Miracle-dz/UniFAR-FTUs huggingface dataset:
A Machine Learning Approach to Water Leak Localization

A smart water management system is proposed in this paper to identify leakages and predict the location of leakages in pipelines. The system determines leakages by utilizing the flow rates of water in pipelines and predicts the location of the leakages by applying machine learning (ML) techniques. To predict the location of the leakages in the pipeline, different ML approaches have been developed and tested. A comparison of these models is performed to obtain the best model for location prediction. A prototype has been developed in STAR-CCM+, a Computational Fluid Dynamics (CFD) software, to test the proposed system. The results show that amongst the machine learning based location prediction models, the Multi-Layer Perceptron (MLP) performs the best with an accuracy of 94.47% and an F1 score of 0.95.
Cooperation or Conflict in Doctor-Patient Relationship? An Analysis From the Perspective of Evolutionary Game

The uncoordinated and conflicting relationships between doctors and patients are becoming a real dilemma faced by the medical industry and the whole society, which severely affects people’s sense of well-being and health. Based on the multiple dimensions of trust, information asymmetry, and moral hazard, we use evolutionary game theory and replicating dynamic equations to construct the evolutionary game model, in the model, doctors and patients could select cooperation strategy or conflict strategy. Through an in-depth study on the model and the model’s simulation, we find that the doctor-patient relationship will eventually form a zero-sum game or a win-win situation. As for which situation is stable, it is closely related to the initial parameters of the evolutionary game model and the payment matrix of the evolutionary game. Increasing the trust degree, reducing the degree of information asymmetry and moral hazard would help doctors and patients shift their strategic choices from conflict to cooperation. We also find that increasing the trust degree, reducing the degree of information asymmetry, and reducing the degree of the patients’ moral hazard could promote the cooperation level effectively. The study aims to ease the contradiction between doctors and patients, solve the current doctor-patient dilemma, and provide a particular reference for building a new doctor-patient cooperation relationship.
Multiple Authentic Project-Based Experiences and Persistent Learning?

This work-in-progress paper in the research category investigates students’ experiences of learning over two project-based courses, of which the first is taken during bachelor studies and the second is taken during master studies. The research goal was to explore if and how the first project experience was considered beneficial to the latter. A pilot interview was conducted and analyzed for qualitatively different themes. Transformative learning theory (TLT) is introduced as the theoretical framework because of its focus on persistent learning originating in transformations in meaning perspectives. The results are discussed in the context of TLT, and they inform curriculum design and research on project-based learning.
Approaches for diagrams accessibility for blind people: a systematic review

This research category full paper brings a landscape of the primary studies which aim to turn diagrams accessible for blind people. There are a considerable number of people with visual impairments. However, was noticed poor accessibility features in tools to work with diagrams, which are typically used to present concepts and structured data, especially for courses in STEM fields. Thus, this systematic literature review gathered the studies about this matter in the last six years, to reveal the approaches being used to make diagrams accessible for blind people, checking, in addition to perception, if interaction and collaboration have been considered. The initial search and screening resulted in 369 studies, out of which 26 were selected for the final analysis. The results presented the preponderance of the auditory approach followed by refreshable Braille displays to achieve perception. Concerning interaction, the keyboard was the main input interface, whereas four out of nine studies found considered it. From the four studies which treated the possibility of simultaneous users working on the diagrammatic representation, only one considered distributed collaboration. Finally, the conclusion is that may there is a research gap in the collaboration, which could guide further developments.
Knowledge Graph Enhanced Third-Party Library Recommendation for Mobile Application Development

With the prevalence of smart terminal devices and the rapid development of the mobile Internet, mobile application markets become increasingly prosperous. Third-party libraries have played an essential role in mobile application development. These libraries can shorten development time, increase development efficiency, and improve development quality. Currently, a large number of third-party libraries have been published, which puts a heavy burden on developers in selecting appropriate libraries. Towards this issue, in this paper, we propose a novel third-party library recommendation approach by integrating topic modeling and knowledge graph techniques. In the topic modeling component, we extract topics from textual application descriptions and make recommendations based on libraries used by applications that share similar topics with the new application to develop. In the knowledge graph component, we leverage knowledge graph to incorporate structured information of third-party libraries and applications, as well as the interaction information of applications and libraries for the recommendation. Experiments conducted on a real-world dataset show that our proposed approach outperforms several state-of-the-art approaches in terms of recommendation performance.
A Novel Approach to Client-Side Monitoring of Shared Infrastructures

Modern computing is migrating towards more prevalent use of shared infrastructures and is transforming physical and fixed resources into a more dynamic and virtualized environment. This trend has proved to be a boon for advanced monitoring and profiling tools and approaches available for infrastructure operators and service providers. On the other end of the spectrum, the clients have been provided with previously unavailable flexibility and simple access to vast resources at the price of being unable to detect or correctly locate issues caused by lower layers of the new complex but abstracted infrastructure. This issue is most visible with performance degradation caused by other tenants of the same infrastructure, tenants formally invisible for other end-users. Although performance interference in shared infrastructures is a known problem that is covered by research aimed at detection and mitigation by infrastructure operators, there is a lack of research aimed at end-users and their ability to detect and locate such performance degradation. This paper covers the areas of shared networking, virtualized compute and grid compute infrastructures, gives an analysis of the specifics of each of the infrastructures, presents a novel approach for client-side monitoring and discusses the practical implementation issues. The system presented in the paper aims to provide end-users with valuable insight into underlying physical infrastructure without burdening them with complex and heterogeneous nature of monitored devices by reducing the shared infrastructure to a single representative generated node simply accessible as a generic SNMP (Simple Network Management Protocol) enabled networked device. The primary aim of the system is to enable end-users to detect and locate the domain of the issue causing the performance interference and not to replace or reimplement existing general -purpose monitoring systems. The system was tested on a regional e-Infrastructure within VI-SEEM and NI4OS-Europe projects where it performed as expected.
Simultaneous Mid-Infrared Gas Sensing and Upconversion Based on Third Harmonic Generation in Cascaded Waveguides

The performance of conventional gas sensors based on light absorption in the mid-infrared are limited by the high-cost and low efficiency of photon detection at these wavelengths. In this paper, cascaded suspended waveguides are proposed and analyzed for mid-infrared gas sensing with enhanced detection limit. The cascaded structure contains two sections in which the first part is optimized for light absorption and the other one is tailored to satisfy the phase matching condition for third harmonic generation toward near-infrared wavelengths. In this configuration, the input mid-infrared light firstly experiences “fingerprint” frequency absorption in the on-chip gas chamber. Consequently, the residuary light produces third harmonic radiation in the second section. Benefiting from the nonlinear relation between pump and harmonic power, the sensitivity of the sensor is significantly improved. Moreover, the signal is up converted from mid-infrared to near-infrared and thus it can be easily detected by efficient near-infrared detectors. The results show that the detection limit can reach the order of nmol/L and the absorption lengths can be reduced to three times shorter comparing to direct mid-infrared detection. The proposed configuration has great potential for high performance on-chip gas sensing.
ROBÔ-TI: Educational Robotics and Project-Based Learning Stimulating High School Students in the Information Technology Area

This Research to Practice Full Paper presents a Brazilian Project called ROBÔ-TI promoted by Federal Institute of Amazonas (IFAM). Brazil has a huge shortage of qualified professionals in the area of Information Technology (IT). This is further aggravated in the state of Amazonas, located in a forest region. So, ROBÔ-TI proposes reducing the deficit of professionals in these areas in future, developed with the objective of encouraging High School students from the public schools of the State of Amazonas to careers in IT area. The project was directed to promote in High School students a greater interest in careers in this area, in order to meet the needs of the job market. The use of Educational Robotics (ER) within the context of Project-Based Learning (PBL) was the strategy used in order to motivate students to continue their studies in IT career. In total there were 7 cities besides the state capital (Manaus), a total of 170 students and 8 teachers involved in the project. The PBL was used in a context of team formation for a playful competition at the end of the project, with Scratch programming language and LEGO® Mindstorms® EV3 Kit. The results obtained demonstrate the positive contribution of this project for the professional and technological training of students and teachers.
E-Field Analysis of a 3D Capacitive Power Transfer Configuration with Single Source Excitation

This paper presents the electric field analysis of a 3D capacitive power transfer configuration with three orthogonal transmitter metal plates. By analyzing the contribution of each transmitter plate and vector addition, the E-field at an arbitrary point in the 3D space is determined, and a closed form equation for calculating the electric field intensity at the geometrical center of the 3D space is developed. When a single voltage source excitation is applied to all three plates, the simulation results show that the magnitude and direction of the resultant E-field at the central point of the 3D space are in a good agreement with the theoretical results. Experimental demonstration with LED loads shows that E-field is mainly distributed along diagonal direction inside the define 3D space, which is consistent with theoretical predictions.
Strong consistency of the distributed stochastic gradient algorithm

With the development of computer science and communication, the sensor networks are widely applied due to the advantages of flexibility, fault tolerance, and ease of deployment. In this paper, a distributed stochastic gradient (SG) algorithm is proposed where the distributed estimators are aimed to collectively estimate an unknown time-invariant parameter from a set of noisy measurements obtained by distributed sensors. The proposed distributed SG algorithm combines the consensus strategy of the estimation of neighbors with the diffusion of regression vectors. The cooperative excitation condition is introduced, under which the strong consistency can be established for the distributed SG algorithm, without relying on the independency and stationarity assumptions of regression vectors which are commonly used in existing literature.
Medical Data Sharing Scheme Based on Attribute Cryptosystem and Blockchain Technology

Electronic medical data have significant advantages over paper-based patient records when it comes to storage and retrieval. However, most existing medical data sharing schemes have security risks, such as being prone to data tampering and forgery, and do not support the ability to verify the authenticity of the data source. To solve these problems, we propose a medical data sharing scheme based on attribute cryptosystem and blockchain technology in this paper. First, the encrypted medical data are stored in the cloud, and the storage address and medical-related information are written into the blockchain, which can ensure efficient storage and eliminate the possibility of irreversible modification of the data. Second, the proposed scheme combines attribute-based encryption (ABE) and attribute-based signature (ABS), which achieves the sharing of medical data in many-to-many communications. The ABE achieves data privacy and fine-grained access control, and the ABS verifies the authenticity of the source of the medical data while protecting the signer’s identity. Moreover, the data user outsources most of the operations of medical data ciphertext decryption to the cloud service provider (CSP), which can greatly reduce the computational burden. Finally, results of the analysis show that our scheme satisfies the requirements for confidentiality and unforgeability in the random oracle model, and that the proposed scheme offers higher computational performance than other similar schemes.
A novel Emotion Recognition Model for the Visually Impaired

Visually impaired people lead a tough life, and not being able to see is least of their problems. There are so many experiences a visually impaired person fails to witness in his/her life, the colors, the imagery, faces of their loved ones and many more. So many visually impaired people go through the course of their life without ever witnessing a vision of a smile or happiness in the eyes of their loved ones. Encountering instances of smiling faces of loved ones bring out an inner joy in every being’s soul. Warmth of a smile and glee of happiness can only be felt truly if shared with a loved one. In an effort to aid visually impaired people feel this warmth and help them experience a fulfilled version of life, a template emotion detection system is proposed.This model attempts to bridge the emotional gap between a visually impaired individual and his/her adored one by utilizing a wearable to impart the sentiments of bliss and happiness. For the scope of this system, the visually impaired individual as our essential subject and are on the other hand utilizing the word - primary subject alternatively is being considered. Elaborately, this wearable recognizes the electrical activity in a third individual's cerebrum and predicts utilizing machine learning the happy quotient and buzzes the primary subject’s wearable identifying vicinity and context in the surroundings at whatever point the third person smiles or is happy with regards to that conversation. This paper intends to outline a reasonable template for discovery of electrical activity in the third person’s brain and detect pleasant feelings in him/her utilizing a headgear to notify the primary subject of a pleasant/happy feeling in other individual's mind trying to enhance nature of discussions and thus improve quality of life.
Discrete update pose filter on the special Euclidean group SE(3)

This paper proposes two variants of the Geometric Approximate Minimum Energy (GAME) filter on the Special Euclidean Group SE(3) in the case that exteroceptive measurements are obtained in discrete time. Continuous-discrete versions of the GAME filter are provided that near-continuously predict pose and its covariance using high frequency interoceptive measurements and then update these estimates utilizing low frequency exteroceptive measurements obtained in discrete time. The two variants of the proposed filter are differentiated in their derivation due to the choice of affine connection used on SE(3). The proposed discrete update filters are derived based on first principles of deterministic minimum-energy filtering extended for discrete time measurements and derived directly on SE(3). The performance of the proposed filters is demonstrated and compared in simulations with a short discussion of practical implications of the choice of affine connection.
Frequency 16 Tupling Technique with the use of Four Parallel Polarization Modulators

We have proposed a novel architecture using polarization modulators in the generation of a millimeter wave signal. The proposed technique does not employ a filter and is frequency tunable. Drawbacks of existing Mach Zehnder Modulator based techniques have been effectively alleviated. The obtained Optical Sideband Suppression Ratio (OSSR) and Radio Frequency Spurious Suppression Ratio (RFSSR) are quite high around 62 dB and 36 dB respectively. Simulation results have been verified by theoretical computations. Analysis has been carried out and it has been found that degradation caused by offsets in phase shifts from their ideal values is negligible.
Exploring Latent Information for Unsupervised Person Re-Identification by Discriminative Learning Networks

For unsupervised domain adaption in person re-identification (Re-ID) tasks, the generally used label estimation approaches simply use the global features or the uniform part features. They often neglect the variations of samples having the same identity caused by occlusion, misalignment and uncontrollable camera settings. In this paper, we propose a discriminative learning network with target domain latent information (LatentDLN) to enhance the generalization ability of the Re-ID model. Specifically, to generate a discriminative and robust representation, two types of latent information in the samples from the target domain are explored by the multi-branch deep structure. First, the key points based valid region information is used to leverage the local and global cues in human body, and then a heuristic distance metric learning method based on the global features and the local features is proposed to effectively evaluate the similarity between different images. Second, the camera style transferred images are used as augmentation data to bridge the gap between different cameras in target domains. Moreover, the re-rank mechanism based on reciprocal neighbors is designed to improve the quality of the label estimation. Experimental results on Market-1501, DukeMTMC-ReID and MSMT17 datasets validate the significant effectiveness of the proposed LatentDLN for unsupervised Re-ID.
Coupled-Line-Based Ka-Band CMOS Power Dividers

We present the design and analysis of coupled-line-based <italic>Ka</italic>-band CMOS power dividers. A single spiral structure with a symmetrical layout is adopted to achieve a miniature area and small amplitude imbalance (AI) and phase difference (PD) performance. The first divider (divider-1) achieves <inline-formula> <tex-math notation="LaTeX">${S} _{21}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${S} _{31}$ </tex-math></inline-formula> of −4.029 and −4.008 dB, respectively, at 33 GHz. The corresponding AI and PD are −0.021 dB and 0.369°, respectively. Moreover, the second divider (divider-2) achieves <inline-formula> <tex-math notation="LaTeX">${S} _{21}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${S} _{31}$ </tex-math></inline-formula> of −4.093 and −4.055 dB, respectively, at 33 GHz. The corresponding AI and PD are −0.038 dB and 0.148°, respectively. The chip area is <inline-formula> <tex-math notation="LaTeX">$1.7\times 10^{-4} \lambda _{0}^{2}$ </tex-math></inline-formula>, one of the smallest normalized chip area ever reported for millimeter-wave (mm-wave) dividers. The prominent results of the dividers indicate that they are suitable for power division/combination in mm-wave transceivers.
Optimal Energy aware Dynamic Virtual Machine consolidation in Cloud Data Centers

Cloud computing has become successful in provisioning of different types hosted services through internet. In order to provide Quality of Service(QoS), all the cloud service providers were looking to ensure availability of resources all the time. This will have direct impact on power consumption of physical hosts in data centers and CO2 emissions. VM consolidation is a one of the most useful solution to reduce energy consumption in cloud computing . However truculent VM consolidation leads increase in SLA violations for which cloud service provider need to bear some penalty as per the agreement with the clients. So there is a great need of efficient algorithms which can show a better trade-off between energy consumption and SLA violations. In this paper, we propose Optimal Energy aware Dynamic Virtual Machine consolidation by using new VM Selection Policy (MCSSD), which is Maximum Correlation of Sum of Squares of ordinary least squares regression. the experimental results shows that better trade-off between energy consumption and SLA violations.
Calibration-Free Indoor Positioning Using Crowdsourced Data and Multidimensional Scaling

Indoor positioning plays an important role in various location-based services (LBSs). In conventional systems, the process of constructing radio maps for positioning usually involves labor-intensive manual calibrations, which seriously limits the system’s scalability and adaptiveness. In this paper, we propose an efficient calibration-free method by leveraging on crowdsourced WiFi signal data that are captured passively through a WiFi sensing testbed. Since the ground truths of the crowdsourced data are unavailable, the radio maps cannot be directly constructed. In the proposed method, we adopt the multidimensional scaling (MDS) technique to compute the positions of the unlabeled data thereby generating radio maps. In order to enable MDS, we estimate the pairwise distances among the unlabeled data by using an improved trilateration method and a law of cosine (LoC)-based geometrical algorithm without online pairwise measurements. Experimental results show that the accuracy of the proposed method is higher than trilateration-based method and reasonably lower than that of calibration-based method. Meanwhile, the run time of the proposed method is shorter than previous optimization-based methods. The short run time allows the radio maps to be dynamically updated against the environmental variations.
Digitally Controlled Low Amplitude Signal Generator

Low-level signal is a signal which has its range in microvolts . This paper describes a design of a low amplitude signal generator that has the capability of producing square, triangular and sine waveform as its output in the range of microvolt (up to 2μV) with a wide range of frequency from 0 to 10 MHZ and also a setup that can validate the low amplitude signals that is produced by the signal generator. In this low-level signal generator, it is possible to adjust the amplitude using digital code so that the control can be executed by using the computer. A problem in signal generation and measurement is the reduction of noise in the signal. Noise generation and noise picked by the circuit is generally in the range of millivolts. Removal of noise has been achieved using noise reduction technique like grounding and shielding.