Miracle-dz/UniFAR-FTUs huggingface dataset:
Cooperative Anomaly Detection With Transfer Learning-Based Hidden Markov Model in Virtualized Network Slicing

Network slicing can partition a shared substrate network into multiple logically isolated virtual networks to support diverse service requirements. However, one anomaly physical node (PN) in substrate networks will cause performance degradation of multiple network slices. To realize the self-organizing management of network slices, a cooperative anomaly detection scheme is designed in this letter through utilizing the transfer learning-based hidden Markov model (TLHMM). The PNs are first classified into four different states. Then, the hidden Markov model (HMM) is used to capture the current states of PNs based on the measurements of virtual nodes (VNs). Finally, according to the learned knowledge of networks and the similarity between PNs, the concept of transfer learning is introduced into HMM to propose a cooperative anomaly detection algorithm. Simulation results demonstrate that the proposed TLHMM-based cooperative anomaly detection algorithm cannot only speed up the learning process, but also achieve an average detection accuracy of more than 90%.
A 9-10.6 GHz Microstrip Antenna - UWB Low Noise Amplifier with differential Noise Canceling technique for IoT applications.

An ultra-wide band (UWB) receiver front-end that operates at the UWB frequency range, starting from 9 GHz - 10.6 GHz is proposed in this paper. The proposed system consists of an off-chip microstrip antenna and CMOS differential low noise amplifier with a differential noise canceling (DNC) technique. The proposed antenna is trapezoidal dipole shaped with balun and printed on a low-cost FR4 substrate with dimensions 10 × 10 × 0.8 mm3. The balun circuit integrated with the ground antenna to improve the antenna impedance matching. Noise canceling is obtained by using a differential block with each stage having 2 amplifiers that generate differential signals, subtracted to improve total noise performance. The proposed DNC block improves NF by 50% while increasing total power consumption with only 0.1 Mw. The differential CMOS cascode LNA with DNC block is implemented using UMC 0.13 μm CMOS process, exhibits a flat gain of 19 dB, maximum noise figure of 2.75 dB, 1 dB compression point −16 dBm and 3rd order intercept point (IIP3) −10 dBm. The proposed system has total DC power consumption of 2.8 mW at 1.2 V power supply.
Ispitivanje odnosa školskog postignuća i motivacije učenika VI razreda za angažovanje na časovima fizičkog vaspitanja

Relations between school achievements and motivation for involvement in PE classes were investigated in a sample of 185 6th graders of both genders in elementary school. The indepe-ndent variables included: the general success of the previous grade; PE grade; students' opinions about the sufficiency of knowledge acquired in physical education classes and students’ engagement in sport. The scale for measurement of motivation consisted of 29 items obtained by adaptation of the Scale for measurement of the motives of sports achievement. The data were analyzed by multiple regression analysis and χ2 test. It was established that there is a greater motivation for engagement in sport of male students compared to females. The following as-sumptions were not confirmed: that males will have higher PE grade compared to females; that the success of male students was negatively associated with sport commitment; that general success is negatively related to students’ engagement in physical education and that PE grade is positively related to motivation for engagement in classes. However, in contrast to the results obtained in the sample of 7th graders, it was confirmed that the PE grade was associated with engagement in sport. Additionally, it was shown that students’ general success and PE grade are not statistically significant predictors of student motivation for active participation and engagement in physical education classes, and that at this age, gender, students’ engagement in some sport outside regular curricular activities and their opinion on the amount of knowledge acquired in PE classes are significant predictors.
Parameter Estimation in Mathematical Modelling for Photovoltaic Panel

The demand for solar photovoltaic (PV) system is growing rapidly driven by new technology and strong economies of scale. PV systems directly convert solar energy into electricity without release any pollution into the environment and deplete natural resources. PV technology has matured and its reliability keeps improving. However, PV system is more expensive to produce than conventional sources of energy due in part to the cost of manufacturing PV devices and in part to the conversion efficiencies of the equipment. Besides, important attention in designing, developing and installing the PV systems is time-consuming. In this paper, we propose a mathematical model to predict the PV systems behaviour and performance by considering the plausible factors. The factors accept in this model are solar irradiance and manufacturers’ information for the type of PV panel. A case study at the eastern part of Peninsular Malaysia was conducted to examine the effect of factors on the performance of PV. Through the comparative analysis, the results have a good agreement.
A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation

Recent neural language generation systems often hallucinate contents (i.e., producing irrelevant or contradicted facts), especially when trained on loosely corresponding pairs of the input structure and text. To mitigate this issue, we propose to integrate a language understanding module for data refinement with self-training iterations to effectively induce strong equivalence between the input data and the paired text. Experiments on the E2E challenge dataset show that our proposed framework can reduce more than 50% relative unaligned noise from the original data-text pairs. A vanilla sequence-to-sequence neural NLG model trained on the refined data has improved on content correctness compared with the current state-of-the-art ensemble generator.
Mobile crowdsensing applications for intelligent parking and mobility

Due to the worldwide rapid urbanization and rising private vehicular mobility, most cities face problems such as traffic congestion, parking deficit, and cruising for parking, leading to resource wastage and environmental costs. This includes the first/lastfurlong problem for personal vehicular mobility in public places i.e. the challenge to find/locate a parking space near the destination. Intelligent mobile sensing, which fuses the computing/sensing capabilities of mobile devices with machine learning, holds a great potential towards solving such urban mobility challenges. Opportunistic Mobile Crowdsensing (MCS) has also emerged as a new paradigm that enables intelligent mobility applications and services. Mobile computing research has addressed the onstreet/outdoor traffic and parking problems to a great extent. However, the occupancy estimation of indoor parking garages remains mostly unsolved especially in garages that lack vehicle-tracking sensor infrastructure and/or real-time information delivery. In fact, the major bottleneck that precludes us from reusing successful outdoor schemes into parking garages is the inadequate indoor localization potential, due to unavailability of infrastructural support such as GPS/WiFi especially when underground. In addition, enterprises using MCS face concerns regarding the scalability of their proven system deployments to newer venues; this typically calls for extra data collection for training/tuning, which can be prohibitively expensive to reach a certain spatial urban coverage required to bootstrap a viable service. Moreover, drivers who park their vehicles in such garages often face an embarrassing problem of forgetting where they parked. Most existing indoor solutions rely on infrastructure such as WiFi or BLE, whereas current smartphone-only proposals mostly require major data collection and training efforts per garage, making them impractical to adopt at indoor parking garages. In this thesis, we address these issues by proposing crowdsensing methods, implementing system prototypes and endorsing them through empirical evaluation. In the first part of this thesis, we focus on the parking occupancy inference problem for indoor parking garages without precise localization. As we cannot use mobile crowd-
Quantum Singular Value Transformation & Its Algorithmic Applications

In this dissertation we study how efficiently quantum computers can solve various problems, and how large speedups can be achieved compared to classical computers. In particular we develop a generic quantum algorithmic framework that we call "quantum singular value transformation", capable of working with exponentially large matrices, that can apply polynomial transformations to the singular values of a block of a unitary. We show how quantum singular value transformation unifies a large number of prominent quantum algorithms, and show several problems where it leads to new quantum algorithms or improves earlier approaches. We develop an improved version of Jordan's quantum algorithm for gradient computation that can speed up the training of variational quantum optimization, and prove an essentially matching lower bound on quantum gradient computation. We also show that a quantum computer can very efficiently compute an approximate subgradient of a convex Lipschitz function. Combining this with some recent classical results we get improvements for black-box convex optimization problems. Then we take a new perspective on quantum SDP-solvers, introducing several new techniques, and improve on all prior quantum algorithms for SDP-solving. Finally we study the variable version of the Lovasz Local Lemma (LLL) and its quantum generalization. We improve on the previous constructive quantum results by designing an algorithm that works efficiently for non-commuting terms as well, assuming that the system is "uniformly" gapped. For the variable version of the classical LLL we find optimal bounds for the "guaranteed-to-be-feasible" probabilities on cyclic dependency graphs.
Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation

Unsupervised bilingual word embedding (UBWE), together with other technologies such as back-translation and denoising, has helped unsupervised neural machine translation (UNMT) achieve remarkable results in several language pairs. In previous methods, UBWE is first trained using non-parallel monolingual corpora and then this pre-trained UBWE is used to initialize the word embedding in the encoder and decoder of UNMT. That is, the training of UBWE and UNMT are separate. In this paper, we first empirically investigate the relationship between UBWE and UNMT. The empirical findings show that the performance of UNMT is significantly affected by the performance of UBWE. Thus, we propose two methods that train UNMT with UBWE agreement. Empirical results on several language pairs show that the proposed methods significantly outperform conventional UNMT.
Analysis of Empirical Mode Decomposition Method of Heart Rate Extraction from Motion Artifact Corrupted PPG Signal

The main objective of this paper is to detect the heart rate from any wearable wrist device. The number of times one’s heart beats per minute is called heart rate. Theoretically, any body part can be used to measure heart rate through the sensor of the device, although wrist type things are commonly targeted. In this paper the heart rate monitoring is based on photoplethysmoghaphy (PPG). But during the physical exercise it is tough to get the heart rate (HR) because then the PPG signals are vulnerable to motion artifacts (MA). To find HR an algorithm is presented here, which combines ensemble empirical mode decomposition (EEMD) with spectrum subtraction (SS). Ensemble Empirical Mode Decomposition decomposes a PPG signal and acceleration signal into intrinsic mode function (IMF). That’s mean it jointly estimates the spectra of PPG signals and simultaneous acceleration signals, utilizing the multiple measurement vector model in sparse signal recovery. This method can easily identify and remove the spectral peaks of motion artifact (MA) from the PPG spectra. The method explained in this paper has better performance than other methods.
Design and Implementation of Multi-parameter Healthcare System with Positioning Function

Based on multi-parameter physiological module CSN808 and GSM/GPRS engine SIM508 supporting GPS function, this paper presents a multi-parameter healthcare system with positioning function. The system can acquire ECG, respiration, blood pressure, body temperature signals and GPS position information of patients, analyse signals quickly and feedback diagnoses to patients. Under the circumstance of a sudden heart attack, the system can alarm automatically, and doctors in the healthcare center can guide patients’ self-treatment via GSM audio communication and take quick rescuing measures on the basis of patients’ GPS position information. To ensure the correctness of data transmission, Grøstl hash algorithm is realized to verify the integrity of data. This system can provide a round-the-clock, rapid-response, stable and reliable monitoring.
Applications of H-infinity loop shaping design procedure in UAV Flight Control Systems

The UAV flight control system is rich with attractive and challenging design problems to achieve robust stability and acceptable performance across specified flight envelope in the presence of uncertainties. Therefore, this paper is devoted to design an adequate flight control system for stabilizing a fixed wing (Aerosonde) UAV under exogenous inputs. This UAV is modeled and it is utilized with the   loop-shaping design procedure (LSDP) to design the necessary flight control system such that the performance requirements are achieved. This work is compared with a previous one using the classical controllers in terms of performance and stability robustness including disturbance rejection, noise attenuation, unmodeled dynamics, and control effort. The obtained results clarify the ability of the designed system using  H to cope with the specified levels of uncertainty including unmodeled dynamics, disturbances and measurement noise. In addition, it reveals its superior capabilities upon the classical techniques.
An Efficient Diagnosis-Aware ATPG Procedure to Enhance Diagnosis Resolution and Test Compaction

This paper proposes an efficient diagnosis-aware automatic test pattern generation (ATPG) procedure that can quickly identify equivalent-fault pairs and generate diagnosis patterns (DPs) for nonequivalent-fault pairs, where a (non)equivalent fault pair contains two stuck-at faults that are (non)equivalent. The proposed procedure contains three main methods, which together can efficiently generate highly compacted DPs by using a conventional ATPG tool. First, an all-pairs at-a-time diagnosis pattern generation (AFPAT-DPG) method, which adopts user-defined fault models (UDFMs), is employed to quickly generate DPs for most fault pairs that cannot be distinguished by a given set of, typically fault detection, test patterns (TP). For those fault pairs that cannot be distinguished by AFPAT-DPG, a multipair diagnostic ATPG method (MP-DATPG) is used. MP-DATPG is a complete method in the sense that it can generate diagnosis tests for every distinguishable pair of faults or prove that the pair of faults is indistinguishable. However, due to back-track limits in test generation procedures, diagnosis test generation for some fault pairs may be aborted after the application of the two methods. For such fault pairs, a subcircuit analysis (SCA) method is applied to identify equivalent fault pairs among the aborted fault pairs by trimming the circuit under consideration into one that is much easier to process within the back-track limits of the test generation procedures. Experimental results show that the proposed procedure is the first work that distinguishes 100% of all fault pairs in all ISCAS’89 and IWLS’05 benchmark circuits and over 99.99% for all ITC’99 benchmark circuits using a conventional ATPG tool that generates tests to detect faults.
Text Summarization Based on Thematic Representation of Texts

The described approach to text summarization is based on thematic representation of a text. Thematic representation includes nodes of thematically related terms simulating topics of the text. Thematic representation is created on the basis of detailed description of the domain and allows to create coherent summaries tbr texts of any size, to control length of summary using structural representation of contents of documents.
Modeling and Experimental of the Mechanical Behavior of Seat Cushion for Wheelchair Users

The cushions assistance to the pressure ulcers (PUs) prevention has like objective to improve wheelchair users by decreasing the effects of pressure between the both surfaces. Innovating honeycomb cushion (HC) containing thermoplastic polyurethane (TPU) which has many advantages was studied using a biomechanical and digital model for simulation. We used the finite element method (FEM) for the cushion simulation and modelization. This method led to the structural decomposition of the cushion in the aim to evaluate his mechanical behavior. This comportment was simulated numerically by FEM on cell of the cushion, then on the whole cushion. The tests of compression carried out with the electromechanical press (INSTRON 33R4204 machine) on HC permitted to determine curves related to his mechanical behavior. The result helps the comparison with the numerical model developed in this study. This result permits to study the distribution of pressure, shearing, friction and the microclimate which are sources of PUs through HC. The idea following this work is to optimize the HC in the aim to present a new product satisfying the needs for wheelchair users. The design of HC for prevention assistance of the scabs in only one layer is possible and present many advantages: optimization of the manufacturing process, improved ventilation, facilitated cleaning and decreasing cost.
Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations

Learning from social-media conversations has gained significant attention recently because of its applications in areas like rumor detection. In this research, we propose a new way to represent social-media conversations as binarized constituency trees that allows comparing features in source-posts and their replies effectively. Moreover, we propose to use convolution units in Tree LSTMs that are better at learning patterns in features obtained from the source and reply posts. Our Tree LSTM models employ multi-task (stance + rumor) learning and propagate the useful stance signal up in the tree for rumor classification at the root node. The proposed models achieve state-of-the-art performance, outperforming the current best model by 12% and 15% on F1-macro for rumor-veracity classification and stance classification tasks respectively.
Monitoring and Visualisation Approach for Collaboration Production Line Environments: A Case Study in Aircraft Assembly

In this paper, the SPMonitor tool, which is designed to monitor and visualise run-time execution of productive processes, is proposed. SPMonitor enables the dynamic visualisation and monitoring of workflows running in a system. It displays versatile information about currently executed workflows, thus providing a better understanding of processes and the general functionality of the domain. Moreover, SPMonitor enhances cooperation between different stakeholders by offering extensive communication and problem-solving features that allow the actors concerned to react more efficiently to different anomalies that may occur during a workflow execution. The ideas discussed are validated through a real-life case study related to aircraft assembly lines.
Automatically generated acceptance test: a software reliability experiment

The author presents results of a software reliability experiment that investigates the feasibility of a new error detection method. The method can be used as an acceptance test and is solely based on empirical data about the behavior of internal states of a program. The experimental design uses the existing environment of a multiversion experiment and used the launch interceptor problem as a model problem. This allows the controlled experimental investigation of versions with well-known single and multiple faults, and the availability of an oracle permits the determination of the error detection performance of the test. Fault-interaction phenomena are observed that have an amplifying effect on the number of error occurrences. Preliminary results indicate that all faults examined so far are detected by the acceptance test. This shows promise for further investigations and for the use of this test method in other applications. <<ETX>>
SupernoVAE : VAE based kernel PCA for analysis of spatio-temporal Earth data

It is a constant challenge to better understand the underlying dynamics and forces driving the Earth system. Advances in the field of deep learning allow for unprecedented results, but use of these methods in Earth system science is still very limited. We present a framework that makes use of a convolutional variational autoencoder as a learnable kernel from which to extract spatio-temporal dynamics via PCA. The method promises the ability of deep learning to digest highly complex spatio-temporal datasets while allowing expert interpretability. Preliminary results over two artificial datasets, with chaotic and stochastic temporal dynamics, show that the method can recover a latent driver parameter while baseline approaches cannot. While further testing on the limitations of the method is needed and experiments on real Earth datasets are in order, the present approach may contribute to further the understanding of Earth datasets that are highly non-linear.
Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums

Teaching machines to ask questions is an important yet challenging task. Most prior work focused on generating questions with fixed answers. As contents are highly limited by given answers, these questions are often not worth discussing. In this paper, we take the first step on teaching machines to ask open-answered questions from real-world news for open discussion (openQG). To generate high-qualified questions, effective ways for question evaluation are required. We take the perspective that the more answers a question receives, the better it is for open discussion, and analyze how language use affects the number of answers. Compared with other factors, e.g. topic and post time, linguistic factors keep our evaluation from being domain-specific. We carefully perform variable control on 11.5M questions from online forums to get a dataset, OQRanD, and further perform question analysis. Based on these conclusions, several models are built for question evaluation. For openQG task, we construct OQGenD, the first dataset as far as we know, and propose a model based on conditional generative adversarial networks and our question evaluation model. Experiments show that our model can generate questions with higher quality compared with commonly-used text generation methods.