Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Passengers' Choice in Ride-Hailing Service with Dedicated-Ride Option and Ride-Sharing Option

With the popularity of mobile application and mobile payment, online ride-hailing business developed most rapidly in recent years. Most ride-hailing companies provide both dedicated-ride option (with one passenger in the car) and ride-sharing option (with more than one passenger for different destinations) for passengers. The passengers' choice among different options is of great interests to the ride-hailing operators for improving their business. In this paper, logit-based choice models are established to estimate the passengers' choice among taxi, dedicated ride, and ride-sharing. Survey data in China is collected for model calibration. The models reveal how the key factors, e.g., traveling distance, waiting time, and pricing affect passengers' choice. It is also interesting to find that the pricing of ride-sharing option is vital for profit maximization.
Demystifying Arm TrustZone

The world is undergoing an unprecedented technological transformation, evolving into a state where ubiquitous Internet-enabled “things” will be able to generate and share large amounts of security- and privacy-sensitive data. To cope with the security threats that are thus foreseeable, system designers can find in Arm TrustZone hardware technology a most valuable resource. TrustZone is a System-on-Chip and CPU system-wide security solution, available on today’s Arm application processors and present in the new generation Arm microcontrollers, which are expected to dominate the market of smart “things. ” Although this technology has remained relatively underground since its inception in 2004, over the past years, numerous initiatives have significantly advanced the state of the art involving Arm TrustZone. Motivated by this revival of interest, this paper presents an in-depth study of TrustZone technology. We provide a comprehensive survey of relevant work from academia and industry, presenting existing systems into two main areas, namely, Trusted Execution Environments and hardware-assisted virtualization. Furthermore, we analyze the most relevant weaknesses of existing systems and propose new research directions within the realm of tiniest devices and the Internet of Things, which we believe to have potential to yield high-impact contributions in the future.
Deep Semantic Mapping for Heterogeneous Multimedia Transfer Learning Using Co-Occurrence Data

Transfer learning, which focuses on finding a favorable representation for instances of different domains based on auxiliary data, can mitigate the divergence between domains through knowledge transfer. Recently, increasing efforts on transfer learning have employed deep neural networks (DNN) to learn more robust and higher level feature representations to better tackle cross-media disparities. However, only a few articles consider the correction and semantic matching between multi-layer heterogeneous domain networks. In this article, we propose a deep semantic mapping model for heterogeneous multimedia transfer learning (DHTL) using co-occurrence data. More specifically, we integrate the DNN with canonical correlation analysis (CCA) to derive a deep correlation subspace as the joint semantic representation for associating data across different domains. In the proposed DHTL, a multi-layer correlation matching network across domains is constructed, in which the CCA is combined to bridge each pair of domain-specific hidden layers. To train the network, a joint objective function is defined and the optimization processes are presented. When the deep semantic representation is achieved, the shared features of the source domain are transferred for task learning in the target domain. Extensive experiments for three multimedia recognition applications demonstrate that the proposed DHTL can effectively find deep semantic representations for heterogeneous domains, and it is superior to the several existing state-of-the-art methods for deep transfer learning.
A Perspective Analysis of Handwritten Signature Technology

Handwritten signatures are biometric traits at the center of debate in the scientific community. Over the last 40 years, the interest in signature studies has grown steadily, having as its main reference the application of automatic signature verification, as previously published reviews in 1989, 2000, and 2008 bear witness. Ever since, and over the last 10 years, the application of handwritten signature technology has strongly evolved and much research has focused on the possibility of applying systems based on handwritten signature analysis and processing to a multitude of new fields. After several years of haphazard growth of this research area, it is time to assess its current developments for their applicability in order to draw a structured way forward. This perspective reports a systematic review of the last 10 years of the literature on handwritten signatures with respect to the new scenario, focusing on the most promising domains of research and trying to elicit possible future research directions in this subject.
From autism educators to game designers: integrating teaching strategies into game design for autism education support

Many autism intervention approaches are based in playful interactions and games such as toys and puzzles, grounded in findings on the positive influence of play on learning. Previous studies have investigated the potential of interactive technology for autism education, showing that it enhances the learning experience and reduces the need for continuous assistance. However, teaching strategies are rarely considered as direct input for designing games that educators would use in their teaching practice. We conducted ethnographic research at a school for autistic children and found that educators want digital technologies that embed their current teaching and behaviour management strategies. Our results highlight links between autism teaching strategies and game design. We then used the results to devise a game design model for supporting autism education.
Using a Mind Map to Learn English Vocabulary

English vocabulary constitutes a challenge for English foreign language teaching and learning. Most students spend a lot of time studying English vocabulary, but the result is often frustrating and unsatisfactory when so many students learn vocabulary by rote memorization, which is boring and dampens students' enthusiasm for learning. Therefore, it is desirable to find an effective tool to help students learn English vocabulary. The Mind Map is a thinking tool that, based on divergent thinking, builds up a structure of knowledge for each target word by associating it with other related words or concepts. Thus, recalling of one vocabulary word will prompt the other related items. It also incorporates words, pictures, color, images and graphics, making learning information a vividly visual format. Using Mind Maps in learning is in conformity with the theory of knowledge of visualization, information processing theory and brain science theory. This paper analyzes the feasibility of using the Mind Map in teaching and learning English vocabulary theoretically. Prior to any trials or other empirical research, this initial study adopts the qualitative methodology, mainly analyzing the features of the Mind Map and the scientific basis for using the Mind Map in teaching and learning English vocabulary. The goal of this paper is to encourage English foreign language teachers and students to incorporate the Mind Map into their educational process as an effective technique for teaching and learning English vocabulary.
Distinction system of left and right hands placed on a keyboard of laptop computers

We propose a left-right hand distinction system on the touchpad of a laptop computer. The system uses a mirror and a front-facing camera of the laptop computer to obtain the image around the keyboard and the touchpad. The system distinguishes the left and right of a user's hand by image processing the obtained image. In this paper, we demonstrate that the user can execute different operations while keeping the hands on the home position of the keyboard.
Classification of Benign and Malignant Breast Mass in Digital Mammograms with Convolutional Neural Networks

Breast cancer is one of the most common cancers affecting women lives worldwide. It is usually quite difficult for radiologists to accurately distinguish between malignant and benign tumor in digital mammograms. An intelligent classifier based on conventional machine learning algorithms can help radiologists classifying abnormal breast mass and diagnosing breast cancer. Recently, deep learning has attracted much research attention in medical image analysis due to its higher classifying accuracy and the capability of learning features from annotated imaging data automatically. Therefore, we proposed a deep neural network model to classify benign and malignant tumors in digital mammograms. Our model is an improved version of the AlexNet, which is a Convolutional Neural Networks (CNN) model of deep learning. Totally 115 regions of interest (ROIs) were extracted from Mammographic Images Analysis Society (MIAS) database and finally augmented to 4600 images used as the training and testing dataset. In order to compare our proposed model with conventional learning models, an SVM-based classifier was implemented based on the same dataset. Experimental results showed that our model has more significant classification capability with the accuracy of 97.57%, while the SVM-based model has only 86.08% accuracy.
Semi-supervised document classification using heterogeneous rule selection

In traditional supervised classification, a large set of labeled data is required to train the model. However, labeled data are often hard to obtain and expensive, because human efforts are needed for the labeling. Therefore, semi-supervised learning techniques that utilize both labeled and unlabeled data together have gained much interest in the literature of data classification field. Among the semi-supervised learning techniques, self-training is one of the representative algorithms, which able to train a classifier iteratively by enlarging the learning set. Unfortunately, this iterative training process only works well if the predicted labels in high confidence are correct. To overcome this problem, we modify the self-training method of semi-supervised learning to improve the accuracy of document classification. In this work, we attempt to classify the source documents into different categories and measure their classification accuracy. We improve the self-training classifier in two ways. At first, we use heterogeneous unlabeled documents for enlarging original learning set. Next, in order to overcome the performance degradation problem of self-training, the rule selection algorithm is applied. Lastly, the classification accuracies of traditional supervised classification and our approach-integrated semi-supervised classification are compared.
An Evaluation Model of Omni-channel Retail Logistics Service Integration Level

The era of Omni-Channel Retailing (OCR) is coming, but the commercial practice of OCR is in its infancy with relevant researches are also less. This paper analyzes the content of Omni-Channel Retail Logistics Service (OCRLS), clarifies the index system of the evaluation model of OCRLS integration level, and empowers the indicators. Finally, evaluate the capabilities of OCRLS of 112 well-known retail enterprises through the model while analyze the current status of the OCRLS integration in China. The results show that at present most companies in China do not pay enough attention to physical store logistics services and reverse logistics services, but most companies are aware of the arrival of the Omni-channel era and are in the process of integrating all channels and integrating logistics services.
Differentiating Regularization Weights -- A Simple Mechanism to Alleviate Cold Start in Recommender Systems

Matrix factorization (MF) and its extended methodologies have been studied extensively in the community of recommender systems in the last decade. Essentially, MF attempts to search for low-ranked matrices that can (1) best approximate the known rating scores, and (2) maintain low Frobenius norm for the low-ranked matrices to prevent overfitting. Since the two objectives conflict with each other, the common practice is to assign the relative importance weights as the hyper-parameters to these objectives. The two low-ranked matrices returned by MF are often interpreted as the latent factors of a user and the latent factors of an item that would affect the rating of the user on the item. As a result, it is typical that, in the loss function, we assign a regularization weight λp on the norms of the latent factors for all users, and another regularization weight λq on the norms of the latent factors for all the items. We argue that such a methodology probably over-simplifies the scenario. Alternatively, we probably should assign lower constraints to the latent factors associated with the items or users that reveal more information, and set higher constraints to the others. In this article, we systematically study this topic. We found that such a simple technique can improve the prediction results of the MF-based approaches based on several public datasets. Specifically, we applied the proposed methodology on three baseline models -- SVD, SVD++, and the NMF models. We found that this technique improves the prediction accuracy for all these baseline models. Perhaps more importantly, this technique better predicts the ratings on the long-tail items, i.e., the items that were rated/viewed/purchased by few users. This suggests that this approach may partially remedy the cold-start issue. The proposed method is very general and can be easily applied on various recommendation models, such as Factorization Machines, Field-aware Factorization Machines, Factorizing Personalized Markov Chains, Prod2Vec, Behavior2Vec, and so on. We release the code for reproducibility. We implemented a Python package that integrates the proposed regularization technique with the SVD, SVD++, and the NMF model. The package can be accessed at https://github.com/ncu-dart/rdf.
Simultaneous Detection and Segmentation of Cell Nuclei based on Convolutional Neural Network

In this paper, we mainly focus on automatic detection and segmentation of cell nuclei in histopathology images. Though some methods have been presented to solve these issues, there is scope for efficiency and performance to improve. We propose an end-to-end trainable convolutional neural network, which can learn the object-level and pixel-level information of image patches. In this way, the output feature map could be applied in nuclei detection and segmentation tasks concurrently. Then the weighted patch aggregation and refinement methods are utilized to achieve the final segmentation result. The experiments on the standard public dataset demonstrate that that our method achieves a good performance on nuclei detection and segmentation.
An Introduction to Multiobjective Simulation Optimization

The multiobjective simulation optimization (MOSO) problem is a nonlinear multiobjective optimization problem in which multiple simultaneous and conflicting objective functions can only be observed with stochastic error. We provide an introduction to MOSO at the advanced tutorial level, aimed at researchers and practitioners who wish to begin working in this emerging area. Our focus is exclusively on MOSO methods that characterize the entire efficient or Pareto-optimal set as the solution to the MOSO problem; later, this set may be used as input to the broader multicriteria decision-making process. Our introduction to MOSO includes an overview of existing theory, methods, and provably convergent algorithms that explicitly control sampling error for (1) MOSO on finite sets, called multiobjective ranking and selection; (2) MOSO with integer-ordered decision variables; and (3) MOSO with continuous decision variables. In the context of integer-ordered and continuous decision variables, we focus on methods that provably converge to a local efficient set under the natural ordering. We also discuss key open questions that remain in this emerging field.
Discussion on The Consumers Motivation to Watch E-sports Game and The Follow-up Behaviors Intention

This study aims to explore the inner history of the behavior of E-sports game video viewers, mainly to explore the relationship between motivation, experience and perceived value to behavioral intention. The research subjects were users who have viewed the video of E-sports game in the past six months, used the purposive sampling method to distribute surveys online, 410 questionnaires out of 365 were completed and analyzed, achieving 89% effective collection rate. The obtained data were hypothesized and verified by descriptive statistics, confirmatory factor analysis and structural equation model. The results of this study are as follows: most viewers of E-sports game video are male, age 19-24, the frequency of viewing is at least once a day, no income and gaming games without spending any money; the viewers have the deepest feeling of "entertainment motivation" in viewing motivation, deepest feeling of "think experience" in viewing experience, deepest feeling of "utilitarian value" in perceptual value and deepest feeling of behavioral intention in "I will focus in next upgrade information". The motivation of E-sports game video viewers can directly and indirectly affect the perceived value through the viewing experience, and thus promote the viewer's subsequent intention to produce positive behavior. Based on the results, the suggestions for further studies were offered to the competition game industry and related research as a reference.
Leveraging Sparsity in Distribution Grids

Power distribution grids are sparse networks. The admittance matrix of a (radial or non-radial) power distribution grid is sparse, safety-critical events are relatively sparse at any given time compared with the number of nodes, and loads that produce significant harmonics at a specific order are also sparse. In this highlight talk, we define different types of sparsity in unbalanced three-phase power distribution systems, and explain how sparsity can be leveraged to address three increasingly important problems:
Learning Multi-Level Features for Breast Mass Detection

In order to quickly detect masses from mammography images for the early screening of breast cancer, this paper proposes a breast mass detection improved algorithm based on Faster R-CNN. Firstly, we connect multi- level feature maps (conv-4, conv-5) in ZF model to generated candidate regions in RPN, then use the ROI pool layer to extract the features of the candidate regions. Finally the full connection layer output the region's classification score and the bounding box after regression. Experiments show that the detection sensitivity of this model for breast masses is 93.6%, and the average number of false positives per image is reduced to 0.651. Compared with the original model, the sensitivity of this one increases by 8.5 percentage points and its performance is excellent in the detection of breast masses.
Innovations Assessment Methodology to Promote Knowledge Transfer

Nowadays, in the current globalized and internationalized world it is increasingly important to develop new technological ideas and implement them on a constant basis to assure a dynamic economy. Education centers as universities play an important role on this since their members as students or researchers may contribute to the generation of new ideas or products and consequently leading to the creation of new businesses. On the other hand, the drop out situation of students is a drawback to this purpose, due to failed careers investment that universities and national authorities face without any substantially added value. Therefore, it is intended to create an innovations' assessment methodology to guide students, with entrepreneur characteristics on their innovations, and to help them in the establishment of their educational projects or even businesses motivations. This proposed methodology provides the foundations to the development of an interactive assessment system, as a tool. This instrument intends to evaluate the innovation's ideas acceptance with the main focus of promoting knowledge transfer to facilitate effective innovative research applications testing and products creation.
Litigation Patents Knowledge Flow of Smartphone: Based on the Technology Field Citation Network

Patent litigation reflects the value of patents. Researching the knowledge absorption and diffusion of litigation patents enables us to grasp the technological focus of the industrial competitors, and thus get a map of the industry. The purpose of this study is to understand the characteristics and structure of technological knowledge flow in the smart phone technology field. we proposed the idea of using litigation patents as an entry point to construct a citation network. The study was carried out in three steps: data collection and cleaning; construction of citation network in the technological field and mapping citation network; network analysis. Network analysis was performed by network topological analysis and key nodes centrality analysis. The results showed that the degree distribution of citation network in technology field followed power-law distribution and the citation network was a scale-free network. The key technology fields for smartphone litigation patents were G06F, H04M, H04B, H04N, and H04L. The perspective of this research can be used as a reference for knowledge flow analysis of other mature industries, and it can also provide decision-making basis for industrial competitors' patent layout and prevention of litigation risks.
CVEM: A Cross-chain Value Exchange Mechanism

Blockchain is distributed ledger with the advantage of high security, tamper resistant and traceability. However, in the process of the growth of Blockchain, network isolation hinders the cooperative operation among different Blockchains and greatly restricts the development of Blockchain. There is an urgent need to break the barriers among Blockchains[1], thus cross-chain communication become a new trend of the Blockchain technology[2]. In this paper, we propose CVEM, a value exchange mechanism which supports different kind of tokens transfer cross-chain. First, we put the main exchange process execute off-chain and the final result on-chain. Then, we combine Revocable Sequence Maturity Contract (RSMC) with the idea of cross-token exchange of Blockchain, and use the mechanism of multi-signatures address to constrain the users' behaviors. In addition, we introduce the Simple Payment Verification Proof (SPV Proof) to verify transactions. Our work, to some degree, shortens the transaction process delay and also ensures the security and scalability of the value exchange.