Reviewerly/exHarmony huggingface dataset:
Is your supply chain ready to embrace blockchain?

Journal of Corporate Accounting & FinanceVolume 31, Issue 2 p. 54-64 BLIND PEER REVIEW Is your supply chain ready to embrace blockchain? Naeem Bajwa, Corresponding Author Naeem Bajwa anbajwa@ualr.edu orcid.org/0000-0002-1134-7937 Department of Management, University of Arkansas at Little Rock, Little Rock, Arkansas Correspondence Naeem Bajwa, College of Business, University of Arkansas at Little Rock, 2801 South University Avenue, Little Rock, AR 72204. Email: anbajwa@ualr.eduSearch for more papers by this authorKyleen Prewett, Kyleen Prewett Department of Accounting, University of Arkansas at Little Rock, Little Rock, ArkansasSearch for more papers by this authorCynthia L. Shavers, Cynthia L. Shavers Department of Accounting, University of Arkansas at Little Rock, Little Rock, ArkansasSearch for more papers by this author Naeem Bajwa, Corresponding Author Naeem Bajwa anbajwa@ualr.edu orcid.org/0000-0002-1134-7937 Department of Management, University of Arkansas at Little Rock, Little Rock, Arkansas Correspondence Naeem Bajwa, College of Business, University of Arkansas at Little Rock, 2801 South University Avenue, Little Rock, AR 72204. Email: anbajwa@ualr.eduSearch for more papers by this authorKyleen Prewett, Kyleen Prewett Department of Accounting, University of Arkansas at Little Rock, Little Rock, ArkansasSearch for more papers by this authorCynthia L. Shavers, Cynthia L. Shavers Department of Accounting, University of Arkansas at Little Rock, Little Rock, ArkansasSearch for more papers by this author First published: 07 November 2019 https://doi.org/10.1002/jcaf.22423Citations: 3 Correction added on March 28, 2020, after first online publication: Article category updated from Commentary to Blind Peer Review. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume31, Issue2Special Issue: Blockchain Technology: Promises and PerilsApril 2020Pages 54-64 RelatedInformation
Understanding the Users and Videos by Mining a Novel <i>Danmu</i> Dataset

Recent years have witnessed a successful rise of the time synchronized <i>gossiping comment</i>, or so-called danmu combined with online videos. This new business mode has enriched communication among users by sending users&#x2019; feelings through danmus and sharing these danmus on time synchronized videos. Can danmu communication be helpful for better user behavior modeling or video analyzing? To this question, in this article, preliminary attempts are made on analysis of users and videos by introducing a <i>Danmu</i> dataset which is collected from a real-world danmu-enabled video sharing platform. The dataset contains 1.7 TB of videos and danmus in total across eight video categories. With a focus on the 7.9 million danmus records and 4.8 million video frames, we first perform the basic statistic analysis and high-level semantic analysis. After that, we show some of the previous work on this area, including user behavior modeling, fine-grained video understanding and labeling, video plot generation and image-enhanced semantic understanding. For each application, we also propose its possible future directions. We hope this new dataset will inspire new ideas in areas among language, multimedia, and user understanding.
View-based weight network for 3D object recognition

Projective methods generally achieve better results in 3D object recognition in recent years. This may be similar to that human visual 3D shapes rely on various 2D observations which are unconscious on retina. Each projection is treated fairly in existing methods. However, we note that different viewpoint images of the same object have different discriminative features, and only some of images are completely significant. We propose a novel View-based Weight Network (VWN) for 3D object recognition where the different view-based weights are assigned to different projections. The trainable view-level weights are incorporated as a pooling layer of the multi-view residual network. The pooling layer contains 7 sub-layers. Meanwhile, we find a simple unsupervised criterion to evaluate the prediction results before they output. To improve the recognition accuracy, a new multi-channel integrated classifier combining Extreme Learning Machine, KNN, SVM and Random Forest is proposed based on the criterion. The multi-channel classifier can make the accuracy of Top1 close to Top2. Experiments on Princeton ModelNet 3D datasets demonstrate our proposed method outperforms the state-of-the-art approaches significantly in recognition accuracy.
Understanding the Needs and Challenges of Using Conversational Agents for Deaf Older Adults

Conversational agents (CAs) with voice interfaces are becoming ubiquitous and are routinely used by a wide range of individuals. Users with vision and mobility issues can leverage the voice interfaces of CAs to complete tasks with increasing complexity. However, CAs with voice interfaces pose unique challenges for those with different accessibility needs - specifically, older adults with hearing loss.There has been little work to understand how deaf older adults leverage CAs and the challenges they face while using voice interfaces. To address this gap, we conduct in-depth qualitative interviews with 4 deaf older adults to understand how and why they use CAs. We explore their expectations for their devices and identify common challenges (e.g., default voices used by commercial CAs). We provide suggestions for designing CAs that can better accommodate a range of hearing abilities and provide the first step forward toward a more inclusive CA.
The Organisational Factors of Software Process Improvement in Small Software Industry: Comparative Study

Small and Medium Enterprises (SMEs) are a great contribution to the international economy and have also been considered an important component in today’s world business. Thus, in order to be more competitive, it is necessary for these companies to deliver their products with high-quality. However, despite their importance, small software companies still face myriad challenges and barriers in producing high-quality products. The objective of this study is to identify the organizational factors that have a positive impact to enable Software Process Improvement (SPI) effort in the small software industry. A Systematic Literature Review (SLR) was conducted to achieve the main objective of this study. The findings from this study provide a roadmap to guide future research in order to enable SPI effort in the small software development industry. We believe that findings from this study will give interesting insights to encourage researchers in using compromise technique to analyze future empirical studies based on a specific region to validate the suitability of identified factors in the specific country.
Software Data Planes

Today's datacenters demand high-performance, energy-efficient software data planes, which are widely used in many areas including fast network packet processing, network function virtualization, high-speed data transfer in storage systems, and I/O virtualization. Modern software data planes bypass OS I/O stacks and rely on cores spinning on user-level queues as a fast notification mechanism. Whereas spin-polling can improve latency and throughput, it entails significant shortcomings, especially when scaling to large numbers of cores/queues. In this paper, we pinpoint and quantify challenges of spin-polling--based software data planes using Intel's Data Plane Development Kit (DPDK) as a representative infrastructure. We characterize four scalability issues of software data planes: (1) Full-tilt spinning cores perform more (useless) polling work when there is less work pending in the queues; (2) Spin-polling scales poorly with the number of polled queues due to processor cache capacity constraints, especially when traffic is unbalanced; (3) Operation rate limits (transactions per second) as well as a Polling Tax (the overhead of polling, which is considerable even when operating at saturation throughput) result in poor core scalability. (4) Whereas shared queues can mitigate load imbalance and head-of-line-blocking, synchronization overheads limit their potential benefits. We identify root causes of these issues and discuss solution directions to improve hardware and software abstractions for better performance, efficiency, and scalability in software data planes.
Context-Aware Human Activity and Smartphone Position-Mining with Motion Sensors

Today’s smartphones are equipped with embedded sensors, such as accelerometers and gyroscopes, which have enabled a variety of measurements and recognition tasks. In this paper, we jointly investigate two types of recognition problems in a joint manner, e.g., human activity recognition and smartphone on-body position recognition, in order to enable more robust context-aware applications. So far, these two problems have been studied separately without considering the interactions between each other. In this study, by first applying a novel data preprocessing technique, we propose a joint recognition framework based on the multi-task learning strategy, which can reduce computational demand, better exploit complementary information between the two recognition tasks, and lead to higher recognition performance. We also extend the joint recognition framework so that additional information, such as user identification with biometric motion analysis, can be offered. We evaluate our work systematically and comprehensively on two datasets with real-world settings. Our joint recognition model achieves the promising performance of 0.9174 in terms of F 1 -score for user identification on the benchmark RealWorld Human Activity Recognition (HAR) dataset. On the other hand, in comparison with the conventional approach, the proposed joint model is shown to be able to improve human activity recognition and position recognition by 5.1 % and 9.6 % respectively.
Joint Optimization for Cooperative Image Captioning

When describing images with natural language, descriptions can be made more informative if tuned for downstream tasks. This can be achieved by training two networks: a "speaker" that generates sentences given an image and a "listener" that uses them to perform a task. Unfortunately, training multiple networks jointly to communicate, faces two major challenges. First, the descriptions generated by a speaker network are discrete and stochastic, making optimization very hard and inefficient. Second, joint training usually causes the vocabulary used during communication to drift and diverge from natural language. To address these challenges, we present an effective optimization technique based on partial-sampling from a multinomial distribution combined with straight-through gradient updates, which we name PSST for Partial-Sampling Straight-Through. We then show that the generated descriptions can be kept close to natural by constraining them to be similar to human descriptions. Together, this approach creates descriptions that are both more discriminative and more natural than previous approaches. Evaluations on the COCO benchmark show that PSST improve the recall@10 from 60% to 86% maintaining comparable language naturalness. Human evaluations show that it also increases naturalness while keeping the discriminative power of generated captions.
Detecting Malicious Accounts in Online Developer Communities Using Deep Learning

Online developer communities like GitHub provide services such as distributed version control and task management, which allow a massive number of developers to collaborate online. However, the openness of the communities makes themselves vulnerable to different types of malicious attacks, since the attackers can easily join and interact with legitimate users. In this work, we formulate the malicious account detection problem in online developer communities, and propose GitSec, a deep learning-based solution to detect malicious accounts. GitSec distinguishes malicious accounts from legitimate ones based on the account profiles as well as dynamic activity characteristics. On one hand, GitSec makes use of users' descriptive features from the profiles. On the other hand, GitSec processes users' dynamic behavioral data by constructing two user activity sequences and applying a parallel neural network design to deal with each of them, respectively. An attention mechanism is used to integrate the information generated by the parallel neural networks. The final judgement is made by a decision maker implemented by a supervised machine learning-based classifier. Based on the real-world data of GitHub users, our extensive evaluations show that GitSec is an accurate detection system, with an F1-score of 0.922 and an AUC value of 0.940.
Document Forgery Detection using Printer Source Identification—A Text-Independent Approach

Source printer identification for printed document forgery detection has gained much significance in recent years. Traditional techniques employed in the literature are highly text dependent and therefore may prove insufficient in certain scenarios. In this study, we present a text-independent approach for effective characterization of source printer, using deep visual features. By employing transfer learning on pre-trained Convolutional Neural Networks (CNNs), we achieve significant recognition results on a dataset of 1200 documents from 20 different printers (13 laser and 7 inkjet). A comparison with various conventional features on the same dataset demonstrate that our proposed methodology classifies printed documents more accurately and effectively.
Spectral Interference of Heavy Metal Contamination on Spectral Signals of Moisture Content for Heavy Metal Contaminated Soils

This article examined the spectral interference by heavy metal on the spectral signal of moisture content of heavy metal contaminated soils. Soil samples were collected from an abandoned mine area, and the chemical analysis revealed extremely high contamination amount of copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), and lead (Pb). The mineralogical analysis showed that the spectral signature of the heavy metal contaminated soils was manifested by secondary minerals. Water content suppressed the spectral reflectance of the soil samples but increased the absorption depths. Although a regression model can predict moisture content using the magnitude of the water absorption feature, the accuracy was much lower when the heavy metal concentration was extremely high. It indicates that geochemical reactions between the heavy metal cation and iron oxide/clay minerals may have affected the spectral responses of the contaminated soils at the water absorption bands. Our model also showed that there was a shift of the absorption features of moisture content if the heavy metal contamination level went up. Unlike normal soils, the absorption features of clay minerals and ferric iron were not able to accurately predict moisture in highly contaminated soils. Given the fact that the spectral bands selected in this article were associated with water absorption, the findings from this article may only be useful to a drone-based low-altitude remote sensing of soil moisture content.
Phonetic change in an Antarctic winter

An acoustic analysis was made of the speech characteristics of individuals recorded before and during a prolonged stay in Antarctica. A computational model was used to predict the expected changes due to close contact and isolation, which were then compared with the actual recorded productions. The individuals were found to develop the first stages of a common accent in Antarctica whose phonetic characteristics were in some respects predicted by the computational model. These findings suggest that the phonetic attributes of a spoken accent in its initial stages emerge through interactions between individuals causing speech production to be incrementally updated.
Non-convex hull based anomaly detection in CPPS

Along with the constantly increasing complexity of industrial automation systems, machine learning methods have been widely applied to detecting abnormal states in such systems. Anomaly detection tasks can be treated as one-class classification problems in machine learning. Geometric methods can give an intuitive solution to such problems. In this paper, we propose a new geometric structure, oriented non-convex hulls, to represent decision boundaries used for one-class classification. Based on this geometric structure, a novel boundary based one-class classification algorithm is developed to solve the anomaly detection problem. Compared with traditional boundary-based approaches such as convex hulls based methods and one-class support vector machines, the proposed approach can better reflect the true geometry of target data and needs little effort for parameter tuning. The effectiveness of this approach is evaluated with artificial and real world data sets to solve the anomaly detection problem in Cyber–Physical-Production-Systems (CPPS). The evaluation results also show that the proposed approach has higher generality than the used baseline algorithms.
Verifying Relational Properties using Trace Logic

We present a logical framework for the verification of relational properties in imperative programs. Our frame-work reduces verification of relational properties of imperative programs to a validity problem in trace logic, an expressive instance of first-order predicate logic. Trace logic draws its expressiveness from its syntax, which allows expressing properties over computation traces. Its axiomatization supports fine-grained reasoning about intermediate steps in program execution, notably loop iterations. We present an algorithm to encode the semantics of programs as well as their relational properties in trace logic, and then show how first-order theorem proving can be used to reason about the resulting trace logic formulas. Our work is implemented in the tool RAPID and evaluated with examples coming from the security field.
Retrain-Less Weight Quantization for Multiplier-Less Convolutional Neural Networks

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Ovarian Cancer Classification Accuracy Analysis Using 15-Neuron Artificial Neural Networks Model

Ovarian cancer is a severe disease for older woman. Based on the research, ovarian cancer is the fifth commonly disease and the seventh causes of death for woman worldwide. For ovarian cancer classification problem, many researchers have performed using Artificial Neural Network (ANN). Classification accuracy is a significant factor for taking decision by the Doctors. Higher classification accuracy can help to take the decision by doctors for giving proper treatment. Accurate and early diagnosis can save lives and reduce the percentage of mortality. This study focuses classification accuracy analysis of ovarian cancer. The purpose of this study is to analyze the classification accuracy using 15-neuron ANN model. The proposed model is benchmarked on ovarian cancer dataset. The achieving result from the proposed model has been compared with the other four classification algorithms. The proposed model has achieved 98.7% ovarian cancer classification accuracy which is more promising and higher than other classification algorithms.
Advanced Visual Metaphors and Techniques for Software Maps

Software maps provide a general-purpose interactive user interface and information display for software analytics tools. This paper systematically introduces and classifies software maps as a treemap-based technique for software cartography. It provides an overview of advanced visual metaphors and techniques, each suitable for interactive visual analytics tasks, that can be used to enhance the expressiveness of software maps. Thereto, the metaphors and techniques are briefly described, located within a visualization pipeline model, and considered within the software map design space. Consequent applications and use cases w.r.t. different types of software system data and software engineering data are discussed, arguing for a versatile use of software maps in visual software analytics.
Depth Perception in Projective Augmented Reality: An Evaluation of Advanced Visualization Techniques

Augmented reality (AR) is a promising tool to convey useful information at the place where it is needed. However, perceptual issues with augmented reality visualizations affect the estimation of distances and depth and thus can lead to critically wrong assumptions. These issues have been successfully investigated for video see-through modalities. Moreover, advanced visualization methods encoding depth information by displaying additional depth cues were developed. In this work, state-of-the-art visualization concepts were adopted for a projective AR setup. We conducted a user study to assess the concepts' suitability to convey depth information. Participants were asked to sort virtual cubes by using the provided depth cues. The investigated visualization concepts consisted of conventional Phong shading, a virtual mirror, depth-encoding silhouettes, pseudo-chromadepth rendering and an illustrative visualization using supporting line depth cues. Besides different concepts, we altered between a monoscopic and a stereoscopic display mode to examine the effects of stereopsis. Consistent results across variables show a clear ranking of examined concepts. The supporting lines approach and the pseudo-chromadepth rendering performed best. Stereopsis was shown to provide significant advantages for depth perception, while the current visualization technique had only little effect on investigated measures in this condition. However, similar results were achieved using the supporting lines and the pseudo-chromadepth concepts in a monoscopic setup. Our study showed the suitability of advanced visualization concepts for the rendering of virtual content in projective AR. Specific depth estimation results contribute to the future design and development of applications for these systems.