Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross Teager-Kaiser Energy Operator based Feature Extraction Method for Gait Recognition from Cumulative Foot Pressure Images

The human footprint is a convenient biometric to be used for recognition purposes as it is universal, easy to acquire and does not change much over time. The cumulative foot pressure image (CFPI) records the spatial and temporal changes of the ground reaction force over a single gait cycle, thus offering more information than an ordinary footprint. This enables us to distinguish between various human gait aspects like limb movement. In this paper a scheme is proposed for gait recognition, that exploits features extracted from the cumulative foot pressure images (CFPIs) collected from 88 subjects, using the Cross Teager-Kaiser Energy Operator (CTKEO). This scheme exhibits a consistency in performance across major classifiers, while delivering a maximum recognition accuracy of 97.3%.
Prediction of Diabetes Using Machine Learning Algorithms in Healthcare

There are several machine learning techniques that are used to perform predictive analytics over big data in various fields. Predictive analytics in healthcare is a challenging task but ultimately can help practitioners make big data-informed timely decisions about patient's health and treatment. This paper discusses the predictive analytics in healthcare, six different machine learning algorithms are used in this research work. For experiment purpose, a dataset of patient's medical record is obtained and six different machine learning algorithms are applied on the dataset. Performance and accuracy of the applied algorithms is discussed and compared. Comparison of the different machine learning techniques used in this study reveals which algorithm is best suited for prediction of diabetes. This paper aims to help doctors and practitioners in early prediction of diabetes using machine learning techniques.
A Comprehensive Performance Evaluation of Universal Filtered Multi-Carrier Technique

The Internet of Things (IoT) and machine-to-machine (M2M) communication will play an important role in future communications, but there is currently no ultra-reliable low-latency wireless communication theory that guides its design. For highly mobile vehicle-to-vehicle (V2V) wireless communication scenarios, asynchronous transmission should also be accepted. Therefore, it is particularly urgent to study ultra-reliable low-latency wireless transmission technology that satisfies asynchronous transmission. Universal Filter Multi-Carrier (UFMC) is a new type of filtering wireless transmission mechanism that meets this character. Although some of its properties have been explored in recent years, there are few articles that systematically evaluate its performance. In this paper, firstly, the performance of the UFMC system is fully evaluated in terms of spectral efficiency (SE), bit error rate (BER), peak-to-average power ratio (PAPR), carrier frequency offset (CFO), as well as various multipath fading channels, effects of time delay (TD), etc. Meanwhile, a mathematical analysis model is established for the BER of UFMC system, and some exact closed-loop expressions of bit error probabilities for UFMC are derived. Moreover, the equivalent form of the transmitter in the frequency domain is derived. Finally, the Monte Carlo simulation results related to UFMC are presented. The results reveal that UFMC suffers from the same problems as other multi-carrier system, including higher PAPR, affected by CFO, but it has its own inherent advantages such as insensitivity to time delay or energy-efficient, and the main negative factor of UFMC is inter-carrier interference (ICI) not inter-symbol interference (ISI), which may play an important role in future M2M and V2V communication.
MES Specific Data Analysis. Case Study with the Baxter Robot

In this research paper we aim to improve the functions of the Baxter robot [1] through data mining methods. Our case study belongs to the robotics domain, integrated in the context of Manufacturing Execution Systems (MES) and Product Lifecycle Management (PLM). The experimental data includes the parameters registered during the activities of the robot, such as the movement of the left or right arm and refers to collision events. The state of the art concerning the data mining methods is described, then our solution is detailed by presenting the approached data mining techniques. The adopted methods are detailed and then the experimental results are presented and discussed. Finally, the conclusions and further development possibilities are formulated, highlighting the utility of the adopted data mining methods, based on our previously stated objectives. An accuracy above 98% was achieved concerning the validation of our model.
VLF Current Distribution and Input Impedance of an Arbitrarily Oriented Linear Antenna in a Cold Plasma

In this paper, we proposed a semianalytical method for calculating the current distribution and input impedance of a very low frequency (VLF: 3–30 kHz) linear antenna of arbitrary orientation in a homogeneous anisotropic cold plasma. By considering the effect of the geomagnetic inclination angle, the kernel function, in this case, has a more complicated form and requires extra analytical techniques to deal with. The computations show that the amplitude coefficients for the ordinary wave are evidently greater than those for the extraordinary wave. We also found that the shape of the current distribution is not sensitive to the orientation of the antenna, but the total current moment on the antenna will be decreased when the inclination angle becomes larger. Moreover, due to the higher attenuation rates for both the ordinary and extraordinary waves at a propagation direction perpendicular to the magnetic field, the overall trend for the input impedance of the antenna is increasing with the geomagnetic inclination angle. It is then concluded that the optimal posture for a VLF space-borne linear antenna should be as parallel as possible to the direction of the geomagnetic field in order to achieve maximum antenna efficiency.
Public Health Surveillance System for Online Social Networks using One-Class Text Classification

Public health surveillance by traditional means is a costly and time consuming process. Today, the widespread use of social media has enabled researchers to study different aspects of life such as health, lifestyle, etc. Anonymous postings on these forums enable people to benefit from the collective experience of others facing similar problems. To effectively discern target data from the outliers in a web corpus, an efficient mechanism is required. Traditional approaches such as keyword-based filtering results in the loss of relevant data due to limited vocabulary and lack of contextual information. In this paper, we present a data filtration framework based on Long short-term memory (LSTM) recurrent neural network model for one-class text classification. We compare similarity of regenerated texts using this model for each disease with the original text using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric for outlier filtration and classification. Optimal value of ROUGE similarity threshold is determined by introducing an optimization parameter that minimizes the misclassification rate. Leveraging data from three major online health forums, we show that our classification technique outperforms keyword-based filtering and conventional approach of multi-class text classification. Our classification technique can be effectively used for online social networks, search engines, and online recommender systems.
A New No-reference Color Image Quality Assessment Metric in Wavelet and Gradient Domains

Most of image quality assessment methods are designed for grayscale images on a single domain. Therefore, they do not sufficiently make use of image color and multi-domain information that could provide valuable insights on the human visual mechanisms. In this paper, we propose a new No-reference Image Quality metric named WG-LAB, based on extraction of a set of features from distorted images in multiple domains (i.e., wavelet, and gradient domains) and multiple color channels (i.e., L, a, and b). Features are then exploited using Relevance Vector Machine Algorithm (RVM). The predictive performances of our method have been evaluated and compared to subjective judgments in terms of correlation, monotonicity and accuracy using the LIVE image database release 2 (LIVE II). The predictive performances obtained for our metric show that it has interesting features when compared to an array of existing full-reference (FR) and no-reference (NR) metrics on LIVE database.
Evaluating theUsability ofNatural LanguageQuery Languages and Interfaces to SemanticWebKnowledge Bases

The need to make the contents of the Semantic Web accessible to end-users becomes increasingly pressing as the amount of information stored in ontology-based knowledge bases steadily increases. Natural language interfaces (NLIs) provide a familiar and convenient means of query access to Semantic Web data for casual end-users. While several studies have shown that NLIs can achieve high retrieval performance as well as domain independence, this paper focuses on usability and investigates if NLIs and natural language query languages are useful from an enduser’s point of view. To that end, we introduce four interfaces each allowing a different query language and present a usability study benchmarking these interfaces. The results of the study reveal a clear preference for full natural language query sentences with a limited set of sentence beginnings over keywords or formal query languages. NLIs to ontology-based knowledge bases can, therefore, be considered to be useful for casual or occasional end-users. As such, the overarching contribution is one step towards the theoretical vision of the Semantic Web becoming reality.
A Framework for Self-Managing Database Systems

Database systems that autonomously manage their configuration and physical database design face numerous challenges: They need to anticipate future workloads, find satisfactory and robust configurations efficiently, and learn from recent actions. We describe a component-based framework for self-managed database systems to facilitate development and database integration with low overhead by relying on a clear separation of concerns. Our framework results in exchangeable and reusable components, which simplify experiments and promote further research. Furthermore, we propose an LP-based algorithm to find an efficient order to tune multiple dependent features in a recursive way.
Data Mining Approach to Chinese Food Analysis for Diet-Related Cardiometabolic Diseases

Data mining is the discovery of valuable and novel structures in datasets. Considering the number of people suffering from diet-related cardiometabolic increase, it brings into question the media's efforts in food and health communication. One of the main objectives in this study focuses on the emerging data mining methodology to understand the structure of what and how the news media discuss food, diet, and the related cardiometabolic diseases. A total of 6,625 items of coverage on food, flavor, and condiments along with cardiometabolic diseases is identified. Data mining algorithms concern food for predicting health outcomes and providing policy information. The most typical usage of a food data corpus is automatic conversion from text to health afflictions on larger cultural forces.
Mapping dynamic environments using Markov random field models

This paper focuses on dynamic environments for mobile robots and proposes a new mapping method combining hidden Markov models (HMMs) and Markov random fields (MRFs). Grid cells are used to represent the dynamic environment. The state change of every grid cell is modelled by an HMM with an unknown transition matrix. MRFs are applied to consider the dependence between different transition matrices. The unknown parameters are learnt from not only the corresponding observations but also its neighbours. Given the dependence, parameter maps are smooth. Expectation maximization (EM) is applied to obtain the best parameters from observations. Finally, a simulation is done to evaluate the proposed method.
Influence of Rotor Design on Field Regulation Capability of Hybrid Excited Electric Machines

The paper contains results of simulation research of a hybrid excited machine - Electrically Controlled Permanent Magnet Excited Synchronous Machine (ECPMSM) - with three construction concepts of the rotor. Selected experimental results of the prototype with two rotor's construction variants are also presented. In addition, in order to compare the properties at high rotational speeds for presented rotor designs, the expected performance, i.e. the range of speed and torque of the machine and the possibility of the excitation field weakening, were evaluated.
Abnormality Detection in Musculoskeletal Radiographs Using Capsule Network

To treat the diseases or injuries of the joints, bones, muscles, and spine in both adult and pediatric imaging the musculoskeletal radiographs bring a significant depth of expertise. Abnormality detection in the musculoskeletal study is backbreaking as more than 1.7 billion people are affected by musculoskeletal condition (BMU, 2017). Hence if we want to create enough opportunity to treat a maximum amount of patients, machine learning and deep learning can play a crucial role. CNN is an excellent deep learning method for image classification and other computer vision tasks. But CNN has exhibited some serious limitations when the images are rotated and deformed. Hence capsule network architecture is introduced in this paper for musculoskeletal radiographs abnormality detection and this capsnet architecture has shown very promising features that can help to vanquish the limitations of CNN. In addition, this capsule network has scored 10% higher kappa score than 169 layer densenet using less training data in the case of musculoskeletal radiographs abnormality detection. This feature of capsule network can help to use deep learning in such cases where an aggregate of a large amount of data is not possible. For image quality investigation, blind image spatial quality evaluator (BRISQUE) and naturalness image quality evaluator (NIQE) scores are measured and it is found that when the pixel size of the resized images are more close to the pixel size of the original images, we get a better approximation. Hence in the case of musculoskeletal radiographs abnormality detection, our method outperforms state-of-the-art method using a less amount of training data.
Real-Time Computation of Legendre-Sobolev Approximations

The present work is motivated by the problem of mathematical handwriting recognition where symbols are represented as plane curves, (X(λ), Y(λ)) parameterized by arc length λ ε[0, L]. Earlier work has shown that approximating the coordinate functions as certain truncated orthogonal polynomial series yields fast and effective recognition. It has been previously shown how to compute Legendre series representation in real time, as the curve is being traced out. In this article we show how to compute Legendre-Sobolev series representation in real time. The idea is to numerically integrate the moments of the coordinate functions as the curve is being traced. We show how the Legendre-Sobolev coefficients may be constructed either from the Legendre series coefficients or directly from the moments. Computing via Legendre series coefficients requires two matrix vector products, while the direct method requires only one.
Impact of Rotor Design on Motion Properties of a Synchronized Induction Motor

The article presents the results of tests of low-power induction motors operating at synchronous rotation speed. This working condition was achieved by introducing changes to the rotor shape design. The changes caused unevenness in the air gap between the stator and the rotor. The reluctance torque created in this structure enables a synchronous operation. The paper presents the results of experiments of a series of numerical simulations carried out for structures with various shapes of large teeth on squirrel-cage rotors. All considered structures maintained the continuity of the rotor cage.
A Technique for Electrical Error Localization with Learning Methods During Post-silicon Debugging

Error localization is a challenging step in the process of post-silicon validation owing to modern design complexity. This is exacerbated by the limited visibility of internal signals at the post-silicon validation stage. Incorporated design-for-debug features and off-line techniques assist in system-level error localization for processor based systems. However, for general SoCs and special purpose IPs, error localization at the netlist level is a challenging problem. This paper proposes a machine learning based error localization methodology during the debug step. Using limited trace data, unknown signal states are discovered with the help of cluster formation by utilizing k-nearest neighbors algorithm. These clusters assist in enhancing the internal signal state visibility to the maximum extent. We derive features from the enhanced debug data set to understand the nature of the injected bug and the erroneous flip-flop responses, which are then utilized to achieve spatial error localization.
KS-ABESwET: A Keyword Searchable Attribute-Based Encryption Scheme With Equality Test in the Internet of Things

With the widespread application of the Internet of Things (IoT) technology, it is expected to become a new data management model. But IoT devices have limited resource storage and computing power, they are highly vulnerable to hackers and leak sensitive information to third parties. The traditional attribute-based searchable encryption schemes usually require an intelligent terminal to perform complex calculations while accessing data, therefore IoT devices may not be able to withstand excessive calculation burden. In this paper, we present a keyword searchable attribute-based encryption scheme with equality test (KS-ABESwET) in the IoT by combining the notions of attribute-based searchable encryption (ABSE) with equality test. The proposed scheme adopts a keyword search algorithm based on the inverted index and equality test mechanism. If the keyword token match index is successful, the cloud server sends all ciphertexts that meet the conditions to the data user. Then, data user classifies the ciphertexts by equality test mechanism, which is executed by the authorized cloud server to determine whether the two ciphertexts encrypted by different access policies contain the same plaintext without decrypting. In this way, data user does not need to decrypt all ciphertexts, which decreases storage resource consumption of IoT devices and simplifies the complex operations generated by the traditional ABSE schemes. Using outsourcing technology, most calculations in the scheme are outsourced to the server, and IoT devices only perform a few calculations, which reduces greatly the computing and storage burden. Based on the decisional $q-1$ assumption and decisional Diffie–Hellman (DDH) assumption, the proposed scheme proves that has chosen-plaintext security and chosen-keyword security. Moreover, through comparative analysis and experimental simulation, our scheme is effective and suitable for IoT environment.
Dynamic Clustering of n-Dimensional Data on Tangential Space

Clustering of n-dimensional data into classes is consistent problem of research, Large number of efficient clustering techniques are in literature and still more are in development. K-means and Spherical K-means are standard clustering methods which are frequently used. Euclidean distance and cosine distance are mainly used by clustering methods. Data distribution is always non-linear and distributed in n-dimensional hyper sphere. Euclidean distance did not take care of topology of the hyper space. Clustering of data using spherical K-means clustering is done through mapping all data points in hyper sphere to the nearest cosine angular distance, but both do not take care of geodesic distance between the points on the surface of the hyper sphere. In this paper new mathematical dynamic clustering approach has been proposed which take care of topology of the data distribution between various clusters and geodesic distance between the points with in the cluster. Theoretical and mathematical results are discussed and empirically verified on the iris data set.
A New Centralized Access Control Scheme for D2D-Enabled mmWave Networks

In high-density indoor environments (e.g., airports or malls), it is expected an increase in data demand, in the number of applications having stringent Quality-of-Service requirements, and in the number of services among nearby users, such as video sharing or gaming. In order to satisfy these requirements, we adopt D2D communications, 60 GHz band transmissions, and adaptive beamforming techniques. The symbiosis of these technologies provides notable energy saving, high data rate, and strong interference reduction. Despite these benefits, a major issue is the limited communication range, due to the high path loss. To overcome this issue, we consider a Millimeter-Wave Mobile Broadband (MMB) system, consisting of several Access Points interconnected among themselves through a wireless backhaul network. Based on the specific features of the technologies adopted for the considered MMB system, we aim to improve the transmission efficiency, by exploiting concurrent transmissions. Therefore, we propose a new centralized access control scheme which jointly manages D2D communications and transmissions in the access and the backhaul network. Our control strategy aims to maximize the system throughput, to minimize the end-to-end delay, and to improve the fairness among users, while taking into account to keep the computational load as low as possible. To meet these goals, our proposed access control scheme is composed of a data flow management strategy and a multi-criteria scheduling algorithm based on greedy graph vertex-coloring techniques.
Practices of Energy Consumption for Sustainable Software Engineering

Sustainable Software Engineering, also known as “Green IN Software”, focuses on the production of sustainable software. The traditional software engineering process causes negative influences on the environment, economy and to society. For instance, the energy consumption during the software processing is considered as a first order impact because it directly leads to high costs on energy bills and consequently on the environment. Moreover, the optimization of a process implementation and software development can lead to second order impacts, also referred to as indirect impacts. Finally, the third other impact considers the user’s behaviors and consciousness regarding the concept of sustainability. In order to mitigate these negative impacts, the purpose of this research is to identify, via systematic literature review, the practices of sustainable software engineering reported by the academy applied in the industry. Through the systematic literature review, it was possible to discover 170 practices in which 70 were related to practices of energy consumption that could be adopted during the software development. Our results indicate that those practices emerged from the grounded theory, which are part of SWEBOK areas and are applicable in the industry.