Reviewerly/exHarmony huggingface dataset:
State estimation based on improved cubature Kalman filter algorithm

During the recursive calculate process of the cubature Kalman filter (CKF), the covariance matrix tends to lose positive definiteness and noise statistical characteristics are inaccurate, which results in inaccurate filtering or even filter divergence. This study presents an improved algorithm based on the CKF. The algorithm combines the square root filter algorithm and the Sage–Husa maximum a posterior noise estimator, which can ensure the non-negative determination and symmetry of the covariance matrix and has the ability to deal with unknown and time-varying noise statistical characteristics in the filtering process adaptively. In the multi-dimension system, the noise covariance matrix may dissatisfy non-negative definiteness and result in filter divergence, and then the noise covariance matrix estimator is improved. The analysis is verified by state estimation example of the non-linear system, compared with the standard CKF, the accuracy of the adaptive square root CKF (ASRCKF) state estimation is increased by 63.13, 63.88, and 42.71%, respectively. Finally, the effectiveness of the ASRCKF is verified by the state estimation of the permanent magnet linear synchronous motor.
Acquisition of Digital Images and Identification of Aedes Aegypti Mosquito Eggs using Classification and Deep Learning

The mosquito Aedes aegypti can transmit some diseases, which makes the study of the proliferation of this vector a necessary task. With the use of traps made in the laboratory, called ovitraps, it is possible to map egg deposition in a community. Through a camera, coupled with a magnifying glass, are acquired images containing the elements (eggs) to be counted. First, the goal is to find pixels with a similar color to mosquito eggs; for that, we take advantage of the slice color method. From these already worked images, a process of transfer learning with a convolutional neural network (CNN) is carried out. The intention is to separate which elements are eggs from the others. In 10% of the test images, the count performed by the model, and the ground truth of the number of eggs was considered weakly correlated. This problem occurs in images that have a high density of eggs or appear black elements that resemble mosquito eggs, but they are not. For the remaining 90% of the test images, the counting was considered to be perfectly correlated.
Who Goes There? Exploiting Silhouettes and Wearable Signals for Subject Identification in Multi-Person Environments

The re-identification of people in private environments is a rather complicated task, not only from a technical standpoint but also for the ethical issues connected to it. The lack of a privacy-sensitive technology to monitor specific individuals prevents the uptake of assistive systems, for example in Ambient Assisted Living and health monitoring applications. Our approach adopts a deep learning multimodal framework to match silhouette video clips and accelerometer signals to identify and re-identify the subjects of interest within a multi-person environment. Brief sequences, which may be as short as only 3 seconds, are encoded within a latent space where simple Euclidean distance can be used to discriminate the matching. Identities are only revealed in terms of accelerometer carriers, and the use of silhouettes instead of RGB signals helps to ring-fence privacy concerns. We train our method on the SPHERE Calorie Dataset, for which we show an average area under the ROC curve of 76.3%. We also propose a novel triplet loss for which we demonstrate improving performances and convergence speeds.
Improving the Robustness and Efficiency of Continuous Integration and Deployment

Modern software is developed at a rapid pace. To sustain that rapid pace, organizations rely heavily on automated build, test, and release steps. To that end, Continuous Integration and Continuous Deployment (CI/CD) services take the incremental codebase changes that are produced by developers, compile them, link, and package them into software deliverables, verify their functionality, and deliver them to end users. While CI/CD processes provide mission-critical features, if they are misconfigured or poorly operated, the pace of development may be slowed or even halted. To prevent such issues, in this thesis, we set out to study and improve the robustness and efficiency of CI/CD. The thesis will include (1) conceptual contributions in the form of empirical studies of large samples of adopters of CI/CD tools to discover best practices and common limitations, as well as (2) technical contributions in the form of tools that support stakeholders to avoid common limitations (e.g., data misinterpretation issues, CI configuration mistakes).
User abnormal behavior recommendation via multilayer network

With the growing popularity of online services such as online banking and online shopping, one of the essential research topics is how to build a privacy-preserving user abnormal behavior recommendation system. However, a machine-learning based system may present a dilemma. On one aspect, such system requires large volume of features to pre-train the model, but on another aspect, it is challenging to design usable features without looking to plaintext private data. In this paper, we propose an unorthodox approach involving graph analysis to resolve this dilemma and build a novel private-preserving recommendation system under a multilayer network framework. In experiments, we use a large, state-of-the-art dataset (containing more than 40,000 nodes and 43 million encrypted features) to evaluate the recommendation ability of our system on abnormal user behavior, yielding an overall precision rate of around 0.9, a recall rate of 1.0, and an F1-score of around 0.94. Also, we have also reported a linear time complexity for our system. Last, we deploy our system on the "Wenjuanxing" crowd-sourced system and "Amazon Mechanical Turk" for other users to evaluate in all aspects. The result shows that almost all feedbacks have achieved up to 85% satisfaction.
A High Efficiency Multistage Coder for Lossless Audio Compression using OLS+ and CDCCR method

In this paper, the improvement of the cascaded prediction method was presented. Three types of main predictor block with different levels of complexity were compared, including two complex prediction methods with backward adaptation, i.e., extension Active Level Classification Model (ALCM+) and extended Ordinary Least Square (OLS+). Our own approach to implementation of the effective context-dependent constant component removal block is also presented. Additionally, the improved adaptive arithmetic coder with short, medium and long-term adaptation was presented, and the experiment was carried out comparing the results with other known lossless audio coders against which our method obtained the best efficiency.
Mobile phone use impairs stair gait: A pilot study on young adults

Human movement control requires attention to accurately tune motor commands in response to environmental changes. Dual task paradigms are used to test the role of attention on motor performance. Usually the tasks used have little resemblance with every day experience. Here we ask: Does a common cognitive task, such as a mobile phone conversation, compromise motor performance on stairs? Eight young participants negotiated an instrumented seven-step staircase. Stair negotiation while talking on a mobile phone was compared to normal stair negotiation. Stepping parameters, jerk cost (measure of smoothness of locomotion) and step clearance were measured. When talking on a mobile phone, participants' overall body velocity (mean(sd): Ascent 0.534(0.026) vs 0.511(0.024) m/s, Descent 0.642(0.026) vs 0.511(0.024) m/s, No phone/Phone respectively) and cadence decreased significantly (Ascent 75.8(5.8) vs 65.6(4.4) steps/min, Descent 117.4(4.2) vs 108.6(6.0) steps/min, No Phone/Phone respectively). Pelvis and feet jerk cost also changed significantly, mostly decreasing with phone use. Foot clearance did not show significant changes between No Phone and Phone conditions. These pilot results show that, even for young, healthy and cognitively intact individuals, talking on a mobile phone whilst negotiating a staircase induces measurable changes in motor performance. Participants moved slowly but more smoothly, reducing the motor control cost, possibly at the expense of movement accuracy. The reduction in motor performance is likely to be due to the difficulty in integrating the two sub-tasks. These results suggest that even young, healthy individuals show stair gait impairment when simultaneously negotiating stairs and performing another cognitive task, such as talking on the phone.
Proof of Authenticity-Based Electronic Medical Records Storage on Blockchain

Electronic health records driving over the hype of digitalization are currently booming in many hospitals. Despite advancements, plethora of challenges such as data interconnectivity, interoperability, and data sharing arises due to hospitals with their own hospital management information system form clusters of data. These can be solved by effectively employing blockchain platform. The authors in this work are proposing a novel consensus algorithm titled Proof of Authenticity over the distributed platform for all medical stakeholders. Unlike the previous approaches, wherein researchers were the miners, this work illustrates a methodology to implement blockchain for health care, where the hospitals and clinics are assumed the roles of both miners and validators. The peer-to-peer network is leveraged with a designed smart contract that follows the proof of authenticity mechanism. The medical stakeholders will access the medical data under security protocols and patient’s consent in a tamper-proof network. The proposed work aims for more patient centric and transparent health care.
Software Defect Prediction using Hybrid Approach

Defective software modules have significant impact over software quality leading to system crashes and software running error. Thus, Software Defect Prediction (SDP) mechanisms become essential part to enhance quality assurance activities, to allocate effort and resources more efficiently. Various machine learning approaches have been proposed to remove fault and unnecessary data. However, the imbalance distribution of software defects still remains as challenging task and leads to loss accuracy for most SDP methods. To overcome it, this paper proposed a hybrid method, which combine Support Vector Machine (SVM)-Radial Basis Function (RBF) as base learner for Adaptive Boost, with the use of Minimum-Redundancy-Maximum-Relevance (MRMR) feature selection. Then, the comparative analysis applied based on 5 datasets from NASA Metrics Data Program. The experimental results showed that hybrid approach with MRMR give better accuracy compared to SVM single learner, which is effective to deal with the imbalance datasets because the proposed method have good generalization and better performance measures.
Software process anti-patterns catalogue

For software project managers and other practitioners, an important activity is to detect, and consequently find solutions to, insufficiencies and mistakes in process and other project management (PM) activities. Particularly interesting among these are anti-patterns: commonly occurring solutions with known negative effects. Their detection in running, as well as finished, projects is often hard as it needs to be performed by specialists, demands expertise and skill, and is prone to biases. Obtaining the expertise is a long-time effort and the sources of relevant knowledge are scattered and vary in the depth of treatment.
Deep Video Inverse Tone Mapping

Inverse tone mapping is an important topic in High Dynamic Range technology. Recent years, deep learning based image inverse tone mapping methods have been extensively studied and perform better than classical inverse tone mapping methods. However, these methods consider the inverse tone mapping problem as a domain transformation problem from LDR domain directly to HDR domain and ignore the relationship between LDR and HDR. Besides, when using these deep learning based methods to transform frames of videos, it will lead to temporal inconsistency and flickering. In this work, we propose a new way to consider the inverse tone mapping problem and design a deep learning based video inverse tone mapping algorithm to reduce the flickering. Different from previous methods, we first transform LDR resources back to approximate real scenes and use these real scenes to generate the HDR outputs. When generating HDR outputs, we use 3D convolutional neural network to reduce the flickering. We also use methods to further constrain the luminance information and the color information of HDR outputs separately. Finally, we compare our results with existing classical video inverse tone mapping algorithms and deep image inverse tone mapping methods to show our great performance, and we also prove the necessity of each part of our method.
Data acquisition methods using backup data decryption of Sony smartphones

As the storage capacity of smartphones increases, more user data such as call logs, SMS records, media data, and instant messages are stored in smartphones. Therefore, it is important in digital investigation to acquire smartphones containing the personal information of users. However, even when a prime suspect's smartphone is acquired, it is difficult to extract user data without obtaining root privilege. In this situation, smartphone backup data may be a valuable alternative to the extraction of user data. Using a smartphone backup, an investigator can extract most of the data stored in a smartphone including user data, with straightforward methods, and transfer them to a storage device such as an SD card, a USB, or a PC. Despite its convenience, backup data are hard to use as evidence, because backup data are encrypted using different methods depending on smartphone manufacturers, in order to protect user privacy. In this paper, we propose methods for decrypting encrypted backup data of Sony smartphones. In our analysis, we reverse-engineered the backup processes of the local backup and the PC backup provided by Sony smartphones, and analyzed the encryption methods applied to each set of backup data. In particular, we developed an algorithm for decrypting encrypted backup data on Sony smartphones, which we experimentally verified. As far as we know, this is the first research that has addressed the decryption of backup data on Sony smartphones.
Risking

Risks in several categories such as: tasks, technology, people, among others, can affect the management of software development projects, since risky situations can occur in any type of project. Therefore, risk management is a vital activity that allows carrying out strategic planning that reduces the probability of occurrence and the negative impact of the risks, leading to the success of the project and the development of quality computer systems. Risk Management is one of the topics taught in introductory and advanced classes of software engineering, commonly addressed through lectures and case discussions. New learning objects, such as serious games, can also be applied to assist in teaching risk management. This paper presents the proposal of the game Risking (Arriscando in Portuguese), a card game that motivates the student to relate risks of software projects with possible solutions. To obtain empirical evidence regarding the learning and experience evoked by the game, we used the game in three software engineering classes. To evaluate the game, we applied the MEEGA+ questionnaire and a Focus Group dynamic. The results suggest that the game was well accepted, and that the students approved the dynamic used, the social interaction and the learning through an entertaining activity.
A Virtual Makeup Augmented Reality System

Virtual makeup systems allow users to try makeup on remotely, without wasting products or spending time cleaning up later. Some virtual makeup systems just show the final makeup result on the user's face, while other systems allow some interaction when applying makeup on a photo. In this paper we introduce an augmented reality system that allows users to apply virtual makeup directly on their face using a physical applicator, simulating a virtual mirror experience. Facial features are detected and tracked using an RGBD camera and mapped to a normalized 2D facial mesh composed of 124 triangles. Finger touches on the face are also detected on the RGBD video stream, and used to store the applied makeup texture representation on the corresponding 2D facial triangle. Rendering the face with the virtual makeup is performed by back-projecting the makeup stored on the facial mesh to the image captured by the camera. Our initial prototype demonstrates the feasibility of our technique, that detects touches very accurately (about 2.2 mm), and that achieves real-time interactive performance (about 15 fps) when tracking and rendering makeup using a regular PC and an Intel RealSense RGBD camera.
The effects of Dzyaloshinskii–Moriya interaction on entanglement dynamics of a spin chain in a non-Markovian regime

We study the dynamics of entanglement in a one-dimensional spin-1/2 XX model with three-spin interaction (TSI) and Dzyaloshinskii–Moriya (DM) interaction. The revival of the quantum entanglement is an indication of non-Markovian dynamics, which is observed in the system with TSI interaction. Results of the research show that in the system with both TSI and Heisenberg interaction, there is a critical value in which a dynamical phase transition from the Markovian to the non-Markovian regime. The dynamic behavior of the correlation in the system with XX Heisenberg and DM interaction is non-Markovian in αc. In the system with TSI and DM interaction, in D≥J′, DM interaction reinforces the non-Markovian behavior of the correlation introduced by TSI interaction. Moreover, the results show that in system with TSI, XX Heisenberg, and DM interaction, if D=αc, the non-Markovian behavior of the system is improved.
Fictitious Personas for Interdisciplinary Team Alignment in the Requirements Elicitation Activities

Population aging is accompanied by several comorbidities that require the preparation of society to ensure a better quality of life for the elderly. From this scenario, interdisciplinary projects, which align researchers and professionals in the area of health and technology, arise to build solutions that encompass the needs of the elderly, as well as their families, caregivers, and health professionals. This paper shows an experience report on developing this kind of project focusing on the requirements elicitation activities. First, we present a description of the requirements elicitation stage with the identification process of 12 potential requirements for the platform using two techniques: workshop with stakeholders and analysis of functionalities of mobile applications that aimed at the elderlys health. Then, we report the difficulties of team members in reaching a consensus on the definition of end-users and the requirements to be prioritized in the platform to be developed. Lastly, we present our experience on using fictitious personas, created from statistical data and information provided by the health specialists of the elderly, can help the communication between the members of the project, promoting greater consensus and empathy of the interdisciplinary team, assisting and accelerating the decision making on the prioritization of requirements and the definition of end-user.
Comparing shape descriptor methods for different color space and lighting conditions

Abstract Detecting and recognizing objects is one of the most important uses of vision systems in nature and is consequently highly evolved. This paper aims to accurately detect an object using its shape and color information from a complex background. In particular, we evaluated our algorithm to detect 19 different integrated circuits (IC) from 10 different printed circuit boards (PCB) of different colors. We have compared three different shape descriptors for four different color space models. We have evaluated shape detection algorithms in different lighting conditions (indoor, outdoor, and controlled light source) to find suitable illumination for image acquisition. We undertook statistical hypothesis testing to find the effect of color space models and shape descriptors on the accuracy, false positive and false negative rates. While measuring accuracy, we have noted that L*a*b* color space is significantly worse, and the best result is obtained in YCbCr color space using bounding box shape descriptors for 2500 Lux using LED.
Framework for Blockchain Deployment: The Case of Educational Systems

Blockchain is an emerging technology that lacks sophisticated guidelines and frameworks for deployment purposes. This paper proposes a framework that helps in making suitable decisions concerning blockchain model adoption. In addition, the authors classified the major categories of blockchain metrics. Furthermore, the authors evaluated the proposed framework with two well-known educational blockchain-based models.
Multiple Multi-Spectral Remote Sensing Data Fusion and Integration for Geological Mapping

This paper investigates spaceborne multiple multispectral data-fusion and blending to generate an integrated data with higher spatio-spectral resolution and spectral coverage in order to obtain improved geological mapping. A hybrid approach using Gram-Schmidt pan-sharpening and Inverse Distance Weighting (IDW) based downsampling technique is developed to generate integrated data from multiple multispectral data. In this study, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Landsat 8, and Sentinel-2 data have been used to evaluate the developed approach for lithological mapping. Liikavaara to Puoltikasvaara including Nautanen and nearby-mining area, in the Gällivare district of Norrbotten county, Sweden, is chosen as a case study. Lithological map of the study area is produced using Support Vector Machine (SVM) classifier. Bedrock geological map from the Geological Survey of Sweden (SGU) is used for classification accuracy assessment. The results show that integrated data produced better accuracy than original individual spaceborne multispectral data for lithological mapping of the study area.
Mathematical model and numerical algorithms to analyze gas filtration process in a porous medium

Abstract The paper deals with the problem of gas filtration process in a porous medium using a mathematical model of an object; the model is described by a nonlinear partial differential equation and the corresponding boundary and internal conditions. The paper presents the main stages in construction of mathematical model of the process of gas filtration in porous media, taking into account the changes in hydrodynamic parameters of the object of study. To solve the above problem, the following numerical methods have been used: local-one-dimensional schemes and longitudinal-transverse direction schemes. To solve the problem of nonlinear gas filtration in a porous medium, several methods of constructing an iterative process have been checked. To study the responses of the principal parameters of the process, a series of computational experiments on a computer, their analyses and conclusions are given in the paper.