Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Performance Analysis of the Best Effort Online Power Control Policy for Energy Harvesting Communications

Recent advances in energy harvesting techniques have enabled the development of selfsustainable systems that are powered by renewable energy sources in the environment. In this field, designing power control policies that can achieve high throughput is a significant problem. In this thesis, we analyze the long-term average throughput of Best Effort Policy in energy harvesting communications under a Bernoulli energy arrival process. We provide a limit and an order’s upper bound of the difference between throughput induced by limited battery and throughput induced by unlimited battery for two Bernoulli cases respectively when battery capacity approaches infinity. Besides, some other energy harvesting processes are considered, their characteristics of throughput are discussed by numerical simulation.
A hybrid optimization approach for unequal-sized dynamic facility layout problems under fuzzy random demands

Dynamic facility layout problems involve devising the optimal layout for each different production period. This article studies unequal-area dynamic facility layout problems under fuzzy random environment to minimize the sum of the material handling costs and rearrangement costs. For a more general situation, a novel model of unequal-area dynamic facility layout problems is proposed on the basis of fuzzy random theory, in which uncertain demands are characterized by fuzzy random variables. Unequal-area dynamic facility layout problems are one of the non-deterministic polynomial-time hard problems. Therefore, a hybrid particle swarm optimization and simulated annealing algorithm is innovated to solve the proposed unequal-area dynamic facility layout problems model under fuzzy random environment, in which the shapes and areas of facilities are changed dynamically. Two facility-swapping methods and two local search methods help hybrid algorithm escape from local optima, allowing a more reliable solution. Moreover, a new shifting method is developed to prevent the spatial overlapping between adjacent facilities and save material handling costs. The performance of the hybrid algorithm is confirmed by some test problems available. Finally, the proposed method is extended to a facility layout planning of a new aircraft assembly shop floor. Computational results show that the efficiency and effectiveness of the proposed method, in sharp comparison with other approaches.
The Utilitarian vs. Hedonic Teacher Acceptance of ICT Use

This research examined which teachers’ cognitive and motivational factors, i.e., constructivist beliefs, intrinsic and extrinsic motivation, perceived ease of use and behavioral intention, fostered an effective and satisfactory integration of technology into educational practices. 374 Italian teachers (MAge = 38.69, SD = 5.30; F = 198) attending a training course in ICT were divided into two groups: the first with less than 20 years of teaching experience, the second with more than 21 years. Participants completed a battery of questionnaires that comprised a socio-anagraphic section, the Teacher’s Beliefs Scale-Constructivist Teaching, the Intrinsic Motivation-Computer Enjoyment Scale, the Extrinsic Motivation-Perceived Usefulness, the Perceived Ease of Use Scale, the Behavioural Intention to Use Computer Scale, the ICT Class Use Scale. Descriptive and causal analyses (correlation and stepwise regression) were applied to the data. Results showed differences between the groups in the scores of Perceived usefulness and Behavioral intention to use computer. Bivariate associations with Classroom use of technology were strong and positive. Moreover, teachers’ Beliefs were strongly related to all motivational factors. Model 5 of regression analysis explained 46.9% of the variance in ICT Class use and indicated the highest value of Perceived enjoyment, thus highlighting the teachers’ hedonic-oriented motivation.
Sensor Data Visualization for Indoor Point Clouds

Abstract. Integration and analysis of real-time and historic sensor data provides important insights into the operational status of buildings. There is a need for the integration of sensor data and digital representations of the built environment for furthering stakeholder engagement within the realms of Real Estate 4.0 and Facility Management (FM), especially in a spatial representation context. In this paper, we propose a general system architecture that integrates point cloud data and sensor data for visualization and analysis. We further present a prototypical web-based implementation of that architecture and demonstrate its application for the integration and visualization of sensor data from a typical office building, with the aim to communicate and analyze occupant comfort. The empirical results obtained from our prototypical implementation demonstrate the feasibility of our approach for the provisioning of light-weight software components for the service-oriented integration of Building Information Modeling (BIM), Building Automation Systems (BASs), Integrated Workplace Management Systems (IWMSs), and future Digital Twin (DT) platforms.
Linear Codes From Perfect Nonlinear Functions Over Finite Fields

In this paper, a class of <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>-ary 3-weight linear codes and a class of binary 2-weight linear codes are proposed respectively by virtue of the properties of the perfect nonlinear functions over <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{p^{m}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$(m,s)$ </tex-math></inline-formula>-bent functions from <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{2^{m}}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{2^{s}}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> is an odd prime and <inline-formula> <tex-math notation="LaTeX">$m, s$ </tex-math></inline-formula> are positive integers. The weight distributions are completely determined by the sign of the Walsh transform of weakly regular bent functions and the size of the preimage of the employed <inline-formula> <tex-math notation="LaTeX">$(m,s)$ </tex-math></inline-formula>-bent functions at the zero point, respectively. As a special case, a class of optimal linear codes meeting Griesmer bound is obtained from our construction.
Machine learning tools for mRNA isoform function prediction

This dissertation is focused on improving mRNA isoform characterization in terms of functional networks, function prediction and tissue-specificity. There are three major challenges in solving these problems. The first is the unavailability of mRNA isoform level functional data which is required to develop machine learning tools. However, the available data, even at the gene level doesn’t include all genes, further complicating the matter. The second challenge is the lack of information about tissue-specificity in functional databases such as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and UniProt. The third challenge is the lack of mRNA isoform level “ground truth” functional annotation data. The scope of this dissertation includes using mRNA isoform and protein sequences, high-throughput RNA-sequencing data and functional annotations at the gene level to develop computational methods for predicting functions for alternative spliced mRNA isoforms in mouse. To address these challenges, this dissertation develops and describes two computational tools. The first is a supervised learning-based machine learning framework for predicting tissue-specific mRNA isoform functional networks. Tissue-spEcific mrNa iSoform functIonal Networks (TENSION) makes use of single mRNA producing gene annotations and gene annotations tagged with “NOT” to create a high-quality mRNA isoform level functional data. We use these mRNA isoform level functional data to train random forest algorithms to develop mRNA isoform functional network prediction models. By using a leave-one-tissue-out approach and incorporating tissue-specific mRNA isoform level predictors along with those obtained from mRNA isoform and protein sequences, we have developed mRNA isoform level functional networks for 17 mouse tissues. We
Messages Classification and Dynamic Batch Verification Scheme for VANETs

Under a high-density traffic environment in a vehicular ad hoc network (VANET), many safety messages will not be verified in time and then get dropped. To touch such an issue appropriately, we aim at improving the efficiency of message verification and the number of verified safety messages. In our proposed scheme, messages are properly classified and prioritized first so that more important basic safety messages can be verified earlier. To improve batch verification, a dynamic batch verification scheme with an adjustable batch size is designed accordingly. The batch size is halved when failures of batch verification occur consecutively over a certain threshold to improve the efficiency of batch verification. On the contrary, the batch size is doubled when successes of batch verification occur consecutively over a certain threshold to increase the messages in a batch to be verified. For a failure of batch verification, a divide-and-conquer approach is utilized to find the illegitimate message(s). Via analysis, the superiority of our design over some closely related schemes in terms of verification delay and number of verified basic safety messages etc. is successfully demonstrated. By simulation, performance of our design and some closely related schemes is further investigated in realistic scenarios.
Author identification for Under-Resourced language (KadazanDusun)

This paper presents the task of Author Identification for KadazanDusun language by using tweets as the source of data to perform Author Identification task of short text on KadazanDusun, which is considered as one the under-resourced language in Malaysia. The aim of this paper is to demonstrate Author Identification of short text on KadazanDusun. Besides, this paper also examines the performance of two machine learning algorithms on the KadazanDusun data set by analyzing the stylometric features. Stylometric features are used to quantify the writing styles of the authors which includes character n-grams and word n-grams. The workflow of Author Identification implements the machine learning approach to solve the single-labelled multi-class problem and predict the author of a given message in KadazanDusun. Two classifiers are used to compare the accuracy including Naive Bayes and Support Vector Machine (SVM). The results show that the combination of n-grams which is word-level unigram and {1-5}-grams with character 3-grams are the most relevant stylometric features in identifying the author of KadazanDusun message with an accuracy of 80.17%. The results also show that SVM classifier has outperformed Naive Bayes in this Author Identification task with the accuracy of 80.17%.
Towards a Bayesian framework for model validation

In this paper we discuss some concepts and a methodology of a Bayesian framework for model validation under uncertainty, which produces a probabilistic value for a models validity and may be used in the design of”validation experiments. By using a stochastic metric as a measure of the distance between experiment and prediction, we update a validation distribution. We show this in practice using a simple numerical experiment and discuss the current shortcomings of the method. We finally discuss the role of information entropy in designing validation experiments.
A Second-Order Purely VCO-Based CT $\Delta\Sigma$ ADC Using a Modified DPLL Structure in 40-nm CMOS

This article presents a power-efficient purely voltage-controlled oscillator (VCO)-based second-order continuous-time (CT) $\Delta \Sigma $ analog-to-digital converter (ADC), featuring a modified digital phase-locked loop (DPLL) structure. The proposed ADC combines a VCO with a switched-ring oscillator (SRO)-based time-to-digital converter (TDC), which enables second-order noise shaping without any operational transconductance amplifiers (OTAs). The nonlinearity of the front-end VCO is mitigated by putting it inside a closed loop. An array of phase/frequency detectors (PFDs) is used to relax the requirement on the VCO center frequency and thus reduces the VCO power and noise. The proposed architecture also realizes an intrinsic tri-level data-weighted averaging (DWA). A prototype chip is fabricated in a 40-nm CMOS process. The proposed ADC achieves a peak signal-to-noise-and-distortion ratio (SNDR) of 69.4 dB over 5.2-MHz bandwidth, while operating at the 260 MS/s and consuming 0.86 mW from a 1.1-V supply.
Two-Sided Online Micro-Task Assignment in Spatial Crowdsourcing

With the rapid development of smartphones, spatial crowdsourcing platforms are getting popular. A foundational research of spatial crowdsourcing is to allocate micro-tasks to suitable crowd workers. Many existing studies focus on the offline scenario, where all the spatiotemporal information of micro-tasks and crowd workers is given. In this paper, we focus on the online scenario and identify a more practical micro-task allocation problem, called the <italic><underline>G</underline>lobal <underline>O</underline>nline <underline>M</underline>icro-task <underline>A</underline>llocation in spatial crowdsourcing</italic> (GOMA) problem. We first extend the state-of-the-art algorithm for the online maximum weighted bipartite matching problem to the GOMA problem as the baseline algorithm. Although the baseline algorithm provides a theoretical guarantee for the worst case, its average performance in practice is not good enough since the worst case happens with a very low probability in the real world. Thus, we consider the average performance of online algorithms, <italic>a.k. a. </italic> random order model. We propose a two-phase-based framework, based on which we present the TGOA algorithm with a <inline-formula><tex-math notation="LaTeX">$\frac{1}{4}$</tex-math><alternatives><mml:math><mml:mfrac><mml:mn>1</mml:mn><mml:mn>4</mml:mn></mml:mfrac></mml:math><inline-graphic xlink:href="chen-ieq1-2948863.gif"/></alternatives></inline-formula>-competitive ratio under the random order model. To improve its efficiency, we further design the TGOA-Greedy and TGOA-OP algorithm following this framework, which runs faster than the TGOA algorithm with a competitive ratio of <inline-formula><tex-math notation="LaTeX">$\frac{1}{8}$</tex-math><alternatives><mml:math><mml:mfrac><mml:mn>1</mml:mn><mml:mn>8</mml:mn></mml:mfrac></mml:math><inline-graphic xlink:href="chen-ieq2-2948863.gif"/></alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\frac{1}{4}$</tex-math><alternatives><mml:math><mml:mfrac><mml:mn>1</mml:mn><mml:mn>4</mml:mn></mml:mfrac></mml:math><inline-graphic xlink:href="chen-ieq3-2948863.gif"/></alternatives></inline-formula>, respectively. We also revisit the average performance of Greedy, which has long been considered as the worst due to its unbounded competitive ratio in the worst case. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on synthetic and real datasets.
INTRAOPERATIVE TRANSOSEPHAGEAL ECHOCARDIOGRAPHY IN VALVE REPLACEMENT SURGERY: OUR EXPERIENCE

Transosephageal echocardiography (TEE) is applied widely during heart valve replacement surgery (1). The first successful demonstration of the use of the esophageal window for echocardiography was reported in 1976 (2). The esophageal echocardiography has provided an airless pathway especially in patients with chronic obstructive pulmonary disease, which yield interpretable echocardiographic windows and concluded as a safe diagnostic procedure (2). Transosephageal echocardiography (TEE) showed a huge improvement that, it became one of the essential diagnostic procedures for the valvular disease and valvular surgery (3-8). It has become the standard of care for evaluating the reparative or curative replacement valvular surgical procedures, thus providing an immediate gauge of the surgical results and helping to avoid suboptimal surgical results (8). From that time starting with the cardiology specialists, today TEE is performed perioperatively either cardiology or anesthesiology specialists or both of them according to the guidelines (9-12). Our aim is to report our TEE guided perioperative valvular surgical results in the light of the literature.
Multisensing System for Parkinson’s Disease Stage Assessment Based on FPGA-Embedded Serial SVM Classifier

Editor’s notes: This article presents an FPGA-based system with combined electroencephalographic (EEG) and electromyography (EMG) to diagnose the stage of Parkinson’s disease. The proposed approach leverages different biosignals for support vector machine (SVM)-based analysis and shows that it is possible to perform the analysis in a small embedded device. —Zhiru Zhang, Cornell University
Cluster validation in clustering‐based one‐class classification

Reconstruction‐based one‐class classification has shown to be very effective in a number of domains. This approach works by attempting to capture the underlying structure of the normal class, typically, by means of clusters of objects. It has the main disadvantage, however, that one has to indicate the number of clusters in advance, for this yields an efficient way of computing a clustering. In this paper, we introduce a new algorithm, OCKRA++, which achieves a better performance, by enhancing a clustering‐based one‐class ensemble classifier (OCKRA) with a cluster validity index that is used to set the best number of clusters during the classifier's training process. We have thoroughly tested OCKRA++ in a particular domain, namely masquerade detection. For this purpose, we have used the Windows‐Users and ‐Intruder simulation Logs data set repository, which contains 70 different masquerade data sets. We have found that OCKRA++ is currently the algorithm that achieves the best area under the curve, with a significant difference, in masquerade detection using the file system navigation approach.
Adaptive Quadrature Spatial Modulation

ABSTRACT In this paper, we investigate an enhanced form of spatial modulation, in the form of quadrature spatial modulation (QSM). The existing analytical error performance of QSM does not agree well with Monte Carlo simulation results in the low signal-to-noise ratio (SNR) region. Hence, the first contribution of the paper is to formulate a new analytical bound on error performance, which improves on the validation of simulation results at low SNRs. Secondly, a link adaptation technique for QSM is investigated in order to further improve its error performance. The proposed scheme is based on a selection technique between candidate transmission modes chosen to satisfy a target spectral efficiency, which employs optimal transmit antenna selection and modulation order selection, such as to minimize the average bit error probability (BEP). Euclidean distance-based antenna selection is employed to select the enumeration of transmit antennas for each transmission candidate, while the instantaneous BEP (IBEP) is employed to select the transmission candidate mode. Significant SNR gain over QSM is demonstrated. In order to reduce the computational complexity (CC) overhead of optimal transmit antenna selection, the third contribution is to investigate suboptimal low-complexity transmit antenna selection for the proposed scheme. Monte Carlo simulation results demonstrate a trade-off between the error performance and CC.
Personalized Question Bank Research Based on Particle Swarm Optimization

— Traditional question banks only pay attention to digitalize questions. This article proposes a new algorithm based on particle swarm optimization improved by personal parameters, which combining analyzing user’s exercise data. This method can provide an online, on-the-fly and personal service for each student. Experiments show that in response to varied numbers of questions, it takes a shorter time and is able to combine user’s understating of themselves knowledge with the right of selecting chapters. This method has the ability to lift students’ experience when using question bank.
Simulation Platform for Reduced Crew Operations — A Case Study

The introduction of imaginable future reduced crew operations of airliners requires thorough empirical testing of safety issues in comparison to nowadays modern multi crew operations. In the present case, the future concept of flight operations includes a single-pilot on airside and a human ground-based support component. The allocation of functions from airside to ground varies in the degree of remote control depending on workload and flight phase. A suitable simulation infrastructure is needed to investigate different aspects in operator, team and system performance of the new concept empirically. The exemplary present simulation platform for such research undertakings is composed of several interconnected hard- and software modules. These elements are extended by several functions for remote control to accommodate the requirements for a human-in-the-loop simulation. A case study exemplifies how the simulation platform was used to consider workload and technology acceptance of these two types of crewing during different flight phases. Against this background, the application of hard- and software as well as the reliability of the respective modules is reflected critically.
Study on degraded image sharpening method

: In industrial practice, images are often collected for study. In bad weather, the images collected may be degraded to different extents. Therefore, it is necessary to study how to sharpen these degraded images. Presently, domestic and foreign scholars mostly realise sharpening of degraded images by improving and optimising the existing classic algorithms based on image enhancement techniques and image restoration techniques. Meanwhile, subjective and objective evaluation methods for image sharpening effects have also been proposed. The article presents an image enhancement algorithm with adjustable threshold and tuning parameter and verifies this algorithm through experiment by subjective and objective evaluation methods, proposing a new solution for degraded image sharpening.
A Unified Neural Network for Panoptic Segmentation

In this paper, we propose a unified neural network for panoptic segmentation, a task aiming to achieve more fine‐grained segmentation. Following existing methods combining semantic and instance segmentation, our method relies on a triple‐branch neural network for tackling the unifying work. In the first stage, we adopt a ResNet50 with a feature pyramid network (FPN) as shared backbone to extract features. Then each branch leverages the shared feature maps and serves as the stuff, things, or mask branch. Lastly, the outputs are fused following a well‐designed strategy. Extensive experimental results on MS‐COCO dataset demonstrate that our approach achieves a competitive Panoptic Quality (PQ) metric score with the state of the art.