Reviewerly/exHarmony huggingface dataset:
The dilemma of government intervention in a firm's financing: Evidence from China

This paper discovers the dilemma of government intervention in a firm's financing. With evidence from China, we document that government intervention causally promotes a firm's financial access but impedes the positive change of financial access (i.e., micro-financial development). Considering that our instrumental variable (IV) may be imperfectly exogenous, we apply the plausible exogeneity theory to verify that our IV estimates are robust to the relaxation of exclusion restriction. We further use mediation estimates to show that the corruption is the by-product of government intervention in financing and that the corruption due to government intervention constrains micro-financial development. Therefore, we discover that the corruption is the crux of the government-intervention dilemma.
COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations

Interpretable machine learning models trade -off accuracy for simplicity to make explanations more readable and easier to comprehend. Drawing from cognitive psychology theories in graph comprehension, we formalize readability as visual cognitive chunks to measure and moderate the cognitive load in explanation visualizations. We present Cognitive-GAM (COGAM) to generate explanations with desired cognitive load and accuracy by combining the expressive nonlinear generalized additive models (GAM) with simpler sparse linear models. We calibrated visual cognitive chunks with reading time in a user study, characterized the trade-off between cognitive load and accuracy for four datasets in simulation studies, and evaluated COGAM against baselines with users. We found that COGAM can decrease cognitive load without decreasing accuracy and/or increase accuracy without increasing cognitive load. Our framework and empirical measurement instruments for cognitive load will enable more rigorous assessment of the human interpretability of explainable AI.
Deep Matching Network for Handwritten Chinese Character Recognition

Just like its remarkable achievements in many computer vision tasks, the convolutional neural networks (CNN) provide an end-to-end solution in handwritten Chinese character recognition (HCCR) with great success. However, the process of learning discriminative features for image recognition is difficult in cases where little data is available. In this paper, we propose a matching network which builds a connection between template characters and handwritten characters inspired by the human learning process of writing Chinese characters. The matching network replaces the parameters in the softmax regression layer with the features extracted from the template character images. After the training process has been finished, the powerful discriminative features help us to generalize the predictive power not just to new data, but to entire new Chinese characters that never appear in the training set before. Experiments performed on the ICDAR-2013 offline HCCR datasets have shown that the proposed method achieves a comparable performance to current CNN-based classifiers. Besides, the matching network has a very promising generalization ability to new Chinese characters that never appear in the existing training set.
An Exploratory Study of Augmented Reality Presence for Tutoring Machine Tasks

Machine tasks in workshops or factories are often a compound sequence of local, spatial, and body-coordinated human-machine interactions. Prior works have shown the merits of video-based and augmented reality (AR) tutoring systems for local tasks. However, due to the lack of a bodily representation of the tutor, they are not as effective for spatial and body-coordinated interactions. We propose avatars as an additional tutor representation to the existing AR instructions. In order to understand the design space of tutoring presence for machine tasks, we conduct a comparative study with 32 users. We aim to explore the strengths/limitations of the following four tutor options: video, non-avatar-AR, half-body+AR, and full-body+AR. The results show that users prefer the half-body+AR overall, especially for the spatial interactions. They have a preference for the full-body+AR for the body-coordinated interactions and the non-avatar-AR for the local interactions. We further discuss and summarize design recommendations and insights for future machine task tutoring systems.
Podoportation: Foot-Based Locomotion in Virtual Reality

Virtual Reality (VR) allows for infinitely large environments. However, the physical traversable space is always limited by real-world boundaries. This discrepancy between physical and virtual dimensions renders traditional locomotion methods used in real world unfeasible. To alleviate these limitations, research proposed various artificial locomotion concepts such as teleportation, treadmills, and redirected walking. However, these concepts occupy the user's hands, require complex hardware or large physical spaces. In this paper, we contribute nine VR locomotion concepts for foot-based locomotion, relying on the 3D position of the user's feet and the pressure applied to the sole as input modalities. We evaluate our concepts and compare them to state-of-the-art point & teleport technique in a controlled experiment with 20 participants. The results confirm the viability of our approaches for foot-based and engaging locomotion. Further, based on the findings, we contribute a wireless hardware prototype implementation.
Virtual Reality Games for People Using Wheelchairs

Virtual Reality (VR) holds the promise of providing engaging embodied experiences, but little is known about how people with disabilities engage with it. We explore challenges and opportunities of VR gaming for wheelchair users. First, we present findings from a survey that received 25 responses and gives insights into wheelchair users' motives to (non-) engage with VR and their experiences. Drawing from this survey, we derive design implications which we tested through implementation and qualitative evaluation of three full-body VR game prototypes with 18 participants. Our results show that VR gaming engages wheelchair users, though nuanced consideration is required for the design of embodied immersive experiences for minority bodies, and we illustrate how designers can create meaningful, positive experiences.
SVkNN: Efficient Secure and Verifiable k-Nearest Neighbor Query on the Cloud Platform

With the boom in cloud computing, data outsourcing in location-based services is proliferating and has attracted increasing interest from research communities and commercial applications. Nevertheless, since the cloud server is probably both untrusted and malicious, concerns of data security and result integrity have become on the rise sharply. However, there exist little work that can commendably assure the data security and result integrity using a unified way. In this paper, we study the problem of secure and verifiable k nearest neighbor query (SVkNN). To support SVkNN, we first propose a novel unified structure, called verifiable and secure index (VSI). Based on this, we devise a series of secure protocols to facilitate query processing and develop a compact verification strategy. Given an SVkNN query, our proposed solution can not merely answer the query efficiently while can guarantee: 1) preserving the privacy of data, query, result and access patterns; 2) authenticating the correctness and completeness of the results without leaking the confidentiality. Finally, the formal security analysis and complexity analysis are theoretically proven and the performance and feasibility of our proposed approaches are empirically evaluated and demonstrated.
DE-Ada*: A novel model for breast mass classification using cross-modal pathological semantic mining and organic integration of multi-feature fusions

Computer-aided breast mass classification is an effective and widely used technology to assist pathologists in formulating clinical diagnoses and improving working efficiencies. Existing studies usually use a single image feature to perform breast mass classification. Herein, we propose a simple, yet effective, model called the DE-Ada*, which is an organic integration of multi-feature fusions, for breast mass classification. Firstly, we extract a set of complementary features, namely, scale-invariant feature transform (SIFT), GIST, histogram of oriented gradient (HOG), local binary pattern (LBP), residual network (ResNet), densely connected convolutional networks (DenseNet), and visual geometry group (VGG), to characterize mammograms from diverse perspectives. We attempt to mine the cross-modal pathological semantics among these features and complete their early fusion. The dynamic weight of any feature or cross-modal pathological semantics is computed and utilized to complete mid-level feature fusion. Finally, we design two voting-based ensemble learning strategies to implement late feature fusion. Our experiments demonstrate that the DE-Ada* model outperforms baselines on two well-known mammographic datasets. Our model encourages the use of cross-modal pathological semantics to deal with the overfitting problem.
Ethics of digital contact tracing and COVID-19: who is (not) free to go?

Digital tracing technologies are heralded as an effective way of containing SARS-CoV-2 faster than it is spreading, thereby allowing the possibility of easing draconic measures of population-wide quarantine. But existing technological proposals risk addressing the wrong problem. The proper objective is not solely to maximise the ratio of people freed from quarantine but to also ensure that the composition of the freed group is fair. We identify several factors that pose a risk for fair group composition along with an analysis of general lessons for a philosophy of technology. Policymakers, epidemiologists, and developers can use these risk factors to benchmark proposal technologies, curb the pandemic, and keep public trust.
Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Amazon.com Inc. seeks alternative ways to improve manual transactions system of granting employees resources access in the field of data science. The work constructs a modified Artificial Neural Network (ANN) by incorporating a Discrete Hopfield Neural Network (DHNN) and Clonal Selection Algorithm (CSA) with 3-Satisfiability (3-SAT) logic to initiate an Artificial Intelligence (AI) model that executes optimization tasks for industrial data. The selection of 3-SAT logic is vital in data mining to represent entries of Amazon Employees Resources Access (AERA) via information theory. The proposed model employs CSA to improve the learning phase of DHNN by capitalizing features of CSA such as hypermutation and cloning process. This resulting the formation of the proposed model, as an alternative machine learning model to identify factors that should be prioritized in the approval of employees resources applications. Subsequently, reverse analysis method (SATRA) is integrated into our proposed model to extract the relationship of AERA entries based on logical representation. The study will be presented by implementing simulated, benchmark and AERA data sets with multiple performance evaluation metrics. Based on the findings, the proposed model outperformed the other existing methods in AERA data extraction.
Flocking-based live streaming of 360-degree video

Streaming of live 360-degree video allows users to follow a live event from any view point and has already been deployed on some commercial platforms. However, the current systems can only stream the video at relatively low-quality because the entire 360-degree video is delivered to the users under limited bandwidth. In this paper, we propose to use the idea of "flocking" to improve the performance of both prediction of field of view (FoV) and caching on the edge servers for live 360-degree video streaming. By assigning variable playback latencies to all the users in a streaming session, a "streaming flock" is formed and led by low latency users in the front of the flock. We propose a collaborative FoV prediction scheme where the actual FoV information of users in the front of the flock are utilized to predict of users behind them. We further propose a network condition aware flocking strategy to reduce the video freeze and increase the chance for collaborative FoV prediction on all users. Flocking also facilitates caching as video tiles downloaded by the front users can be cached by an edge server to serve the users at the back of the flock, thereby reducing the traffic in the core network. We propose a latency-FoV based caching strategy and investigate the potential gain of applying transcoding on the edge server. We conduct experiments using real-world user FoV traces and WiGig network bandwidth traces to evaluate the gains of the proposed strategies over benchmarks. Our experimental results demonstrate that the proposed streaming system can roughly double the effective video rate, which is the video rate inside a user's actual FoV, compared to the prediction only based on the user's own past FoV trajectory, while reducing video freeze. Furthermore, edge caching can reduce the traffic in the core network by about 80%, which can be increased to 90% with transcoding on edge server.
A Theoretical Foundation of Intelligence Testing and Its Application for Intelligent Vehicles

Intelligent vehicle testing received quickly increasing attention due to the intermittent accidents of intelligent vehicle prototypes that occurred recently. In this paper, we investigate the theoretical underpinnings of such testing and establish a rigid analyzing framework for general intelligence testing problems by borrowing the ideas of Probably Approximately Correct (PAC) learning. Our focus is on the relationship between the number of sampled scenarios and the testing efficiency. We explain various existing algorithms within this new framework and clarify some misconceptions about the reasoning underpinning these methods. We show that intelligent vehicles are testable if the testing scenarios are well defined and appropriately sampled. Moreover, we propose a sampling strategy to generate new challenging scenarios to boost testing efficiency.
An Efficient Double-Layer Blockchain Method for Vaccine Production Supervision

A vaccine is a biological product which is an important means for human beings to protect themselves. Most of its users are young children with weak immunity. Once a vaccine has a problem, it will pose a serious threat to the lives of many people. At present, the supervision of vaccine production is very simple. The vaccine production record is completely controlled by the enterprises. Enterprises only submit production records to the supervisory agency for review when the vaccine needs to be sold. Production records are easily forged and modified. In order to solve the shortcomings of traditional centralized management. We propose a supervision method for vaccine production based on double-level blockchain. At first, we have designed a double-level blockchain structure. The first level is private data of vaccine prduction enterprise, including production records and corresponding hash. The next level is public data, including production records hash and vaccine information. In this way, we make vaccine enterprise to submit production records in a timely manner without fear of privacy leaks. We avoid enterprise tampering or falsification of production records through the non-tampering features and time stamps of the blockchain. To improve the time efficiency, we propose a consensus mechanism for multi-node cooperate. The primary supervisory node provides sorting services and verifies the correctness of the blockchain replica. The ordinary supervisory node can replace the primary supervisory node when necessary, and help the primary supervisory node recovers data in case of information loss. The review node is responsible for providing complete and correct blockchain copies for other nodes. So we can avoids the problem of waste of time resources in the traditional blockchain system. In addition, in order to avoid the waste of space caused by the redundancy of the blockchain, we propose a vaccine data cutting mechanism. We use the timestamp of the blockchain and the vaccine validity period to determine if the block can be cutted. At the same time, it is also possible to judge whether the block can be cutted based on the information exchange with the vaccination institution. Through these methods, we have realized spatiotemporal efficiency supervision of vaccine production. And for the time being, research work in the field of vaccine production supervision is still very rare. So Our work is ground-breaking.
Generative attention adversarial classification network for unsupervised domain adaptation

Domain adaptation is a significant and popular issue of solving distribution discrepancy among different domains in computer vision. Generally, previous works proposed are mainly devoted to reducing domain shift between source domain with labeled data and target domain without labels. Adversarial learning in deep networks has already been widely applied to learn disentangled and transferable features between two different domains to minimize domains distribution discrepancy. However, these methods rarely consider class distributions among source data during adversarial learning, and they pay little attention to these transferable regions among source and target domains images. In this paper, we propose a Generative Attention Adversarial Classification Network (GAACN) model for unsupervised domain adaptation. To learn a joint feature distribution between source and target domains, we present an improved generative adversarial network (GAN) following the feature extractor. Firstly, the discriminator of GAN discriminates the distribution of domains and the classes distribution among source data during adversarial learning, so that our feature extractor can learn a joint feature distribution between source and target domains and maintain the classes consistent simultaneously. Secondly, we present an attention module embedded in GAN, which allows the discriminator to discriminate the transferable regions among the images of source and target domains. Lastly, we propose a simple and efficient method which allocates pseudo-labels for unlabeled target data, and it can improve the performance of our model GAACN while mitigating negative transfer. Extensive experiments demonstrate that our proposed model achieves perfect results on several standard domain adaptation datasets.
The effectiveness of app‐based language instruction for developing receptive linguistic knowledge and oral communicative ability

Abstract Hundreds of millions of language learners around the globe study a second language with popular apps such as Duolingo, Rosetta Stone, Busuu, and Babbel. This study examined the effectiveness of one app, Babbel, for developing both receptive linguistic knowledge of vocabulary and grammar, as well as oral communicative ability in Spanish as a second language. Fifty‐four English speakers not enrolled in Spanish classes studied Spanish on Babbel over the course of 12 weeks, with a goal of studying roughly 10 min per day. Participants completed pretests and posttests, and provided data on their motivation to learn Spanish and use Babbel. Results showed that learners were able to develop grammar and vocabulary knowledge as well as oral communicative ability. The amount of Babbel study time was the strongest predictor for all three measures, with motivation to learn Spanish also significantly predicting growth in oral communicative ability.
Assessing the biochemical oxygen demand using neural networks and ensemble tree approaches in South Korea

The biochemical oxygen demand (BOD), one of widely utilized variables for water quality assessment, is metric for the ecological division in rivers. Since the traditional approach to predict BOD is time-consuming and inaccurate due to inconstancies in microbial multiplicity, alternative methods have been recommended for more accurate prediction of BOD. This study investigated the capability of a novel deep learning-based model, Deep Echo State Network (Deep ESN), for predicting BOD, based on various water quality variables, at Gongreung and Gyeongan stations, South Korea. The model was compared with the Extreme Learning Machine (ELM) and two ensemble tree models comprising the Gradient Boosting Regression Tree (GBRT) and Random Forests (RF). Diverse water quality variables (i.e., BOD, potential of Hydrogen (pH), electrical conductivity (EC), dissolved oxygen (DO), water temperature (WT), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (T-N), and total phosphorus (T-P)) were utilized for developing the Deep ESN, ELM, GBRT, and RF with five input combinations (i.e., Categories 1-5). These models were evaluated by root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), coefficient of determination (R2), and correlation coefficient (R). Overall evaluations suggested that the Deep ESN5 model provided the most reliable predictions of BOD among all the models at both stations.
Semantic code search via equational reasoning

We present a new approach to semantic code search based on equational reasoning, and the Yogo tool implementing this approach. Our approach works by considering not only the dataflow graph of a function, but also the dataflow graphs of all equivalent functions reachable via a set of rewrite rules. In doing so, it can recognize an operation even if it uses alternate APIs, is in a different but mathematically-equivalent form, is split apart with temporary variables, or is interleaved with other code. Furthermore, it can recognize when code is an instance of some higher-level concept such as iterating through a file. Because of this, from a single query, Yogo can find equivalent code in multiple languages. Our evaluation further shows the utility of Yogo beyond code search: encoding a buggy pattern as a Yogo query, we found a bug in Oracle's Graal compiler which had been missed by a hand-written static analyzer designed for that exact kind of bug. Yogo is built on the Cubix multi-language infrastructure, and currently supports Java and Python.
A robust time series prediction method based on empirical mode decomposition and high-order fuzzy cognitive maps

Fuzzy cognitive maps (FCMs) have been widely used in time series prediction due to the excellent performance in dynamic system modeling. However, existing time series prediction methods based on FCMs have some defects, such as low precision and sensitivity to hyper parameters. Therefore, more accurate and robust methods remain to be proposed for handling non-stationary and large-scale time series. To address this issue, in this paper, a novel time series prediction method based on empirical mode decomposition (EMD) and high-order FCMs (HFCMs) is proposed, termed as EMD-HFCM. First, EMD is applied to extract features from the original sequence to obtain multiple sequences to represent the nodes of HFCM. To learn HFCM efficiently and accurately, a robust learning method based on Bayesian ridge regression is employed, which can estimate the regular parameters from data instead of being set manually. Then, prediction can be performed based on the iterative characteristics of HFCM. To compare EMD-HFCM with existing methods, extensive experiments are conducted on eight benchmark datasets and the results validate the performance of the proposal in handling large-scale and non-stationary time series. Furthermore, the experiments also show that the proposed method is much more robust and insensitive to hyper parameters than the state of art methods. Finally, non-parametric statistical tests are carried out and the superiority of the proposed method is verified in the statistical sense.