Reviewerly/exHarmony huggingface dataset:
A dynamic bidirectional heuristic trust path search algorithm

Online social networks greatly promote peoples' online interaction, where trust plays a crucial role. Trust prediction with trust path search is widely used to help users find the trusted friends and obtain valid information. However, the shortcomings of accuracy and time still exist in some famous algorithms. Therefore, the dynamic bidirectional heuristic search (DBHS) algorithm is proposed in this paper to find the reliable trust path by studying the heuristic search. First, the trust value and path length are comprehensively considered to find the most trusted user. Specially, it constrains the traversal depth based on the ‘small world’ theory and obtains the acceptable path set by using the relaxation coefficient λ to relax the depth of the shortest path. By this way, some longer path with the higher trust can be considered to improve the precision of algorithm. Then, an adjustment factor is designed based on the meet in the middle (MM) algorithm to assign search weights to two directions based on the size of the search tree expanded, so as to improve the problem of no priori when fixed parameters are used. Besides, the complexity of unidirectional trust path search can also be reduced by searching from two directions, which can reduce the depth and improve the efficiency of search. Finally, the predictive trust degree is outputted by the trust propagation function. Two public datasets are used to generate experimental results, which show that DBHS can quickly search and form reliable trust relationship, and it partly improves other algorithms.
Data-driven learning with younger learners: exploring corpus-assisted development of the passive voice for science writing with female secondary school students

Corpus-based approaches to language and literacy education, commonly known as data-driven learning (DDL), are increasing in prominence. However, the vast majority of DDL interventions involve adult tertiary level learners, leaving a dire need for comprehensive DDL studies for secondary education. The present study reports on a half-year DDL experiment conducted at an all-girls secondary school in Australia, focusing on the development of receptive and productive knowledge of passive voice constructions used when writing scientific research reports for a physical science class. Pre/post-tests were conducted testing learners' receptive knowledge and productive use of the passive, alongside data on learners' autonomous use of corpora within a written research report. Learners' perceptions of corpora and DDL were also collected through questionnaire survey and interview data taken both immediately post-training and three months after training. The results suggest learners' corpus consultation was effective in improving use of the passive voice for science writing with pre-tertiary learners, although clear preferences for (and criticisms of) certain corpus tools, functions and usage was apparent, and continued uptake post-training was relatively weak. Generally however, the implications of these findings paint a positive picture of what is possible regarding DDL with younger learners, and provide a model of how a DDL intervention with younger learners can be successfully managed and integrated in a context where secondary content teachers, rather than solely the applied linguist, can be the main stakeholders in a DDL intervention.
Implicit Regularization Properties of Variance Reduced Stochastic Mirror Descent

In machine learning and statistical data analysis, we often run into objective function that is a summation: the number of terms in the summation possibly is equal to the sample size, which can be enormous. In such a setting, the stochastic mirror descent (SMD) algorithm is a numerically efficient method -- each iteration involving a very small subset of the data. The variance reduction version of SMD (VRSMD) can further improve SMD by inducing faster convergence. On the other hand, algorithms such as gradient descent and stochastic gradient descent have the implicit regularization property that leads to better performance in terms of the generalization errors. Little is known on whether such a property holds for VRSMD. We prove here that the discrete VRSMD estimator sequence converges to the minimum mirror interpolant in the linear regression. This establishes the implicit regularization property for VRSMD. As an application of the above result, we derive a model estimation accuracy result in the setting when the true model is sparse. We use numerical examples to illustrate the empirical power of VRSMD.
Promoting Ethical Awareness in Communication Analysis: Investigating Potentials and Limits of Visual Analytics for Intelligence Applications

Digital systems for analyzing human communication data have become prevalent in recent years. Intelligence analysis of communications data in investigative journalism, criminal intelligence, and law present particularly interesting cases, as they must take into account the often highly sensitive properties of the underlying operations and data. At the same time, these are areas where increasingly automated, sophisticated approaches systems can be particularly relevant, especially in terms of Big Data manageability. However, by the shifting of responsibilities, this also poses dangers. In addition to privacy concerns, these dangers relate to uncertain or poor data quality, leading to discrimination and potentially misleading insights. Visual analytics combines machine learning methods with interactive visual interfaces to enable human sense- and decision-making. This technique can be key for designing and operating meaningful interactive communication analysis systems that consider these ethical challenges. In this interdisciplinary work, a joint endeavor of computer scientists, ethicists, and scholars in Science & Technology Studies, we investigate and evaluate opportunities and risks involved in using Visual analytics approaches for communication analysis in intelligence applications in particular. We introduce, at first, the common technological systems used in communication analysis, further discussing the domain-specific ethical implications, tensions, and risks involved. We then make the case of how tailored Visual Analytics approaches may reduce and mitigate the described problems, both theoretically and through practical examples. We show that finding Visual Analytics design solutions for ethical issues is not a mere optimization task, but balancing out and negotiating these trade-offs has, as we argue, to be an integral aspect of the system design process from the outset.
A proposal for future data organization in enterprise systems—an analysis of established database approaches

Abstract The digital transformation sets new requirements to all classes of enterprise systems in companies. ERP systems in particular, which represent the dominant class of enterprise systems, are struggling to meet the new requirements at all levels of the architecture. Therefore, there is an urgent need to reconsider the overall architecture of the systems and address the root of the related issues. Given that many restrictions ERP pose on their adaptability are related to the standardization of data, the database layer of ERP systems is addressed. Since database serve as the foundation for data storage and retrieval, they limit the flexibility of enterprise systems and the chance to adapt to new requirements accordingly. So far, relational databases are widely used. Using a systematic literature approach, recent requirements for ERP systems were identified. Prominent database approaches were assessed against the 23 requirements identified. The results reveal the strengths and weaknesses of recent database approaches. To this end, the results highlight the demand to combine multiple database approaches to fulfill recent business requirements. From a conceptual point of view, this paper supports the idea of federated databases which are interoperable to fulfill future requirements and support business operation. This research forms the basis for renewal of the current generation of ERP systems and proposes to ERP vendors to use different database concepts in the future.
New Qualitative Perspective in Human–Computer Interaction: Designing Mobile English for STEM

The emerging field of human-computer interaction (HCI) opens up more opportunities for technology integration in language learning. Technology creates more workforces in the Science, Technology, Engineering, Mathematics (STEM) field, yet the number of STEM pursuers is declining due to poor command of the English language. There is a gap in English vocabulary for STEM, which needs a novel solution from the perspective of HCI. This study aims to explore the needs of STEM learners in creating an English language competency mobile module. The methodology used is through a qualitative study. Seven STEM learners, 17 years old, participated in semi-structured interviews. The results from the interview are divided into four main themes: (1) the importance of learning English, (2) problems of learners, (3) strategies of English language learning, and (4) learners' readiness in using a mobile app. This study is significant for mobile app designers, English language teachers, and course designers as the input will provide an overview of the STEM learners' needs in the English language. Plus, designing a mobile app from the learners' perspectives gives more effectiveness to HCI, rendering success to second language acquisition. Future work can design and develop a mobile app to enhance STEM learners' English language competency based on the learners' perspectives.
“Two-dimensional Terraced Compression method“ and its application in contour detection of transmission image

Multispectral transmission imaging provides a possibility for early breast cancer screening. Due to the strong scattering effect of the light source and the absorption characteristics of the material itself, the image signal is weak. The frame accumulation and demodulation technique can improve the accuracy of the image, but it brings a lot of redundant data. This paper proposes the "Two-dimensional Terraced Compression Method" and applies it to detecting heterogeneity contour in transmission images. The experiment is designed to prove its effectiveness. Four kinds of LEDs with different central wavelengths are respectively modulated as the light source to obtain the image sequences, and the Fast Fourier Transform (FFT) and frame accumulation are used to obtain single-wavelength images respectively. The image is first low-pass filtered, then find the gray minimum value in the image, and then find the connected area in the influence domain of the gradient threshold. If the connected area meets the area threshold, it is used as an effective growth point, and the gray value in the connected area is reassigned. Otherwise, mark it as an isolated point, return to find the minimum, and finally implement terraced compression on the image. This method not only reduces the redundancy of gray numbers but also greatly improves the gradient information of the image, and be used as a preprocessing image algorithm-nonlinear filtering also can be used to detect the contour of heterogeneity.
Detail preserving conditional random field as 2-D RNN for gland segmentation in histology images

Grading of cancer offers crucial insights for treatment planning. Morphology of glands in histology images is of prime importance for grading several types of cancers. Therefore, accurate segmentation of glands plays a pivotal role in planning the treatment in case of such cancers. We introduce a first-of-its-kind detail preserving conditional random field for gland segmentation from histology images. Our design involves a novel formulation of Gibbs energy that captures the spatial interaction between neighboring pixels through the hidden state of a 2-D recurrent neural network (2-D RNN). We show that the iterative training of the 2-D RNN results in the minimization of the Gibbs energy leading to accurate gland segmentation. Experiments on publicly available histology image datasets show the efficacy of the proposed method in accurate gland segmentation. Our model achieves at least 7% improvement in terms of Hausdorff distance for gland segmentation compared to a number of state-of-the-art techniques.
Contextual Squeeze-and-Excitation Mask R-CNN for SAR Ship Instance Segmentation

Ship detection and ship classification using synthetic aperture radar (SAR) have been extensively studied. Yet, SAR ship segmentation unexpectedly receives less attention. Therefore, we will supplement the blank of such study in this paper. Specifi-cally, we present a novel contextual squeeze-and-excitation Mask R-CNN (C-SE Mask R-CNN) dedicated to ship instance segmen-tation in SAR images. Note that the instance segmentation simultaneously considers ship detection and ship segmentation. Intuitively, C-SE Mask R-CNN is a variant of Mask R-CNN from the computer vision community. It embeds a contextual squeeze-and-excitation module (C-SE Module) into RoIAlign of Mask R-CNN to capture prominent different levels of backgrounds&#x0027; contextual information. Experimental results on the public PSeg-SSDD da-taset reveal the objective accuracy progress (i.e. a 1.4&#x0025; AP gain on the detection task meanwhile a 0.9&#x0025; AP gain on the segmentation task) of C-SE Mask R-CNN, in contrast to the vanilla Mask R-CNN.
Quantum data visualization: A quantum computing framework for enhancing visual analysis of data

Data visualization assists in the evaluation and analysis of large graphical data sets. In this study, quantum data visualization (QDV) is proposed as the first attempt to aid users in more effectively comprehending data via quantum mechanical effects. The QDV framework is introduced to fully illustrate the steps necessary for implementing this novel concept. To provide a more intuitive visual representation for data analysis, the quantum rendering module is established to associate quantum data with color gradient information based on continuous geometric primitives in QDV tools. As an application, 2D and 3D QDV tools are designed and applied, including quantum circuit diagrams for preparing pie charts, scatter plots, bar graphs, and function curves. Moreover, the interaction mechanisms used to perform scaling, numerical calculations, and position swapping operations on geometric primitives are discussed and demonstrated. In analyzing QDV efficiency, evaluation metrics, such as cost, delay, width, and auxiliary qubit quantities, were calculated for key quantum processes, to assess framework performance and illustrate corresponding advantages over conventional data visualization models.
Success factors analysis for requirement elicitation in global software development paradigm: An empirical study

Abstract Requirement's elicitation is the process of gathering requirements from users, customers, and stakeholders using traditional or collaborative elicitation techniques. Software requirement gathering is a challenging task particularly in a Global Software Development (GSD) paradigm due to geographical distance, limited face to face meetings, time zone differences, and language and cultural barriers. As more companies begin to adopt GSD in order to save costs, the success factors need to be identified and evaluated in order to ensure a successful elicitation process. This paper offers an in‐depth analysis on success factors for requirement elicitation within GSD environment. First, all possible success factors are identified from the literature via a Systematic Literature Review (SLR). Next, these factors are evaluated by software industries via a questionnaire survey across different types and levels of experts, size of organizations, and from the client–vendor perspective. The relationships between the success factors and the survey results were evaluated using the Spearman's correlation coefficient. The results produced a 0.835 Spearman's correlation coefficient at significance level ρ = 0.000, which showed a strong positive correlation between the outcome of SLR and survey with no significant difference.
The Role of Cognitive-Motivational Factors in Reading Comprehension of Informative and Narrative Texts: Which Is More Effective?

Many studies in the literature have addressed reading comprehension, the ultimate goal of the reading process, due to its complex nature. These studies have focused on various areas, such as the nature of reading comprehension, how it occurs, the factors that affect the process of comprehension, reading comprehension impediments, and how to improve reading comprehension. The current study aims at examining the cognitive and motivational factors to explain reading comprehension. In the light of the related literature, the cognitive factors in this study were vocabulary, prior knowledge, and use of reading strategies, while the motivational factors include motivation and self-efficacy. Both informative and narrative texts were used to measure the success in reading comprehension. The participants were composed of 384 students studying in 6th grade (F: 205, M: 179). The study results reveal that cognitive and motivational factors predict reading comprehension at a statistically significant level. The most effective two factors are vocabulary and prior knowledge, both of which are cognitive factors. The order of relative importance of the variables is vocabulary, prior knowledge, motivation, self-efficacy, and reading strategies. There are some suggestions made in the light of the study results for researchers and practitioners.
Improvement Of The CIA Triad For Al-Rajhi Online Banking System

Online banking is a financial institution establishes an online presence and allows financial transactions to be conducted over the internet. It's the simplest way to make people's lives better because it eliminates the need for consumers and individuals to waste time and energy physically going to and visiting the bank every time, they need to complete a transaction. Because the main goal of online banking is for customers to be able to conduct their transactions online, a working internet connection is essential. Customers must also have an active account with their individual banks in order to access the online bank's website. The issue in online banking is unpredictable of risks that can impact the organization. This research discuss on the improvement of CIA triad to reduce the risk on online banking system focusing Al-Rajhi Bank.
Personalized Taiwanese Speech Synthesis using Cascaded ASR and TTS Framework

To bring endangered Taiwanese language back to life, this paper leveraged a large-scale Taiwanese across Taiwan (TAT) corpus to construct cascaded automatic speech recognition (ASR) and text-to-speech (TTS)-based personalized Taiwanese speech synthesizers to help young people to learn how to speak Taiwanese. This paradigm not only alleviates the low resource, nonparallel corpus and cross-lingual training data problems but also dramatically reduces the fine-tuning data size and training time. Experimental results on a Taiwanese-to-Taiwanese and Mandarin-to-Taiwanese voice conversion tasks had shown that it allows us to successfully produce good personalized Taiwanese TTS with only approximately 3 minutes of data in both cases.
CAST: Learning Both Geometric and Texture Style Transfers for Effective Caricature Generation

Given a photo of a subject, ability to generate a caricature image that captures distinct characteristics of the subject but with certain exaggeration of their prominent features is of fundamental importance to image processing and facial recognition. There are two main challenges in this task: shape exaggeration and style transfer. The former morphs and exaggerates key facial features of the subject, while the latter generates caricature images in a certain artistic style. In this paper, we propose a CAricature Style Transfer (CAST) framework for caricature generation. There are two modules in the proposed framework. The first is a geometric warping module. Different from the existing style transfer methods, we incorporate the Whitening and Coloring Transformation (WCT) in the geometric style transfer. The WCT is learned on photo and caricature landmarks or the caricature landmark space of a specific artist and is capable of transforming input photo landmarks to caricature landmarks. The second module is a texture style rendering module. We propose a new style transfer method by considering a semantic region-aligned style transfer via affinity constraint. Given a reference caricature image as the style reference, this module is capable of transferring styles between the same or similar semantic regions in caricatures and photos. Furthermore, it can transfer visual attributes of the reference caricatures (such as mouth shape and expressions) to the output caricatures. Experiments have shown desirable effects of the proposed method in transferring both the geometric and artistic texture styles of caricatures. Both qualitative and quantitative results show that the CAST framework is more effective compared than the state-of-the-art caricature generation methods.
Improving IoT data availability via feedback- and voting-based anomaly imputation

In the era of the Internet of Everything, the massive Internet of Things (IoT) data bring multiple challenges to IoT development, high system availability, and ultimately the quality of service (QoS) improvement of IoT systems. Abnormal data is one of the main reasons that lead to unexpected processing results and fatal functionality faults in IoT systems, which also hinder the development and deployment of IoT. Existing outlier identification approaches detect anomalies in IoT without further processing these anomalies to improve the availability of datasets. In this paper, we develop a feedback- and voting-based anomaly imputation technique that improves IoT data availability by imputing anomalous sensor data. Specifically, we first propose a feedback- and voting-based feature selection method that extracts crucial features from raw datasets by using mini-batch data. We then utilize the selected features to detect anomalies, followed by an anomaly imputation technique that replaces abnormal data in raw datasets. Experimental results show that the proposed dataset anomaly imputation technique can achieve anomaly detection accuracy and availability improvement of up to 94.88% and 97.96%, respectively.
Acute Lymphoblastic Leukemia Detection by Tuned Convolutional Neural Network

Convolutional neural networks (CNNs) represent a relatively new type of neural networks but it has been already widely used for different image classification problems and have shown promising results. Medicine represents a science that has a high demand for image processing and classification methods. In this paper, we used CNN for classifying microscopic white blood cell images. The problem with CNN is that it contains a large number of hyperparameters that need to be tuned and it represents a hard optimization problem. In this paper, we proposed a bare bone fireworks algorithm for tuning a subset of CNN hyperparameters. The proposed method was tested on a standard benchmark dataset for acute lymphoblastic leukemia detection. The proposed method was compared with CNN without hyperparameter tuning and optimized SVM method from literature. The proposed optimized CNN method achieved higher accuracy compared to the mentioned two methods from the literature.