Reviewerly/exHarmony huggingface dataset:
State Of the Earthquake Field Disaster Investigation Information Service System

Earthquake field work is complex and urgent, and efficient and orderly emergency work plays an important role in reducing casualties and economic losses. Scientific and reasonable working mode and intelligent information technology means are the key to ensure the efficient and orderly development of seismic field work. At present, domestic and foreign standards and systems for seismic field work have been developed and established, and disaster investigations and assessment software based on mobile terminals have been developed. On the basis of explaining the importance of earthquake field emergency work, this paper summarizes and compares the domestic and foreign earthquake field work modes and disaster investigation information service software, introduces the application of various software and its shortcomings, and discusses the development direction of disaster investigation information system in earthquake field work.
Image generation from bounding box-represented semantic labels

Image generation with pixel-wise semantic information is suitable for the development of adversarial learning techniques. In this study, we propose a method for synthesizing objects with class-specific textures and fine-scale details based on bounding box-represented semantic labels. To achieve this goal, we note that the traditional generative adversarial network (GAN) uses noise as an input to generate realistic images with sufficient textures and details, but it cannot be guided by specific targets and requirements. By contrast, conditional GAN (cGAN) can involve various types of guiding information but it often ignores specific textures and details, thereby leading to less realistic results and low resolution. Thus, we propose a new translator-enhancer framework by combining cGAN and GAN to achieve high quality image generation. cGAN is used as a translator to match the semantic constraints whereas GAN is employed as an enhancer to provide details and textures. We also propose a new form of semantic label map as an input, which is represented by instance-level bounding boxes rather than segmentation masks. The semantic label map represented by bounding boxes makes it easier for users to provide the inputs and it also gives greater flexibility when generating object boundaries. The results obtained from qualitative and quantitative experiments showed that our method can generate realistic images of objects with semantic labels represented by bounding boxes. Our method can be used to generate images of novel scenes to support learning tasks during training with various scenes, which are difficult to capture in the real world.
Implementation of Backpropagation Neural Network and Extreme Learning Machine of pH Neutralization Prototype

This paper presents a comparison between Backpropagation Neural Network and Extreme Learning Machine for pH neutralization process. The system has one input variable and two output variables. The input is pH value in neutralization container and the outputs are time on solenoid valve of acid solution and time on solenoid valve of base solution. There is a sequence system in pH neutralization process to regulate the flow of liquids under certain conditions, so that the liquid does not exceed the maximum capacity if the pH has not reached the setpoint. The performance analysis is done for Backpropagation Neural Network and Extreme Learning Machine implementation in neural region by keeping setpoint 7. From the implementation result, it is found that Backpropagation Neural Network gives better result when compared with Extreme Learning Machine for pH neutralization prototype.
Simple continuous optimal regions of the space of data

The definition of efficient programs for both maintenance and optimization is a struggling task in many industrial sectors. In this context, data analysis can significantly improve the state-of-the-art techniques, employed, for instance, to determine if a particular component or product is showing an anomalous behavior with respect to a defined nominal state. In fact, through the analysis of data collected on field, it is possible to detect optimal operating regions and to detect anomalies in advance. In this context, we propose a multi-purpose algorithm for unsupervised or semi-supervised learning in order to determine a simple continuous region of points. This region can be adopted in order to describe a component or a product nominal behavior and can be used in order to detect anomalies which are outside it. Such a region can be defined adopting a finite ensemble of thresholds, whose value can be physically interpreted. In order to show the effectiveness of our approach, the proposed method has been tested in an Anomaly Detection problem concerning Predictive Maintenance, exploiting data coming from a naval vessel, characterized by a combined diesel-electric and gas propulsion plant.
Static and Dynamic State Predictions for Acoustic Model Combination

Acoustic model combination (AMOC) is an active research area. Model combination techniques are critical for many automatic speech recognition (ASR) scenarios, and provide frameworks to combine diverse acoustic models to boost ASR performance. We scope this work in the broad framework of AMOC, and present static and dynamic state combinations of acoustic models. We motivate and rationalize the benefits from our combination techniques, and present many applications and extensions. We apply our work in the context of combining a generic and a scenario-specific (dedicated) acoustic model; we train the proposed model with an ASR objective to best align with ASR performance. We conduct our experiments on large-vocabulary ASR task with over 30k hours of training data. Compared to generic model, we demonstrate a strong 6% word error relative reduction (WERR) in average across a variety of tasks, and specifically 25% and 8% WERR for far-field speaker and an emerging car scenario.
Machine learning and corporate bond trading

We demonstrate how machine learning based recommender systems can be effectively employed by market makers to filter the information embedded in Requests for Quote (RFQs) to identify the set of clients most likely to be interested in a given bond, or, conversely, the set of bonds that are most like ly to be of interest to a given client. We consider several approaches known in the literature and ultimately suggest the so-called latent factor collaborative filtering as the best choice. We also suggest a scalable optimization procedure that allows the training of the system with a limited computational cost, making collaborative filtering practical in an industrial environment.
A Fast Approach of Provisioning Virtual Machines by Using Image Content Similarity in Cloud

To quickly provision multiple virtual machines (VMs) is a challenge in nowadays cloud data centers (CDCs).By utilizing the content similarity among the virtual machine image (VMI) files, the amount of data transferred in the VM provisioning is reduced, and hence, the provisioning time can be shortened.Thus, minimizing the total amount of transferred VMI file data is helpful for accelerating the VM provisioning.Meanwhile, packing the VMs into the minimum number of physical machines (PMs) is also crucial for the CDCs.To solve these two problems at the same time, we propose a heuristic algorithm, called fast balance placement (FBP), by utilizing several tables to precompute and store the similarity relationships among different VMI files.Comparing to the balance-placement algorithm, the simulation results show that FBP uses less PMs to pack the VMs and its running time is shorter, and it transfers almost the same amount of the VMI file data.
Enterprise Agility: A Balancing Act - A Local Government Case Study

Abstract Austerity and financial constraints have been threatening the public sector in the UK for a number of years. Foreseeing the threat of continued budget cuts, and addressing the situation many local councils face, requires internal transformations for financial stability without losing the key focus on public service. Agile transformations have been undertaken by organisations wanting to learn from the software development community and bringing agile principles into the wider organisation. This paper describes and analyses an ongoing behaviour-led transformation in a district council in the UK. It presents the results of the analysis of 19 interviews with internal stakeholders at the council, of observations of meetings among senior and middle management in a five-month period. The paper explores the successes and the challenges encountered towards the end of the transformation process and reflects on balancing acts to address the challenges, between: disruption and business as usual, empowerment and goal setting, autonomy and processes and procedures, and behaviours and skills. Based on our findings, we suggest that behaviours on their own cannot guarantee a sustained agile culture, and that this is equally important for enterprise agility and for large-scale agile software development transformations.
Semantic Super-resolution for Extremely Low-resolution Vehicle License Plate

Vehicle license plate (VLP) super-resolution (SR) is of great demand in intelligent traffic systems. Super-Resolution for extremely low-resolution VLP remains challenging and the state-of-the-art SR methods hardly provide satisfying results for low-resolution (LR) VLPs. In this study, from a new perspective, we develop an effective solution to achieve the super-resolution of the extremely LR VLP images, by using the semantic information of the characters. Specifically, we firstly exploit the pervasive sparse prior for the character recognition in LR condition for VLPs. Then the semantic information extracted from the sparse representation-based classification (SRC) results is employed to alleviate the illness of the SR problem. To maximize the benefit brought by the semantic information from SRC, we employ sparse-coding based super-resolution (SCSR) method to upscale VLP images. In the end, an exponential soft labeling method is designed to reduce the possible bias introduced by character classification. Extensive experiments on the self-built Chinese VLP dataset (VLP100) and public UFPR-ALPR dataset validate the feasibility and effectiveness of our proposed VLP-SR system.
An Eclipse Plug-in Tool for Generating Test Cases from Source Codes

Most software development will use a standard software development life cycle (SDLC). In SDLC, four phases are involved during development of any system. They are analysis, design, implement and testing. During testing phase, test cases are used in order to test the functionalities of the system developed. Test cases are generally extracted from the source codes manually. This paper presents an alternative approach to automate the process of generating the test cases by developing an eclipse plug-in tool. The plug-in tool will be able to generate the test cases automatically from the source codes by examining its line of code (LOC). The plug-in tool is an open source software to help the software developers generate the test cases for their system that they developed. It will help to ease the problems for software developers to come up with test cases manually as well as to check the system functionalities whether it meets the users' requirements or not.
Damping modes of harmonic oscillator in open quantum systems

Through a set of generators that preserves the hermiticity and trace of density matrices, we analyze the damping of harmonic oscillator in open quantum systems into four modes, distinguished by their specific effects on the covariance matrix of position and momentum of the oscillator. The damping modes could either cause exponential decay to the initial covariance matrix or shift its components. They have to act together properly in actual dynamics to ensure that the generalized uncertainty relation is satisfied. We use a few quantum master equations to illustrate the results.
A Deep-narma Filter for Unusual Behavior Detection from Visual, Thermal and Wireless Signals

Detection of unusual behavior is an important topic in signal and image processing. Because of the topic's complexity, addressing it as a solely RGB video analysis problem raises significant challenges. This has resulted in approaches that aim at exploiting different data modalities that can overcome the inherent restrictions of unimodal techniques. Moreover, the classification outcome of such approaches is affected not only by the input data, but also by previous classification history. To this end, this paper introduces a novel deep-NARMA filter that extends a typical CNN architecture, and endows it with autoregressive moving average behavior. In addition, it incorporates a data fusion framework that supplements RGB video streams, with thermal capturing and information about the distortion of WiFi signal reflectance. Experimental results indicate a better performance compared to conventional as well as deep learning approaches.
A novel 2D partial unwinding adaptive Fourier decomposition method with application to frequency domain system identification

This paper proposes a two‐dimensional (2D) partial unwinding adaptive Fourier decomposition method to identify 2D system functions. Starting from Coifman in 2000, one‐dimensional (1D) unwinding adaptive Fourier decomposition and later a type called unwinding AFD have been being studied. They are based on the Nevanlinna factorization and a maximal selection. This method provides fast‐converging rational approximations to 1D system functions. However, in the 2D case, there is no genuine unwinding decomposition. This paper proposes a 2D partial unwinding adaptive Fourier decomposition algorithm that is based on algebraic transforms reducing a 2D case to the 1D case. The proposed algorithm enables rational approximations of real coefficients to 2D system functions of real coefficients. Its fast convergence offers efficient system identification. Numerical experiments are provided, and the advantages of the proposed method are demonstrated.
Super-resolution reconstruction of speckle phase in depth-resolved wavelength scanning interference using the total least-squares analysis

Depth-resolved wavelength scanning interferometry (DRWSI) is a tomographic imaging tool that employs phase measurement to visualize micro-displacement inside a sample. It is well known that the depth resolution of DRWSI is restricted by a wavelength scanning range. Recently, a nonlinear least-squares analysis (NLS) algorithm was proposed to overcome the limitation of the wavelength scanning range to achieve super-resolution; however, the NLS failed to measure speckle surfaces owing to the sensibility of initial values. To the best of our knowledge, the improvement of depth resolution on measuring a speckle surface remains an open issue for DRWSI. For this study, we redesigned the signal processing algorithm for DRWSI to refine the depth resolution when considering the case of speckle phase measurement. It is mathematically shown that the DRWSI's signal is derived as a model of total least-squares analysis (TLSA). Subsequently, a super-resolution of the speckle phase map was obtained using a singular value decomposition. Further, a numerical simulation to measure the micro-displacements for speckle surfaces was performed to validate the TLSA, and the results show that it can precisely reconstruct the displacements of layers whose depth distance is 5 μm. This study thus provides an opportunity to improve the DRWSI's depth resolution.
Simplified Kripke-Style Semantics for Some Normal Modal Logics

Pietruszczak (Bull Sect Log 38(3/4):163–171, 2009. https://doi.org/10.12775/LLP.2009.013) proved that the normal logics $$\mathrm {K45}$$, $$\mathrm {KB4}$$ ($$=\mathrm {KB5}$$), $$\mathrm {KD45}$$ are determined by suitable classes of simplified Kripke frames of the form $$\langle W,A\rangle $$, where $$A\subseteq W$$. In this paper, we extend this result. Firstly, we show that a modal logic is determined by a class composed of simplified frames if and only if it is a normal extension of $$\mathrm {K45}$$. Furthermore, a modal logic is a normal extension of $$\mathrm {K45}$$ (resp. $$\mathrm {KD45}$$; $$\mathrm {KB4}$$; $$\mathrm {S5}$$) if and only if it is determined by a set consisting of finite simplified frames (resp. such frames with $$A\ne \varnothing $$; such frames with $$A=W$$ or $$A=\varnothing $$; such frames with $$A=W$$). Secondly, for all normal extensions of $$\mathrm {K45}$$, $$\mathrm {KB4}$$, $$\mathrm {KD45}$$ and $$\mathrm {S5}$$, in particular for extensions obtained by adding the so-called “verum” axiom, Segerberg’s formulas and/or their T-versions, we prove certain versions of Nagle’s Fact (J Symbol Log 46(2):319–328, 1981. https://doi.org/10.2307/2273624) (which concerned normal extensions of $$\mathrm {K}5$$). Thirdly, we show that these extensions are determined by certain classes of finite simplified frames generated by finite subsets of the set $$\mathbb {N}$$ of natural numbers. In the case of extensions with Segerberg’s formulas and/or their T-versions these classes are generated by certain finite subsets of $$\mathbb {N}$$.
Automatically Identifying of animals in the wilderness

The evolution of machine learning and computer vision in technology has driven a lot of improvements and innovation into several domains. We see it being applied for credit decisions, insurance quotes, malware detection, fraud detection, email composition, and any other area having enough information to allow the machine to learn patterns. Over the years the number of sensors, cameras and cognitive pieces of equipment placed in the wilderness have been growing exponentially. However, the resources(human) to leverage these data into something meaningful are not improving at the same rate. For instance, a team of scientist volunteers took 8.4 years, 17000 hours at a rate of 40 hours/ week to label 3.2 million images from the Serengeti wild park for our research, we are going to focus on wild data, and keep proving that deep learning can do better and faster than the human equivalent labour for the same task. Moreover, this is also an opportunity to present some custom Capsule Networks architectures to the deep learning community while solving the above-mentioned critical problem. Incidentally, we are going to take advantage of these data to make a comparative study on multiple Deep learning models. Specifically, VGG-net, RES-net and a custom made Convolutional-Capsule Network. We benchmark our work with the Serengeti project where Mohammed Mohammed Sadegh et al. recently published a 92% top-1 accuracy [15] and Gomez et al. had a 58% top-1 accuracy [8]. We successfully reached 96.4% top-1 accuracy on the same identification task. Concurrently, we reach up to 79.48% top-1 testing accuracy on a big complex dataset using capsule network, which out-perform the best results of Capsule networks on a complex dataset from Edgar Xi et al. with 71% testing accuracy [5,23,18].
Integrating Agile Practices into Architectural Assumption Management

Although managing architectural assumptions can benefit software development in several aspects (e.g., reducing architectural misunderstanding and mismatch), the effort required is a key obstacle towards employing architectural assumption management in practice. One potential solution is to apply agile practices in order to reduce this effort. To this end, we conducted a survey with 91 practitioners to investigate the possibility of integrating agile practices into architectural assumption management in industrial practice. The results offer an overview of which agile practices can be integrated in architectural assumption management and how. Six agile practices were selected by more than half of the subjects: "Backlog", "Iterative and Incremental Development", "Refactoring", "Continuous Integration", "Effective Communication", and "Just Enough Work". Twelve agile practices were further elaborated by the subjects regarding how they can be used in architectural assumption management. Based on the survey results, we developed a classification of agile practices for agile architectural assumption management, which can act as a reference for researchers and practitioners to employ certain agile practices in architectural assumption management.
Enhancing Domain-Specific Supervised Natural Language Intent Classification with a Top-Down Selective Ensemble Model

Natural Language Understanding (NLU) systems are essential components in many industry conversational artificial intelligence applications. There are strong incentives to develop a good NLU capability in such systems, both to improve the user experience and in the case of regulated industries for compliance reasons. We report on a series of experiments comparing the effects of optimizing word embeddings versus implementing a multi-classifier ensemble approach and conclude that in our case, only the latter approach leads to significant improvements. The study provides a high-level primer for developing NLU systems in regulated domains, as well as providing a specific baseline accuracy for evaluating NLU systems for financial guidance.
Gamification of Wearable Data Collection

Games are designed to build on certain inherently reward centered aspects of our psychology. This encourages the user to keep playing and engaging in the entertainment for just a bit longer. In this paper we explore the way in which gamification can be used to benefit researchers aiming to collect large amounts of data from sometimes less than enthusiastically motivated participants.
Cyberbullies, Trolls, and Stalkers: Students’ Perceptions of Ethical Issues in Social Media

Little research has been done to examine users’ perceptions of ethical issues related to communication on social media. This exploratory, descriptive study examines undergraduate students’ perceptions of ethical issues in how they and their peers use social media and the consequences the ethical issues have on themselves and others. An online survey was distributed to students at a regional university (N = 201). Results show that most students have observed violations of ethical norms; they believe that the most common ethical issues are inappropriate pictures, privacy concerns, and people harassing others.
Robust View Synthesis in Wide-baseline Complex Geometric Environments

One of the most challenging problems of novel view synthesis is to predict the scene in complex geometric environments. Existing methods depend on either homography optimization or deep neural networks. In this paper, we provide a framework of view synthesis, which includes grid-based warp, depth refinement and ghost artifacts removal. The depth refinement method is our main contribution, which can be combined with any other warp operation to generate high quality images. To achieve it, the depth refinement method is combined with a shape-preserving warp operation together based on reliable, half-reliable and unreliable superpixel discrimination. We remove outliers in half-reliable superpixels by considering their neighboring reliable superpixels and distinguish half-reliable ones into reliable and unreliable parts. This step helps us to get more accurate depth information. Experimental results show that our view synthesis system has nearly 0.7dB gains in PSNR and 0.03 gains in SSIM compared with the state-of-the-art view synthesis algorithm.