic characteristics of the drone during the transition phase is carefully analyzed to determine whether the maneuver can be performed manually or must be assisted by an automatic flight controller. It is obtained that the climb and fall scenario requires less propulsive energy than the constant altitude scenario. However, the transition requires a proper automatic flight controller since manual input given in the simulation can not make the drone fly in the desired condition.
Mineralogical Characteristics and Short‐wave Infrared Spectra of Chlorite as Indicators of Hydrothermal Centers: A Case Study of the Giant Porphyry Copper‐Molybdenum Deposit at Qulong, Tibet

Abstract The Qulong deposit in Tibet is one of the largest porphyry copper‐molybdenum deposits in China. We used short‐wave infrared (SWIR) spectroscopy to examine the spectral characteristics of the extensively developed chlorite in this deposit. X‐ray diffraction and electron microprobe analyses were used for phase identification and to obtain the chemical composition, ion substitution relationships, and formation environment of the chlorite. SWIR spectral parameters were applied to detect the hydrothermal centers. The results indicate that the wavelength of the absorption feature for Qulong chlorite Fe‐OH (Pos2250) range from 2240 to 2268.4 nm; the chlorite substitution relationships are dominated by Mg‐Fe substitution at the octahedral sites together with Al IV ‐Si substitution at the tetrahedral sites; the chlorite formation temperatures range within the medium‐low temperature hydrothermal alteration range from 164 to 281°C, with an average value of 264°C; the wavelength of the chlorite peak position for Fe‐OH (2250 nm) absorption and its chemical composition are positively correlated with Al VI , Fe + Al VI , Fe/(Fe + Mg), Fe, and Fe + Al IV but negatively correlated with Mg and Mg/(Fe + Mg); and the wavelength associated with the chlorite Fe‐OH (2250 nm) absorption feature is positively correlated with the temperature at which the chlorite formed. These correlations indicate that more Fe and Al VI ions and fewer Mg ions at the octahedral sites of chlorite lead to a longer the wavelength of the chlorite Fe‐OH (2250 nm) absorption feature and a higher chlorite formation temperature. The wavelength of the Qulong chlorite Fe‐OH (2250 nm) absorption feature (&gt;2252 nm) can thus serve as an exploration indicator to guide the detection of hydrothermal centers in porphyry copper deposits. The results of the study indicate that the mineralogical and SWIR spectral characteristics of chlorite are significant indicators for locating hydrothermal centers within porphyry deposits.
In-Network Computation for Large-Scale Federated Learning Over Wireless Edge Networks

Most conventional Federated Learning (FL) models are using a star network topology where all users aggregate their local models at a single server (e.g., a cloud server). That causes significant overhead in terms of both communications and computing at the server, delaying the training process, especially for large scale FL systems with straggling nodes. This article proposes a novel edge network architecture that enables decentralizing the model aggregation process at the server, thereby significantly reducing the training delay for the whole FL network. Specifically, we design a highly-effective in-network computation framework (INC) consisting of a user scheduling mechanism, an in-network aggregation process (INA) which is designed for both primal- and primal-dual methods in distributed machine learning problems, and a network routing algorithm with theoretical performance bounds. The in-network aggregation process, which is implemented at edge nodes and cloud node, can adapt two typical methods to allow edge networks to effectively solve the distributed machine learning problems. Under the proposed INA, we then formulate a joint routing and resource optimization problem, aiming to minimize the aggregation latency. The problem turns out to be NP-hard, and thus we propose a polynomial time routing algorithm which can achieve near optimal performance with a theoretical bound. Simulation results showed that the proposed algorithm can achieve more than 99 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> of the optimal solution and reduce the FL training latency, up to 5.6 times w.r.t other baselines. The proposed INC framework can not only help reduce the FL training latency but also significantly decrease cloud's traffic and computing overhead. By embedding the computing/aggregation tasks at the edge nodes and leveraging the multi-layer edge-network architecture, the INC framework can liberate FL from the star topology to enable large-scale FL.
Jointing Multi-task Learning and Gradient Reversal Layer for Far-Field Speaker Verification

Far-field speaker verification is challenging, because of interferences caused by different distances between the speaker and the recorder. In this paper, a distance discriminator, which determines whether two utterances are recorded at the same distance, is used as an auxiliary task to learn distance discrepancy information. There are two identical auxiliary tasks, one is added before the speaker embedding layer to learn distance discrepancy information via multi-task learning, and then the other is added after that layer to suppress the learned discrepancy via a gradient reversal layer. In addition, to avoid conflicts among the optimization directions of all tasks, the loss weight of every task is updated dynamically during training. Experiments on AISHELL Wake-up show a relatively 7% and 10.3% reduction of equal error rate (EER) on far-far speaker verification and near-far speaker verification respectively, compared with the single-task model, demonstrating the effectiveness of the proposed method.
Studies of Keyboard Patterns in Passwords: Recognition, Characteristics and Strength Evolution

Keyboard patterns are widely used in password construction, as they can be easily memorized with the aid of positions on the keyboard. Consequently, keyboard-pattern-based passwords has being the target in many dictionary attack models. However, most of the existing researches relies only on recognition methods defining keyboard pattern structures empirically or even manually. As a result, only those infamous keyboard patterns such as qwerty are recognized and many potential structures are not specified. Besides, there are limited studies focusing on the characteristics of keyboard patterns.
Enhancing Fine-Grained Classification for Low Resolution Images

Low resolution fine-grained classification has widespread applicability for applications where data is captured at a distance such as surveillance and mobile photography. While fine-grained classification with high resolution images has received significant attention, limited attention has been given to low resolution images. These images suffer from the inherent challenge of limited information content and the absence of fine details useful for sub-category classification. This results in low inter-class variations across samples of visually similar classes. In order to address these challenges, this research proposes a novel attribute-assisted loss, which utilizes ancillary information to learn discriminative features for classification. The proposed loss function enables a model to learn class-specific discriminative features, while incorporating attribute-level separability. Evaluation is performed on multiple datasets with different models, for four resolutions varying from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$32\times 32$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$224\times 224$</tex> . Different experiments demonstrate the efficacy of the proposed attribute-assisted loss for low resolution fine-grained classification.
Online Anomalous Taxi Trajectory Detection Based on Multidimensional Criteria

Online anomalous taxi trajectory detection, which identifies anomalies from ongoing taxi trajectories, has become an important and fundamental concern in many real-world applications. Most of the existing studies define the anomalous trajectories as the ones deviating from the majority of routes or showing abnormal driving time and distance at the same time. However, due to the complexity of road conditions and the variety of passenger preferences, those methods have large false-positive rates, i.e., reporting many normal routes as anomalies. A high false-positive rate is harmful since false alarms will 1) bring unnecessary panic to passengers, 2) cause fiscally punishments to normal drivers, and 3) wastes human resource to deal with drivers' complaints. To this end, this paper proposes an online anomalous trajectory detection method, namely multidimensional criteria based anomalous trajectory (MCAT), to identify anomalous trajectories online. It judges anomalies by considering multidimensional criteria (similarity, time, distance) at the same time, reducing the false positives without sacrificing false negative rates. We evaluate the proposed method based on the real-world taxi data collected from Shanghai, China. The experimental results demonstrate that our method can outperform state-of-the-art methods in terms of accuracy, false-negative rate, and false-positive rate.
Storing Blockchain Data in Public Storage

The idea of blockchain has emerged in many applications, including IoT data processing, over the last few years. Because of the guaranteed security, the blockchain is believed to create the backbone of future technologies as a P2P network. However, applying the blockchain protocol in IoT data computing faces a severe challenge related to extensive data management. This paper presents a BlockStore, the method for storing blockchain data in single public storage while maintaining the security features. The BlockStore also resolves the access management and recovery issues among the blockchain nodes. The preliminary experiments prove that the expected research brings a considerable advantage to IoT data management using blockchain. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
CLACER: A Deep Learning-based Compilation Error Classification Method for Novice Students’ Programs

Compilation errors happen during the debugging process of novice students. Compiler error messages help novices to localize and remove errors, but these messages are difficult to understand for students. Some computing education researchers analyzed the compiler error messages generated by novice’s attempts to compile their programs. However, some important questions remain open. For example, the existing compilation error category cannot cover all programs due to the simple static analysis and program repair patterns. And existing prediction models for classifying compilation errors are unsatisfactory because of the inappropriate neural networks. In this paper, we first propose a new category of compilation error based on the program tokens, which is the smallest unit of the program. Then we develop a neural network model CLACER (ClAssification of Compilation ERrors) based on TextCNN. CLACER performs better on extracting semantic features and statistical features from compiler error messages. To verify the effectiveness of our proposed category and corresponding method CLACER, we choose 16,926 student programs as our experimental subjects. Final experimental results indicate that our proposed classification category covers 16.5% more programs than the state-of-the-art category TEGCER. Moreover, CLACER improves the compiler’s localization effectiveness and with a 4.25% improvement on the TEGCER category. Further analysis shows that CLACER has a promising prediction performance for different error classes, and TextCNN is more suitable for constructing the compilation error classification model.