Reviewerly/exHarmony huggingface dataset:
Ghost in the Machine: How Taxonomic Metadata Allows for Scaling Ethnographic Insights into Search Algorithms

This case study explores how we personalized search results by turning ethnographic insights into taxonomic metadata, which in turn allowed us to use quantitative methods to assess business impact. The first part of the case study focuses on the problem we were trying to solve—creating better search results for nurses—and using ethnographic interviews to understand how nurses approached looking for jobs. The second part of the study dives more deeply into how metadata works, and why it was the perfect partner for capturing our ethnographic findings and making them into a scalable and measurable part of the design process. The third part of the study details how we tested and scaled our designs in the live project, and why we believe others might benefit from using a similar approach. Keywords: mental models, taxonomy, business impact.
The Role of Landsat-8 Multispectral Data in Spill Response: Three Case Studies

Landsat multispectral imagery has become a valuable resource for decision makers in environmental emergency response over the past three decades. In this chapter, Landsat-8 imagery was primarily utilized to assist spill investigations with three case studies: (1) wastewater spill due to a coal mine dam failure, (2) crude oil and saline produced water spill due to a pipeline leak, and (3) highly saline produced water spill due to a pipeline rupture. In case study 1, an early sign of seepage was detected from a containment pond 6 days prior to the dam failure using an automated endmember detection and abundance estimation method. In case study 2, an early sign of spill was detected more than a year before the spill was reported and time-series surface extent of the spill was quantified based on a simple difference of vegetation indices. In case study 3, spatial-temporal progression of highly saline spill was estimated using moisture indices and a spectral matching technique was used to verify the absence of spill on surface before it was reported. These case studies also utilized Sentinel-2 imagery for better understanding of post-spill remediation process and exemplify how publicly available spaceborne multispectral data might help establish the duration of spill event, reconstruct the spill scenario, and optimize the spill mitigation and prevention control planning.
Layered Ontology Construction Method and the Development of Aided Design Semantic Web for Intelligent Vessel Human Factors Application

During the process of vessel Human Factors application, distributed systems are going to generate huge amount of heterogeneous process data. These data should be treated as knowledge to support the automation of the application process. In this paper, a layered ontology construction method (LOC) is developed as well as a bidirectional automatic mapping method. In LOC, logic layer (object property) and data layer (data property) are split and developed separately. In the mapping method, distributed systems' process database are automatically generated according to each data layer ontology's class structure. These help to make automatic engineering knowledge acquisition possible, which highly improve the efficiency of knowledge entry. Based on this, a semantic web platform is developed, and its automatic aided design function is demonstrated by a sample of vessel materials replenishment simulation system.
Improving Image Similarity Learning by Adding External Memory

The type of neural networks widely used in artificial intelligence applications mixes its computation and memory modules in neuron weights and activities. The previously learned information are stored in network weights. When dealing with complex data, e.g., those possessing diverse content or containing long-sequences, some information stored in the weights can be altered drastically or wiped as the training goes, but they are not necessarily unimportant. External memory is a recent technique proposed to prevent from forgetting significant previously learned information. In this work, we aim at taking advantage of this recent technique to advance the similarity learning task that is critical in many real-world artificial intelligence applications. We propose suitable external memory design supported by extended attention mechanism. Two different kinds of memory modules are proposed so that the similarity learning process can dynamically shift focus over a wide range of diverse content contained by the training data. Effectiveness of the proposed method is demonstrated through evaluations based on different image retrieval tasks and compared against various state-of-the-art algorithms in the field.
Where is IT in Information Security? The Interrelationship between IT Investment, Security Awareness, and Data Breaches

Data breaches can severely damage a firm’s reputation and its customers’ confidence. Firms must therefore continuously invest in security measures to prevent such breaches. However, the effectiveness of security investment has been questioned by both practitioners and academics. We illustrate the bidirectional dynamic relationship between information technologies (IT) investment and data breaches using an 8-year panel of 260 U.S.-listed firms, moderated by threat and countermeasure security awareness. Drawing on Straub and Welke’s security planning model, we provide empirical evidence that investing solely in security measures may not effectively prevent data breaches. IT investment must instead be combined with heightened security awareness. Our results suggest that firms should reconsider whether security performance is a direct outcome of security measures and take a broader perspective when addressing information security concerns.
Fast Video Saliency Detection based on Feature Competition

In this paper, we propose a light video saliency prediction model, named SalFCM, which achieves fixation detection rate of 110fps. It is known that the human attention is captured by objects that have always been present or newly appeared. To model this dynamic change, we propose an Inter-frame Feature Competition Module (IFCM) to make an adaptive choice between correlated and differential features of consecutive frames. Besides, it is noted that saliency is better explained by low-level rather than high-level features in some visual scenes. Hence, we design a Hierarchical Feature Competition Module (HFCM) to balance the influence of low-level and high-level features. Our model achieves a good trade-off between precision and processing speed. The developed SalFCM is evaluated on three video saliency datasets: DHF1K, Hollywood-2 and UCF-sports. We conduct ablation studies to verify the effectiveness of the proposed model.
Life Cycle Package Management for Print and Electronic Resources: Cross-training a Customer-oriented Technical Services Team

Several years ago, North Carolina State (NC State) University Libraries technical services department, Acquisitions & Discovery (A&D), merged acquisitions, cataloging, and electronic resources management functions and staff. One intended outcome for the merger included integrating and distributing electronic resources management across all staff positions whereby staff would be trained to manage a larger portion of the life cycle for print and electronic resources. The benefits of a life cycle approach for both print and electronic resources included better staff understanding of resources; staff ownership of packages; and improved staff follow-through, consistency, and ability to troubleshoot. Key positions were reimagined to support this effort. This included the creation of a staff package manager role in the serials unit to provide oversight of e-journal packages, distribute work to staff, and create and maintain an information dashboard (the Electronic Resources Hub) for staff as well as for other stakeholder departments across the libraries. The monographs unit has recently adopted a similar integrated approach to manage NC State's growing collection of e-books. This chapter will outline A&D implementation of two package management models, one for serials and one for monographs; describe the associated tools and technologies used for support; and discuss lessons learned. Benefits will be discussed to illustrate how other libraries might transform their electronic resource management operations by using a package management strategy.
Channel Splitting Network for Single Image Dehazing

Haze affects the image quality and image analysis, so it is necessary to recover the undamaged content from the blurred image. We propose an end-to-end channel splitting network to directly reconstruct clear and haze-free images without relying on the atmospheric scattering model. By introducing the adjacent connection network, a channel splitting residual 1*1 convolution module and a channel splitting pyramid dilated convolution module are constructed to improve the detailed information of the reconstructed image. We propose an attention mechanism with local features, which further improves the quality of haze-free images. The network that can identify the residual of hazy is constructed by the above modules, which is based on three series connected U-shaped networks with only one downsampling. This network optimization reconstructs the loss function by adaptive weighting. Extensive experiments on RESIDE data set demonstrate that compared with several selected state-of-the-art algorithms, the proposed method achieves significant improvement in quantity and quality.
Recognition of Actions and Subjects from Inertial and FSR Sensors Attached to Objects

This paper describes automatic systems for human activity recognition and identification of subjects based on sensorised objects. The action recognition system is based on a deep neural network - hidden Markov model (DNN-HMM) with augmentation of feature vectors by the i-vector of a given recording to deal with the subject variability. The subject identification system is based on i-vectors. The sensors, comprising an accelerometer, gyroscope, magnetometer and force-sensitive resistors (FSRs), are packaged in a coaster attached to the base of an object, here a jug. Evaluations are performed using nearly 11 hours of data recordings from 26 subjects, containing actions involved in manipulating a jug to make cups of tea. We demonstrate the performance of the DNN-HMM action recognition system in a subject-dependent and subject-independent case and in controlled and natural scenarios. While the subject-dependent system achieved error rate of 0.15% in controlled scenario, this increased up to 15.33% for subject-independent system in natural scenario. The proposed i-vector augmentation provided 26% relative error rate reduction. The subject identification system based on i-vectors and cosine similarity calculation achieved over 68% recognition accuracy in natural scenario.
Voter Privacy and Big-Data Elections

Big data and analytics have changed politics, with serious implications for the protection of personal privacy and for democracy. Political parties now hold large amounts of personal information about the individuals from whom they seek political contributions and, at election time, votes. This voter data is used for a variety of purposes, including voter contact and turnout, fundraising, honing of political messaging, and microtargeted communications designed specifically to appeal to small subsets of voters. Yet both privacy laws and election laws in Canada have failed to keep up with these developments in political campaigning and are in need of reform to protect voter privacy. We provide an overview of big data campaign practices, analyze the gaps in Canadian federal privacy and election law that enable such practices, and offer recommendations to amend federal laws to address the threats to voter privacy posed by big data election campaigns.
Human Activity Recognition using Reduced Kernel Extreme Learning Machine for Body Weight Management

The problem with bodyweight management is the inability to calculate the number of calories burned and consumed. Many applications can help to calculate it and one of them is implementing Human Activity Recognition using wearable sensors and smartphones. In this paper, an activity recognition model is built using the Reduced Kernel Extreme Learning Machine (RKELM) algorithm using an accelerometer sensor embedded in a smartphone that is used for calculating calories burned. This model was improved from the Extreme Learning Machine with the addition of the Gaussian kernel. The dataset comes from the London py data event in 2016 which consists of five activity labels. The proposed model will be compared with five other models and evaluated using precision, recall, f1score, training time, and testing time. The results have been validated with 10-fold cross-validation. The experimental results show that the RKELM-based recognition model has a higher performance than the other models with acceptable training and testing time, with an f1 score of 97% and less than 0.06 seconds.
Empirical Study on Commonly Used Combinations of Estimation Techniques in Software Development Planning

Precise estimation of time and effort in the lifecycle of a project plays an important role in delivering a final product on time within an established budget, and with expected quality. Inappropriate estimation of effort may lead to overestimation or underestimation of required resources. As people remain to be a constant part of the software development process they should also be considered as an influencing factor in software development estimation process. This work presents the most popular estimation techniques that are used by the survey sample. The questionnaire was completed by 51 participants with industrial experience in Innopolis. The goal was to find differences between roles in software development teams and the techniques that are used to estimate efforts. The result of the survey used to derive main groups of the respondents and map them to estimation techniques. These mappings were analyzed and compared between two main groups to find tendencies, differences, and similarities. The study is considered to help understand patterns of effort estimation among software engineers and revising existing estimation techniques.
Learning Activity: The Design Dash—Bringing Your Abstract Ideas to Life

There is a common tendency to move rapidly toward physical manifestations of design ideas once they have been roughly sketched. This tendency has become widespread with the growing availability of digital fabrication technologies like 3D printing. While the desire to move into the realm of the concrete and material is understandable, as it stems from wanting to get solutions into the hands of users who will be impacted by them, doing so can hinder the ability to reflect upon the social and contextual implications of a proposed intervention. In this learning activity, we will provide a short walkthrough of a low-fidelity, paper-based method for iterative prototyping that we call The Design Dash. The goal of a Design Dash is to allow collaborative teams to reflect critically upon the various impacts their solution might have on its intended user population. In doing so, this learning activity can help researchers move from an abstract idea to a real-world intervention that accounts for both the underlying functional needs and the larger social context in which it will be deployed.
Breast Lesion Detection from Mammograms Using Deep Convolutional Neural Networks

Mammography has a central role in screening and diagnosis of breast lesions, allowing early detection of the pathology and reduction of fatal cases. Deep Convolutional Neural Networks have shown a great potentiality to address the issue of early detection of breast cancer with an acceptable level of accuracy and reproducibility. In the present paper, we illustrate the development of a deep learning study aimed to process and classify lesions in mammograms with the use of slender neural networks not yet used in literature. For this reason, a traditional convolution network was compared with a novel one obtained making use of much more efficient depth wise separable convolution layers. Preliminary numerical results are detailed and future plans outlined.
위탁폐수 처리를 위한 파일럿 규모의 UF-NF 막여과 공정의 현장적용성 평가

The purpose of this study was to evaluate the field applicability of UF-NF(Ultrafiltration-Nanofiltration) membrane process as an alternative technology of evaporator in the industrial wastewater treatment system of consigned wastewater management companies. Raw water was used the wastewater before entering the evaporator. The pilot-scale UF-NF process for consigned wastewater treatment was produced with a treatment capacity of 100 m3/d and three UF and five NF membranes were installed, respectively. As a result of water quality analysis before and after UF-NF membrane treatment, the average concentrations of raw water was TDS 31,200 mg/L, Cl- 16,500 mg/L, BOD5 7,080 mg/L, CODCr 26,200 mg/L, TOC 6,650 mg/L, T-N 1,852 mg/L, T-P 6.93 mg/L, SS 35 mg/L, respectively. For UF treated water, the average concentrations were TDS 30,900 mg/L, Cl- 15,800 mg/L, BOD5 6,900 mg/L, CODCr 23,900 mg/L, TOC 6,640 mg/L, T-N 1,530 mg/L, T-P 6.81 mg/L and SS undetected, respectively. For NF(70) treated water, the average concentrations are TDS 25,700 mg/L, Cl- 14,100 mg/L, BOD5 6,810 mg/L, CODCr 22,800 mg/L, TOC 5,850 mg/L, T-N 1,370 mg/L, T-P 5.34 mg/L and SS undetected, respectively. Also for NF(90) treated water, the average concentrations are TDS 21,360 mg/L, Cl- 12,600 mg/L, BOD5 6,020 mg/L, CODCr 21,000 mg/L, TOC 4,840 mg/L, T-N 1,360 mg/L, T-P 5.19 mg/L, SS undetected, respectively. Therefore, it is difficult to satisfy the effluent quality standards in the treatment of consigned wastewater using the UF-NF process. When a filtration process was installed for meeting or better the standard water quality, RO membrane must be installed and run.
Convex vs Convex-Concave Objective for Rare Label Classification

Machine learning algorithms based on semi-supervised strategies have drawn the attention of researchers due to their ability to work with limited labeled data making use of huge number of unlabeled samples. Graph based semi-supervised algorithms make an assumption of similarity of examples in lower dimensional manifold and use an objective that ensures similarity of labels as enforced by the similarity graph. Such methods typically make use of a L2 regularization term to avoid over-fitting. Regularization term further ensures convexity of the overall objective leading to efficient learning algorithms. Addressing the problem of low-supervision and high class imbalance, prior work has shown state-of-the-art results for anomaly detection and other important classification problems by using a convex-concave objective. The current work analyses such performance improvements of convex-concave objective thoroughly. Our study indicates that a KL-Divergence based loss function for semi-supervised learning has performed much better than the convex-concave objective based on L2-Loss. It is also seen that the one-versus-rest setting for multi-class classification using convex-concave objective is performing much weaker compared to the naturally multi-class KL-Divergence based multi-class classification setting.
Strictly capturing non-strict closures

All functional languages need closures. Closure-conversion is a compiler transformation that embeds static code into the program for creating and manipulating closures, avoiding the need for special run-time closure support. For call-by-value languages, closure-conversion has been the focus of extensive studies concerning correctness, such as type preservation and contextual equivalence, and performance, such as space usage. Unfortunately, non-strict languages have been neglected in these studies. This paper aims to fill this gap.
Detecting Alzheimer Disease on FDG PET Images Using a Similarity Index Based on Mutual Information

Mutual information is an image similarity metric often used for the robust registration of multimodality images. The aim of this study is to investigate the use of a simple to implement similarity computation method based on a mutual information index for the automated detection of Alzheimer's disease from FDG PET studies. 102 healthy and 95 Alzheimer's disease FDG PET patient images from the online Alzheimer's disease Neuroimaging Initiative (ADNI) database were used to develop and test the system. Images were preprocessed for enabling comparison. An index was computed for each new image based on its degree of similarity to images belonging to AD patients versus healthy control patients. Classification was made based on the value of this index. The leave-one-out method was used for performance evaluation. Performance was evaluated using Receiver Operating Characteristic (ROC) curves. The diagnostic reliability given by the area under the curve (AUC) was determined as $0.857\pm 0.0261$. The results suggest that a mutual information based image similarity method can potentially be useful as a second opinion computer aided diagnostic (CAD) system providing verification to visual and black box approaches. The system does not need training with new data and does not require the computation of image features.
Normalization Techniques Comparison for QoS-based Web Services Selection by LSP Method

Service-oriented architecture (SOA) and Internet services technologies offer advanced solutions for creating distributed business processes and applications. At the same time, services must be available to a growing number of users and satisfy their requirements for both functional and non-functional properties of services. The problem of selecting services based on the quality of services is formulated as a multi-criteria decision-making (MCDM) problem. MCDM methods typically require preliminary normalization of the criteria, which in turn can significantly affect the results of solving a multi-criteria problem. In this paper, the effects of commonly known normalization techniques (Max-Min, Vector, Max and Sum) on the ranking Web services using a method of Logic Scoring of Preference (LSP) are investigated. As a result of the study, the use of the Vector and Sum techniques to normalize the criteria when using the LSP method is proposed.
Event Coreference Fusion Based on Deep Residual Network and Structure Representation

Event coreference fusion is a challenging task in natural language processing, which plays an important role in event extraction, question answering system and reading comprehension. In this paper, a deep residual network model Res-HASR with hierarchical attention and event structure representation is proposed for event coreference fusion. Res- HASR first uses bi-directional long-short-term memory(Bi- LSTM)network and hierarchical attention to obtain the important context information of event sentences; and deep residual network (ResNET) is used to obtain the deep semantic information of event sentences; then uses neural tensor network to calculate the co-occurrence relationship of entities and elements in event sentence and structure representation; then designs the element fusion algorithm to fuse the elements; and finally the events after fusion is used iteratively for event coreference resolution to further improve system performance. The experimental results illustrate that Res-HASR improves the performance of event coreference fusion significantly compared with the two basic models.
Application and Perspectives of Convolutional Neural Networks in Digital Intelligence

Convolutional neural networks are the state-of-the-art approach for advanced computer vision tasks, as they offer capabilities beyond straightforward application for image processing. This review provides an introduction to five areas where convolutional neural networks are a core topic of research: 2D and 3D object classification, image segmentation, few-shot learning, reinforcement learning and explainability of neural networks. Each section provides an introduction to the research topic, identifies the main research questions, and lists modern solutions to these problems.