Reviewerly/exHarmony huggingface dataset:
A new method for the identification of archaeological soils by their spectral signatures in the vis-NIR region

This paper introduces a statistical method to identify spectral signatures of buried archaeological remains and distinguish them from spectra of the background soil in the visible to near infrared region. The proposed method is based on the Principal Component Analysis (PCA). The difference between an archaeological spectrum and non-archaeological soil spectra is quantified by a so-called R value. R values larger than 1 indicate that the spectrum represents an archaeological material. The method is successfully applied to samples from five study sites in Italy and Hungary with special conditions. The reflection spectra are taken in a time-efficient way with a field spectrometer. The method works best if background non-archaeological soil spectra are gathered from the same area, around the targeted archaeological site. Also, it can work without such local background spectra (but with lower accuracy) using background spectra from existing spectral libraries. This indicates that the method can, in principle, be applied to any archaeological site which is spectrally distinct from its surroundings. The paper highlights that this method does not require high spectral resolution and thus has the advantage of using low spectral resolution spectrometers which can eventually be applied for continuous 2D imaging applications with high temporal resolution. Additional studies are needed to further improve the method and to investigate under which conditions it works well or only with limited accuracy.
Life, Liberty, and the Pursuit of Happiness: Values for Music Education

Abstract In this article, the meaning of values of life, liberty, and the pursuit of happiness advanced in the Declaration of Independence (1776) are critically examined from musical and educational perspectives. These values are posited not only as American values but as human values. Their theoretical and practical implications for music education are unpacked and their implications for democratic music education are assessed.
BIOT: Explaining multidimensional nonlinear MDS embeddings using the Best Interpretable Orthogonal Transformation

Dimensionality reduction (DR) is a popular approach to data exploration in which instances in a given dataset are mapped to a lower-dimensional representation or “embedding.” For nonlinear dimensionality reduction (NLDR), the dimensions of the embedding may be difficult to understand. In such cases, it may be useful to learn how the different dimensions relate to a set of external features (i.e., relevant features that were not used for the DR). A variety of methods (e.g., PROFIT and BIR) use external features to explain embeddings generated by NLDR methods with rotation-invariant objective functions, such as multidimensional scaling (MDS). However, these methods are restricted to two-dimensional embeddings. In this paper, we propose BIOT, which makes it possible to explain an MDS embedding with any number of dimensions without requiring visualization.
Immersive IDE

Positive affects have been shown to be positively correlated with developer productivity. Moreover, the environment in which the developer sits is important as it has a direct bearing on her emotions. However, in typical software development project setups, it is difficult to mold the surroundings of a developer according to her own needs or wishes. Moreover, large project areas may tend to have multiple distractions that may further negatively affect the developer. Multiple studies have shown that Virtual Reality can be an effective medium to induce positive emotions, with the capability to immerse oneself into virtually created environs, allowing for countless opportunities to surround a developer with what she would want. In this paper, we present our approach to allow a developer to choose her own surrounding environment to work in (say a beach, a park or in space!), while allowing for a real-time feed of her workstation/tools to be embedded in the same for a seamless experience. We believe that this approach will enable better mood/engagement and lower distractions/stress, which may lead to higher productivity and a balanced sense of developer well-being.
Leveraging test logs for building a self-adaptive path planner

Recent approaches in testing autonomous driving systems (ADS) are able to generate a scenario in which the autonomous car collides, and a different ADS configuration that avoids the collision. However, such test information is too low level to be used by engineers to improve the ADS. In this paper, we consider a path planner component provided by our industry partner, that can be configured through some weights. We propose a technique to automatically re-engineer the path planner in terms of a self-adaptive path planner (SAPP) following the MAPE loop reference architecture. The Knowledge Base (KB) of SAPP contains descriptions of collision scenarios discovered with testing, and the corresponding alternative weights that avoid the collisions. We forecast two main usages of SAPP. First of all, designers are provided with a prototype that should facilitate the re-implementation of the path planner. As second usage, SAPP can be useful for improving the diversity of testing, as performing test case generation on SAPP will guarantee to find dangerous situations different from those used to build SAPP. Preliminary experiments indicate that SAPP can effectively adapt on the base of the solutions stored in KB.
An Application of ASP in Nuclear Engineering: Explaining the Three Mile Island Nuclear Accident Scenario

Abstract The paper describes an ongoing effort in developing a declarative system for supporting operators in the Nuclear Power Plant (NPP) control room. The focus is on two modules: diagnosis and explanation of events that happened in NPPs. We describe an Answer Set Programming (ASP) representation of an NPP, which consists of declarations of state variables, components, their connections, and rules encoding the plant behavior. We then show how the ASP program can be used to explain the series of events that occurred in the Three Mile Island, Unit 2 (TMI-2) NPP accident, the most severe accident in the USA nuclear power plant operating history. We also describe an explanation module aimed at addressing answers to questions such as “why an event occurs?” or “what should be done?” given the collected data.
Face Aging on Realistic Photo in Cross-Dataset Implementation

Abstract Face aging or age progression is a prediction on how a person looks at the future. Face aging image-to-image translation is a process of translating an image of young people to their older version or vice versa. The need for a paired training dataset to train the generative adversarial networks (GANs) is a major problem with face aging image-to-image translation. Nowadays, there is a method where an unpaired training dataset can be used to do an image-to-image translation. CycleGANs is a GANs extended methods where there is no need for paired training dataset to train the CycleGANs. From the result, it shows that CycleGANs can do face aging image-to-image translation without using the paired training dataset.
Deep Convolutional Neural Network for Real and Fake Face Discrimination

With the progress of society, and the rapid development of science and technology, information identification security issues have become more important. With the use of technologies such as Generative Adversarial Networks (GAN) to generate the generalization of faces, in order to make the face identification system more secure, it is necessary to detect the fake face. With the maturity of artificial intelligence technology, face identification technology is widely used in various fields of real life, especially in the research of identifying computer generated faces. This paper is aimed at discriminating the generation of face problems. An algorithm based on Deep Convolutional Neural Network (DCNN) for neural network architecture for face image style classification is proposed. And provide experimental basis. The results show that the method can obtain satisfactory results, and the average accuracy is above 99.24%.
The State of Mobile UX: Best Practices From Industry and Academia

In this paper we review current literature from both academia and industry to distill best practices for mobile user experience design. We find there is much work to be done to solidify best practices for this emerging conversation, but that several practices have congealed around the broad categories of procedures for designing mobile applications and procedures for ensuring a high-quality user experience within mobile applications. Ultimately, we define a variety of best practices, from methods of assessing user goals and values to designing for routine versus interrupted usage, while pointing to limitations and the need for a wide variety of future mobile user experience research.
KeySLAM: Robust RGB-D Camera Tracking Using Adaptive VO and Optimal Key-Frame Selection

We propose a novel RGB-D camera tracking system that robustly reconstructs hand-held RGB-D camera sequences. The robustness of our system is achieved by two independent features of our method: adaptive visual odometry (VO) and integer programming-based key-frame selection. Our VO method adaptively interpolates the camera motion results of the direct VO (DVO) and the iterative closed point (ICP) to yield more optimal results than existing methods such as Elastic-Fusion. Moreover, our key-frame selection method locates globally optimum key-frames using a comprehensive objective function in a deterministic manner rather than heuristic or experience-based rules that prior methods mostly rely on. As a result, our method can complete reconstruction even if the camera fails to be tracked due to discontinuous camera motions, such as kidnap events, when conventional systems need to backtrack the scene.
Increasing Visibility of Open Access Materials in a Library Catalog: Case Study at a Large Academic Research Library

This article describes efforts to increase the visibility of open access materials in the catalog of a major research library. The authors describe their reasons for embarking on this work, improvements made, and challenges they encountered.
Efficient Reconstruction of Industrial Images Using Optimized HMK Splines

In the context of the fourth industrial revolution (also referred to as Industry 4.0), industrial image processing forms a valuable basis for newly conceived applications and services, especially image reconstruction as one of the key technologies of industrial image processing plays an important role in industrial engineering. To develop an effective method for industrial image reconstruction, this article proposes the optimized hierarchical many-knot (HMK) splines (abbreviated as OHMK splines) method. In the scheme, a fine merged control lattice is derived from the hierarchy of coarse-to-fine control lattices, and then the final reconstruction function obtained by OHMK splines is the function generated on the merged fine control lattice, which is not obtained by superimposing all functions generated on the coarse-to-fine control lattices. Therefore, the computation for the final reconstruction function relies on the number of control points in the merged fine control lattice, not depending on all the control points in the hierarchy. Through the experimental results, we can find that the good reconstruction performance for the given image can be obtained by using the OHMK splines method, and the computational overhead of the OHMK splines method is superior to that of the HMK splines method.
It’s Not Easy Being Agile: Unpacking Paradoxes in Agile Environments

Abstract In this paper, we outline inherent tensions in Agile environments, which lead to paradoxes that Agile teams and organizations have to navigate. By taking a critical perspective on Agile frameworks and Agile organizational settings the authors are familiar with, we contribute an initial problematization of paradoxes for the Agile context. For instance, Agile teams face the continuous paradox of ‘doing Agile’ (= following an established Agile way of working) versus ‘being Agile’ (= changing an established Agile way of working). One of the paradoxes that organizations face is whether to start their Agile journey with a directed top-down (and therefore quite un-Agile) ‘big bang’ or to allow an emergent bottom-up transformation (which may be more in-line with the Agile spirit but perhaps not be able to overcome organizational inertia). Future research can draw on our initial problematization as a foundation for subsequent in-depth investigations of these Agile paradoxes. Agile teams and organizations can draw on our initial problematization of Agile paradoxes to inform their learning and change processes.
Accelerating Computer Vision Algorithms on Heterogeneous Edge Computing Platforms

Heterogeneity has become the cornerstone of modern embedded System-on-Chips (SoCs), used in latest smart-phones and edge computing platforms to achieve high performance under tight power budgets. Alongside the general purpose multi-core CPUs, such SoCs typically integrate several specialized processing elements such as GPU and DSP to ensure power-efficient execution of specific workloads. In the past, many computer vision algorithms and their kernels have been shown to benefit from execution on GPUs, both in terms of performance and power consumption. Existing work has also demonstrated the benefit of accelerating them simultaneously on multi-core CPUs and discrete as well as integrated GPUs found in PCs and workstations. Recently, authors have also focused on accelerating such applications on heterogeneous embedded SoCs with integrated CPU and GPU. In this paper, we first present an extensive literature review of such efforts and highlight their strengths and limitations. Next, we use the latest state-of-the-art edge computing platform, Odroid-N2, and the older Odroid-XU3 platform, both of which use heterogeneous embedded SoCs, to explore the acceleration of the convolution kernel with different filter sizes and its impact on the KLT tracking algorithm. Lastly, we discuss the challenges and opportunities in leveraging such SoCs for computer vision and other AI applications.
Making Decision on Sharing Forensic Data with the Fuzzy Logic Approach

Internet of Things (IoT) devices advance continuously and with such development so do the issues and complexity in their forensic investigation. Worldwide investigators face challenges in retrieving evidence from the IoT environment, which includes Devices, Networks and the Cloud. Creating a report which then can be presented in the court of law is one of the most crucial steps during such forensic investigations. In this article, we propose a Hybrid Forensic IoT Server model which registers devices and stores their data for examination purposes. Furthermore, we developed models to calculate values for report accuracy, evidence accuracy and investigator expertise as well as a new way of decision-making in forensic data sharing utilising the fuzzy logic decision making system.
A permissioned blockchain approach to electronic health record audit logs

In recent years, electronic health records (EHRs) have been subject to data breaches; this indicates that the current EHR infrastructure is no longer suitable for safeguarding health records. Audit logs are one of the key processes in identifying culprits responsible for these data breaches. Audit logs are often tampered with to cover a criminal's tracks. Blockchain technology could provide a tamper-evident audit log process to improve the current EHR infrastructure. Hyperledger Fabric has been identified as a permissioned blockchain technology suited towards use-cases that require privacy. An experiment was conducted with Hyperledger Fabric to demonstrate how permissioned blockchain technology could be used to generate an audit trail for an EHR system. The proposed blockchain infrastructure is presented as a data-flow diagram and smart contracts were employed to explore the capabilities permissioned blockchain technology could provide in terms of improving the EHR audit log process. The result of the experiment revealed that permissioned blockchain technology could serve as an alternative to the audit log process employed by traditional EHRs.
Extended Abstract: Plain Language in the Sciences: A Qualitative Meta-Study of Research

Research on plain-language communication has been conducted across many fields, making it difficult for technical communication scholars and practitioners to engage with it. This presentation provides the preliminary findings of a qualitative meta-analysis that compiles and synthesizes this wide scope of recent research. The session focuses on a subset of the larger project: plain language in the sciences. I will first present a summary of the research from the sciences, mapping questions and findings related to plain language, its effectiveness, and its goals. Second, I consider this research in light of technical communication's calls toward social justice and the role plain language may play in those efforts. I look at the ways these outside studies conceptualize users and whether they account for the social, material, and historical contexts of user communities through the use of plain language.
Adaptive Anchor-Based Partial Multiview Clustering

Partial multiview clustering, which aims to effectively merge multiple prespecified incomplete views to improve clustering performance, is a research hotspot and difficulty in the field of machine learning.Guo et al. proposed a partial multiview clustering method (APMC) based on anchor graph, which uses a Gauss kernel function to solve the similarity matrix.The Gaussian kernel function is sensitive to parameter σ , and it is difficult to find the optimal value only by stepwise adjustment in practical applications.This undoubtedly affects the practicality of the APMC algorithm.To address this issue, an adaptive partial multiview clustering method based on anchor graph (AAPMC) is developed in this paper, which proposes an adaptive neighbor assignment strategy and utilizes it to improve the anchor-based similarity matrix computation of each view.The method proposed in this paper uses an adaptive method to solve the similarity matrix, eliminating the tediousness of parameter adjustment.In addition, the non-iterative method based on anchors is used to solve the optimal solution with low time complexity and is suitable for large-scale datasets.In short, our method is simple and effective, and it is easier to implement in practice.Extensive experiments show that our model can not only effectively solve the parameter setting problem, but also performs better than the state-of-the-art partial multiview clustering methods.
SoCeR: A New Source Code Recommendation Technique for Code Reuse

Motivated by the idea of reusing existing source code from previous projects within a software company, in this paper, we present a new source code recommendation technique called "SoCeR" to help programmers find relevant implementations or sample code based on software requirement specifications. SoCeR assists programmers to search existing code repositories using natural language query. Our proposed approach summarizes Python code into sentences or phrases to match them against user queries. SoCeR extracts and analyzes the content of the code (such as variables, functions, docstrings, and comments) to generate code summary for each function which is then mapped to the respective functions. For evaluation purposes, we developed a web-based tool for users to enter a textual search query and get the relevant code search results that were most relevant to the query. In SoCeR, users can also upload new code to enrich the code base with tested code. If adopted, then SoCeR will benefit a software company to build a trusted code base enabling large-scale software code reuse.
Research on Security Protection Measures of the Perception Layer of Power Distribution Internet of Things

In view of the current low power consumption and low storage characteristics of sensors in the perception layer of power distribution Internet of Things, this paper analyzes the information security features and risks of the perception layer. Combined with the different security requirements of various business data of the power distribution network, it provides a suitable lightweight security protection method for various types of business of the power distribution Internet of Things.
AMP: An Adaptive Multipath TCP for Data Center Networks

MPTCP and its ECN-capable variants such as XMP and DCM have recently been introduced to effectively exploit the path diversity of modern data center networks (DCNs). Although these multipath schemes improve overall network throughput compared to single-path schemes due to their fast, host-based, load balancing ability, they failed to address the following two problems: TCP incast and last hop unfairness. Firstly, these mechanisms cause frequent TCP incast collapses when used for workloads with a many-to-one communication pattern, commonly found in DCNs. Secondly, the last hop unfairness problem severely violates network fairness as single-path flows achieve 2-5 times less throughput than multipath flows. To effectively tackle these problems, we propose the Adaptive MultiPath (AMP) congestion control mechanism that quickly detects the onset of these problems and transforms its multipath flow into a single-path flow. Once these problems disappear, AMP safely reverses this transformation and continues data transmission via multiple paths. Our evaluation results under a diverse set of scenarios in a large-scale fat-tree topology demonstrate that AMP is robust to the TCP incast problem and improves network fairness between multipath and single-path flows significantly with no performance loss.
The development of a sepedi text generation model using long-short term memory

Recurrent Neural Network (RNN) is a type of deep neural network that is developed to generate a set of text given the features to produce the output based on features provided to the model. RNN is difficult to train when dealing with long memory dependencies. Long-short term memory (LSTM) is a recurrent neural network technique that works with memory cells which were developed to deal with the difficulties that were experienced by RNN. The system for word generation in text messages deduce the words based on statistical analysis from a large corpus. Moreover, the word in a sentence depends on its context which requires the use of machine learning algorithms to generate word sequences. The system to generate Sepedi text has not been developed especially using RNN techniques. The aim of the study is to use the LSTM RNN technique to generate Sepedi language text sequences. The data used for the experiments is a version of the NCHLT Sepedi text corpus. We obtained the accuracy of 50.3% with limited data and only 20 epochs.
Rateless Codes for Near-Perfect Load Balancing in Distributed Matrix-Vector Multiplication

Large-scale machine learning and data mining applications require computer systems to perform massive matrix-vector and matrixmatrix multiplication operations that need to be parallelized across multiple nodes. The presence of stragglers - nodes that unpredictably slowdown or fail - is a major bottleneck in such distributed computations. We propose a rateless fountain coding strategy to address this issue. Our idea is to create linear combinations of the m rows of the matrix and assign these encoded rows to different worker nodes. The original matrix-vector product can be decoded as soon as slightly more than m row-vector products are collectively finished by the nodes.We show that our approach achieves optimal latency and performs zero redundant computations asymptotically. Experiments on Amazon EC2 show that rateless coding gives as much as 3× speed-up over uncoded schemes.