Reviewerly/exHarmony huggingface dataset:
The Evolutionary Trajectory of the Agile Concept Viewed from a Management Fashion Perspective

Agile is one of the most popular contemporary management concepts and buzzwords. This paper provides an in-depth examination of the influence of the Agile concept on the discourse, thinking and practices of organizations worldwide. The paper traces the emergence and evolution of the Agile concept from inception to the present by synthesizing findings from a wide range of academic and practitioner-oriented sources. Overall, the picture that emerges from the analysis is that the Agile concept has grown considerably in popularity and has become one of the most dominant concepts in public management discourse. The popularization of Agile has, to a large extent, been driven by an active supply-side made up of actors such as consultants, coaches, and trainers. Another finding is that the Agile concept has evolved considerably over time, from its initial presentation as a narrow and specialized concept rooted in the software development community to a much broader and general approach applicable across nearly all types of organizations and industries. The broadening of the concept has led to neologisms such as Agile Marketing, Agile Government, and Agile Management. The paper ends with reflections on the current status of Agile and some speculation about the concept’s likely future trajectory.
One scan based high average-utility pattern mining in static and dynamic databases

High average utility pattern mining has been proposed to overcome the demerits of high utility pattern mining. Since high average utility pattern mining can extract more valuable patterns than high utility pattern mining, many related researches are being actively conducted. However, most studies in high average utility pattern mining have only focused on mining in static databases not dynamic databases. In addition, the methods of previous studies with dynamic databases consume a huge runtime and memory space due to the inefficient processes and structures. To overcome these problems, we present a novel high average utility pattern mining approach from the dynamic databases. The proposed mining approach reads a database only once and adopts a new data structure called a HAUP-List to store information of patterns more compactly. In addition, in order to reflect the incremental environments, a restructure process is designed to handle the newly inserted data. Thus, our approach can extract high average utility patterns more efficiently than the suggested methods in previous works in dynamic databases. Various experiments are conducted to demonstrate the performance of the proposed approach using both real and synthetic datasets. Results of these experiments show that the proposed mining approach outperforms the other state-of-the-art high average utility pattern mining approaches in dynamic databases.
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference

We introduce a new, rigorously-formulated Bayesian meta-learning algorithm that learns a probability distribution of model parameter prior for few-shot learning. The proposed algorithm employs a gradient-based variational inference to infer the posterior of model parameters for a new task. Our algorithm can be applied to any model architecture and can be implemented in various machine learning paradigms, including regression and classification. We show that the models trained with our proposed meta-learning algorithm are well calibrated and accurate, with state-of-the-art calibration and classification results on three few-shot classification benchmarks (Om- niglot, mini-ImageNet and tiered-ImageNet), and competitive results in a multi-modal task-distribution regression.
Breast Cancer:Tumor Detection in Mammogram Images Using Modified AlexNet Deep Convolution Neural Network

The improvement of system accuracy is a key issue in the detection and classification of tumors in digital mammographic images. This affects how radiologists make accurate analysis in the diagnosis of breast cancer. The goal of this research is to use augmentation techniques to improve system classification accuracy on a large number of datasets. A popular deep convolutional neural network (DCNN) architecture known as AlexNet was modified and used to categorize mammography images into two classes of benign (normal) and malignant (abnormal) tumors. The results demonstrated an overall system accuracy of 95.70%. It indicates an improved performance over traditional approaches in breast cancer diagnosis.
mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations via Metagraph Embedding

Given that heterogeneous information networks (HIN) encompass nodes and edges belonging to different semantic types, they can model complex data in real-world scenarios. Thus, HIN embedding has received increasing attention, which aims to learn node representations in a low-dimensional space, in order to preserve the structural and semantic information on the HIN. In this regard, metagraphs, which model common and recurring patterns on HINs, emerge as a powerful tool to capture semantic-rich and often latent relationships on HINs. Although metagraphs have been employed to address several specific data mining tasks, they have not been thoroughly explored for the more general HIN embedding. In this paper, we leverage metagraphs to learn relationship-preserving HIN embedding in a self-supervised setting, to support various relationship mining tasks. In particular, we observe that most of the current approaches often under-utilize metagraphs, which are only applied in a pre-processing step and do not actively guide representation learning afterwards. Thus, we propose the novel framework of mg2vec, which learns the embeddings for metagraphs and nodes jointly. That is, metagraphs actively participates in the learning process by mapping themselves to the same embedding space as the nodes do. Moreover, metagraphs guide the learning through both first- and second-order constraints on node embeddings, to model not only latent relationships between a pair of nodes, but also individual preferences of each node. Finally, we conduct extensive experiments on three public datasets. Results show that mg2vec significantly outperforms a suite of state-of-the-art baselines in relationship mining tasks including relationship prediction, search and visualization.
The Role of Blockchain in Documenting Land Users' Rights: The Canonical Case of Farmers in the Vernacular Land Market

In this article, we discuss the potential of blockchain technology in addressing the documentation of users’ land rights in the informal land rental market. Blockchain technology is a peer-to-peer protocol that can be leveraged to keep track of transactions over the internet. Publicised for its use in the bitcoin revolution, the technology provides transparency and traceability that can be used in the management of land rights. When it comes to the formalisation of land rights, blockchain technology promises to authenticate owners and other users of land, and provides a fixed ledger of land use rights transactions. At present, blockchain technology is being explored as a proof of concept in several countries to track land titles (state to individual). We extend the idea to capture the granting of land use rights (individual to individual) making use of the decentralisation, peer-to-peer nature of blockchain technology. While the technology is not a panacea to all land administration challenges, it can offer an effective means to manage land transactions, provide digital documentation to actors in the informal land rental market and reduce inefficiency in land systems. However, the uptake of the technology in land administration is limited by human related factors. These limitations include, but are not limited to, the accuracy of data being entered into the system, the ability of the system to facilitate data preservation, pre-existing institutional and legal pillars, and the digital divide across communities. Part of overcoming these barriers requires the political will of governments to invest in digital technologies and develop institutional capacities to overcome current limitations to bring land management into the industry 4.0 era.
A place next to Satoshi: foundations of blockchain and cryptocurrency research in business and economics

Abstract Blockchain technology has become an ubiquitous phenomenon. While the topic originated in computer science, the business and economics literature was comparatively slow to pick up on it. To better understand the academic basis, current developments and future research avenues of the discourse, 9672 cited references of 467 blockchain and cryptocurrency articles from the fields of business and economics are gathered from the Web of Science Core Collection and are analyzed. Five major strands of research are identified through factor analysis. They are reviewed and their interrelation is mapped using social network analysis. Research on (I) market efficiency and economics and (II) asset pricing and valuation is relatively mature and focuses on cryptocurrencies, while research on (III) the principles and applications of blockchain technology, (IV) transactions and anonymity and (V) monetary theory and policy lacks maturity. Potential paths for future research are pointed out and in conclusion, it is assessed that this young field of research still leaves plenty of room for manoeuvre. A scientific place next to Nakamoto (2008) is still available for existing, emerging and new research streams.
Econundrum

While there is a strong relationship between climate change and human food consumption, it is challenging to understand the implications and impact from an individual perspective. The lack of a shared frame of reference, that allows people to compare their impact to others, limits awareness on this complex topic. To support group reflections and social comparison of the impact of people's food consumption on climate change, we designed Econundrum, a shared physical data sculpture that visualizes carbon emissions resulting from dietary choices of a small community. Our three-week field study demonstrates how Econundrum helped people (i) understand the climate impact of various food types, (ii) reflect on the environmental impact of their food choices; and (iii) discuss the relation between climate impact and food consumption with others. Our study shows how a shared physical data sculpture mediates a complex topic to a community by facilitating the social dynamics in context.
AsNet: Asymmetrical Network for Learning Rich Features in Person Re-Identification

Learning part-based features with multiple branches has been proven as an effective way to deliver high performance person re-identification. Existing works mostly exploit extra constraints on different branches to ensure the diversity of extracted features, which may lead to the increased complexity in network architecture and the difficulty for training. In this letter, we propose a quite simple multi-branch structure consisting of a global branch as well as a part branch in an asymmetrical way. We empirically demonstrate that such simple architecture can provide surprisingly high performance without imposing any extra constraint. On top of this, we further prompt the performance with a lightweight implementation of attention module. Extensive experimental results prove that the proposed method, termed Asymmetrical Network (AsNet), outperforms state-of-the-art methods with obvious margin on standard benchmark datasets such as Market1501, DukeMTMC, CUHK03. We believe that AsNet can serve as a strong baseline for related research and the source code is publicly available at https://github.com/www0wwwjs1/asnet.git.
Traffic Analysis Attacks on Tor: A Survey

The Tor anonymity network is one of the most popular and widely used tools to protect the privacy of online users. Tor provides defenses against multiple adversarial activities aiming to identify or trace the users. Traffic analysis is a very strong tool that can be used for internet surveillance. Traffic analysis attacks against Tor's anonymity network has been known as an open question in research. Moreover, the low-latency feature Tor tries to provide to its users imposes limitations in defending against traffic analysis attacks. In our study, we examine traffic analysis attacks from the perspective of the adopted adversary model and how much it fits within Tor's threat model. The purpose of this study is to evaluate how practical these attacks are on real-time Tor network.
Conformal prediction: A unified review of theory and new challenges

In this work we provide a review of basic ideas and novel developments about Conformal Prediction — an innovative distribution-free, non-parametric forecasting method, based on minimal assumptions — that is able to yield in a very straightforward way prediction sets that are valid in a statistical sense also in the finite sample case. The discussion provided in the paper covers the theoretical underpinnings of Conformal Prediction, and then proceeds to list the more advanced developments and adaptations of the original idea.
In-Air Continuous Writing Using UWB Impulse Radar Sensors

We developed an impulse radio ultra-wideband (IR-UWB) radar-based system that can recognize alphanumeric characters in midair without the need for any handheld device. The hardware consists of four IR-UWB radar sensors set up with a rectangular geometry. Writing a single character in midair results in artifacts that make some characters look similar on a position trajectory-based (x, y) plane, which makes them difficult to classify. Thus, we developed an algorithm that transforms 2D coordinate image data into trigonometric ratios (i.e., tangents) and plots them against the time axis to obtain unique images for training a convolutional neural network. An extended Kalman filter is used to obtain the 2D trajectories of hand motions. To evaluate our proposed method, we first applied it to characters that may be written in midair very simply without creating artifacts and compared its performance with that of a state-of-the-art digit classification algorithm. Then, we considered combining characters written midair with and without artifacts. After the individual character recognition, we combined the characters into words. We defined a specific marker based on an energy threshold to detect the start and end of a character for midair writing. The energy level was found to change drastically when the hand is pulled in and out of the radar plane. The proposed method was found to outperform the current state of the art at character classification when artifacts are present in the images.
A new image denoising framework using bilateral filtering based non-subsampled shearlet transform

In this paper, we propose an advanced framework for image denoising using bilateral filtering based non-subsampled shearlet transform (NSST). Initially, we apply the NSST to decompose noisy input image into high and low frequency coefficients. The weighted bilateral filter (WBF) is then used to remove the noise from the low frequency coefficients while; thresholding is used to remove noise from the high frequency coefficients. The outputs of both the process are combined to form the resultant image. Finally, the inverse NSST is applied on the resultant output to estimate the final denoised image. To ensure validity of the proposed model, we conduct several experiments by considering different grayscale images with various noise variances. The qualitative and quantitative comparison is illustrated and it shows the improved performance of the proposed method as compared to the other conventional image denoising methods. Mathematical and simulation results are presented to show the validity of our work.
Data Quality and Explainable AI

In this work, we provide some insights and develop some ideas, with few technical details, about the role of explanations in Data Quality in the context of data-based machine learning models (ML). In this direction, there are, as expected, roles for causality, and explainable artificial intelligence . The latter area not only sheds light on the models, but also on the data that support model construction. There is also room for defining, identifying, and explaining errors in data, in particular, in ML, and also for suggesting repair actions. More generally, explanations can be used as a basis for defining dirty data in the context of ML, and measuring or quantifying them. We think dirtiness as relative to the ML task at hand, e.g., classification.
Augmented-Reality-Based Visualization of Navigation Data of Mobile Robots on the Microsoft Hololens - Possibilities and Limitations

The demand for mobile robots has rapidly increased in recent years due to the flexibility and high variety of application fields comparing to static robots. To deal with complex tasks such as navigation, they work with high amounts of different sensor data making it difficult to operate with for non-experts. To enhance user understanding and human robot interaction, we propose an approach to visualize the navigation stack within a cutting edge 3D Augmented Reality device -the Microsoft Hololens. Therefore, relevant navigation stack data including laser scan, environment map and path planing data are visualized in 3D within the head mounted device. Based on that prototype, we evaluate the Hololens in terms of computational capabilities and limitations for dealing with huge amount of real-time data. Results show that the Hololens is capable of a proper visualization of huge amounts of sensor data. We demonstrate a proper visualization of navigation stack data in 3D within the Hololens. However, there are limitations when transferring and displaying different kinds of data simultaneously.
Mitigating Adversarial Attacks on Medical Image Understanding Systems

Deep learning systems are now being widely used to analyze lung cancer. However, recent work has shown a deep learning system can be easily fooled by intentionally adding some noise in the image. This is called as Adversarial attack. This paper presents an adversarial attack for malignancy prediction of lung nodules. We found that the adversarial attack can cause significant changes in lung nodule malignancy prediction accuracy. An ensemble-based defense strategy was developed to reduce the effect of an adversarial attack. A multi-initialization based CNN ensemble was utilized. We also explored adding adversarial images in the training set, which eventually reduced the rate of mis-classification and made the CNN models more robust to an adversarial attack. A subset of cases from the National Lung Screening Trial (NLST) dataset were used in our study. Initially, 75.1 %, 75.5% and 76% classification accuracy were obtained from the three CNNs on original images (without an adversarial attack). Fast Gradient Sign Method (FGSM) and one-pixel attacks were analyzed. After the FGSM attack, 46.4%, 39.24%, and 39.71 % accuracy was obtained from the 3 CNNs. Whereas, after a one pixel attack 72.15%, 73%, and 73% classification accuracy was achieved. FGSM caused much more damaged to CNN prediction. With a multi-initialization based ensemble and including adversarial images in the training set, 82.27 % and 81.43 % classification accuracy were attained after FGSM and one-pixel attacks respectively.
Fall Monitoring for the Elderly Using Wearable Inertial Measurement Sensors on Eyeglasses

Inertial measurement unit (IMU) sensors are widely used in many wearable systems for detecting falls in real-time and reducing the risks associated with the fall in all instances of accidents. This letter presents a highly portable and comfortable IMU-sensor-based fall detection system for elderly people. Using an accelerometer and a gyroscope, the proposed system can monitor the sudden rise of the acceleration and the orientation of the user's head to detect accidental falls. When an accidental fall is detected, the proposed system transmits alarm messages to the data server via the wireless network. Also, a complementary filter is adopted to eliminate the instability and drift of angular measurement by the accelerometer and the gyroscope, respectively. The functionality of the proposed system is verified in experiments that involve 120 falling and 450 nonfalling actions of five participants. The experimental results indicate that the proposed system achieves a fall detection accuracy of 95.44% and has the potential to assist in the everyday healthcare of elderly people.
The smart feature phone revolution in developing countries: Bringing the internet to the bottom of the pyramid

Until recently, the only way for the population of developing countries to access the Internet was through expensive smartphones, designed in and for developed countries. In the past few years, however, a major new innovation has emerged, the smart feature phone with Internet connectivity, which was specifically designed for those with low incomes in developing countries. This paper explains the development process for the smart feature phone, how this has influenced the nature and extent of adoption, and its use by low-income groups, including their demonstrated preference for uses related to entertainment rather than more traditional “work-related” goals. The focus is on the case of India, where the JioPhone has already reached millions of people with low incomes.
12×12 S-box Design and its Application to RGB Image Encryption

Customarily, 8×8 S-box is a 16×16 look up table over Galois field GF28, algebra over the binary field F2. A 12×12 S-box over GF212 is not workable as the computer memory does not manage it. The finite chain ring F2[x]<x12> is fundamentally an algebra over the binary field F2. In this paper we considered the advantage and devise a novel method of 12×12 S-box design over the unit elements of finite chain ring F2[x]<x12>. The newly obtained S-box has greatly higher confusion ability than any of 8×8 S-box. To observe this effect, an RGB image encryption application is given. In the proposed encryption scheme, we doubled the bits of 12×12 S-box from 12 to 24 for creating confusion in the data. Consequently, a new method of color image encryption is designed by which 24 binary bits are utilized at the place of byte. Whereas for diffusion we apply the linear permutation P=i×32mod257 and finally bitwise Exclusive-or operation is operationalized. Analysis reveals that the proposed image encryption approach might switch many 8×8 S-box based color image encryption schemes.