Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Detection and Synchronization of Superimposed NB-IoT NPRACH Preambles

In the third-generation partnership project narrowband Internet-of-Things specifications, a new narrowband physical random-access channel (NPRACH) is specified as a single-tone frequency-hopping preamble. When multiple uplink users attempt to attach simultaneously, the base station receives superimposed NPRACH preambles and must detect all users and acquire their synchronization parameters: time-of-arrival (ToA) and residual carrier frequency offset (RCFO). In this paper, we first solve the detection problem and derive the optimal Neyman–Pearson detection threshold. Then, we analyze the theoretical detection performance and find the maximum coupling loss under both additive white Gaussian noise and Rayleigh fading channels. Next, the FFT phase trace of each detected user is exploited to estimate the user’s ToA and RCFO parameters. Simulations reveal that the algorithm has both favorable detection and estimation performances with very low complexity.
Who Needs Real-Life? Examining Needs in Virtual Worlds

Several authors have suggested that virtual worlds provide the potential for an altered reality for their users, who may seek to experiment with different personalities, appearances and personal needs. This paper empirically tests this assertion using Maslow's three higher order needs as the base. A survey delivered in the virtual world Second Life examines needs in real-life and the virtual world and personal characteristics. Using ANOVA the paper tests for differences in needs between the two environments and according to personal characteristics. The study finds that real-life needs significantly dominate virtual world needs. However, the pattern of needs in Second Life is different to real-life needs and emphasizes higher order needs, particularly self-actualization. This is particularly the case for the most innovative users of technology. Needs in real-life are still the key motivator for consumers, but the results also demonstrate the notion of an ‘altered reality’ for some respondents.
Computer and Smartphone Continuance Intention: A Motivational Model

ABSTRACT This paper develops and tests a motivational model to explain the overall continuance intention to use computers and smartphones. Based on survey data from 192 undergraduate students, structural equation modeling analysis is used to report: (1) the independent effect of intrinsic motivation on the continuance intention to use computers and smartphones; (2) the independent effect of extrinsic motivation on the continuance intention to use computers but not smartphones; (3) intrinsic motivation mediating the effect of extrinsic motivation on the continuance intention to use computers; (4) the independent effect of technology cognizance on the continuance intention to use computers but not smartphones; (5) intrinsic motivation positively influencing extrinsic motivation and technology cognizance, with respect to both devices. This research contributes to an improved understanding of the independent and interrelated effects of intrinsic and extrinsic motivations to use technological devices. The findings have important implications for theory and practice regarding the overall use of technology.
Edge gradient feature and long distance dependency for image semantic segmentation

Image semantic segmentation is a challenging problem for low-level computer vision. Recently, deep convolutional neural networks (DCNNs) have been proved to achieve outstanding performance in image semantic segmentation. Most current methods still have some problems in segmenting the object edges and the integrity of objects. In this study, the authors first construct the difference-pooling module in the DCNNs to extract the object edge gradients and get finer boundary in segmentation results. Then the combination of the pyramid pooling module and the atrous spatial pyramid pooling extracts the image global features and the context structure information by building long-distance dependency between pixels, which is just like a simple fully connected conditional random field (CRF). Different from other methods, the proposed method does not need extra pre-processing and post-processing steps, such as extracting gradient features by the traditional algorithm and building context relationships by CRF. Finally, the experimental results on the PASCAL VOC2012 benchmark indicate that the proposed model can obtain the finer boundaries and more complete parts.
Determinants of Mobile Learning Adoption: An Empirical Analysis

The purpose of this study is to explore how related factors influence the adoption of mobile learning in Malaysia by incorporating subjective norms and individual differences with Technology Acceptance Model (TAM). This study deploys a self-administered questionnaire and was tested on 401 respondents. Multiple regression analysis was conducted to test the hypotheses in this study. Overall, the results indicated that perceived usefulness, perceived ease of use and subjective norms is positively associated with the intention to adopt mobile learning. The findings for individual differences require further investigations as gender factor did not show significant effect on intention towards mobile learning usage. The study provides valuable managerial implications on how to speed up the process of adopting mobile learning in emerging markets by improving the assessment of mobile learning.
Using Social Network Sites: The Effects of Playfulness, Critical Mass and Trust in a Hedonic Context

The use of Internet social network sites has become an international phenomenon. These websites enable computer-mediated communication between people with common interests such as school, family, and friendship. Popular sites include MySpace and Facebook. Their rapid widespread usage warrants a better understanding. This study contributes to our understanding by empirically investigating factors influencing user adoption of these sites. We introduce the Social Network Site Adoption model to examine the effect of perceptions of normative pressure, playfulness, critical mass, trust, usefulness, and ease of use on usage intention and actual usage of these sites. Structural equation modeling was used to examine the patterns of inter-correlations among the constructs and to empirically test the hypotheses. All the hypothesized determinants have a significant direct effect on intent to use, with perceived playfulness and perceived critical mass the strongest indicators. Intent to use and perceived playfulness have a significant direct effect on actual usage.
Deployment of Unmanned Aerial Vehicle Base Stations for Optimal Quality of Coverage

This letter studies the network performance improvement by deploying flying base stations mounted on unmanned aerial vehicles (UAV-BSs) during some occasional events. Specifically, we consider the problem to minimize the average UAV-user distance while keeping the UAV-BSs connected to the stationary base stations. We propose a distributed algorithm which requires only local information and is scalable to a large number of UAV-BSs. We prove its convergence and conduct simulations with a real data set to evaluate its performance.
The Perceptions of Irish Mathematics Teachers Toward a Curriculum Reform 5 Years After Its Implementation

In Ireland the discussion about the educational goals of a curriculum have often been subsumed by the headlong rush to meet targets defined by state examinations, with the unfortunate consequence that the state examinations have often come to define the goals of the curriculum. In 2010 a new nationwide post-primary mathematics curriculum, locally titled ‘Project Maths’, was introduced in an attempt to modernise a perceived out-dated curriculum by simultaneously altering the content of the curriculum, the pedagogical approaches employed by teachers, and the national assessment strategies. There was the belief by policy makers that only by altering all three of these curricular pillars concurrently could they definitively remove the prevailing approach of “teaching to the test”. This study aims to investigate teachers’ perceptions towards the implementation of this new curriculum five years after it was rolled out. A cross-sectional, mixed-methods research approach, that collected both qualitative and quantitative data, was employed via a nationwide survey of current mathematics teachers. Responses from 147 teachers indicated that for the most part teachers are supportive of the new curriculum goals, but that they are still struggling regarding the implementation of the intended curriculum in the classroom.
Exploring E-Payment Adoption in Vietnam and Taiwan

The purpose of this paper is to analyze how related factors influence customers' e-payment adoption in Vietnam and Taiwan. A conceptual model is proposed with four factors: perceived ease of use, perceived usefulness (from technology acceptance model), perceived risk and information, which impact e-payment use. Beyond these factors, the adaptor's personal innovativeness in technology is used as a key moderator. The sample consists of 676 Vietnamese and Taiwanese banking services users accessed through a survey. Results confirm the effects of the above determinants on customers' choices for e-payment use and reveal the role of personal innovativeness as a moderator. Practical guidelines are provided to bank managers on how to push up the process of adopting or adapting technology in new market. A comparison between Vietnamese and Taiwanese customers' perception brings a valuable reflection for investors or managers when targeting Vietnam or Taiwan market.
Ready for Battle? A Phenomenological Study of Military Simulation Systems

The purpose of this research is to determine if there is task-technology fit for the use of simulation in training. In the context of military training, the research investigated whether military officers experience a fit between the simulations with which they were trained and their tasks (e.g., strategy or operational procedures), as reflected by their perception of whether their performance improved, and whether the simulation supported their individual activities. The research method followed the qualitative research tradition of transcendental phenomenology to query the experience of those who were trained with simulation. This research utilized interview questions that elicit respondents' experience in the broad areas of task-technology fit, and through phenomenological analysis, identified the salient variables within each of these constructs. The analysis resulted in the identification of themes of meaning which were compared with the existing task-technology fit variables. The research identified the emergence of new task-technology fit variables.
Failure prevention and management in business incubation: practices towards a scalable business model

ABSTRACT How support systems such as a business incubator deal with failure, a common phenomenon in new venture creation, is less understood. Employing a value creation perspective helps us to understand failure, the inability of an entrepreneurial team to build a scalable business model. Based on case studies at nine Swedish business incubators, we develop a dynamic process model towards understanding failure prevention and management in business incubation. We find business incubation practices towards failure prevention and management to be a mix of predictive and non-predictive strategies. These practices could help prevent and mitigate failure at personal, organisational and social levels towards value creation for the startups and their stakeholders and channel the effects of failure towards social benefit.
Multiobjective Optimization for Frequency Support Using Electric Vehicles: An Aggregator-Based Hierarchical Control Mechanism

In the last few years, there has been an exponential increase in the penetration of electric vehicles (EVs) due to their eco-friendly nature, and ability to support bidirectional energy exchanges with the smart grid. Besides serving transportation needs and reducing the carbon footprints in the environment, EVs are widely used for instantaneous grid frequency support. However, the existing research proposals have concentrated majorly on unidirectional vehicle-to-grid (V2G) support using fleet of EVs, which in turn leads to reduced frequency regulation and reserve capacity of participating EVs. Motivated from these facts, in this paper, an “aggregator-based hierarchical control mechanism” for secondary frequency regulation (SFR) using a fleet of EVs has been presented. In the proposed solution, EVs’ scheduling problem has been formulated to provide optimal SFR, while satisfying EVs’ energy demands under battery degradation constraints. This multiobjective primal problem (Mo-PP) under multiple constraints is solved using an approximation approach. This task is achieved by decomposing the complex Mo-PP into four different subproblems (SPs), corresponding to controllers deployed at different layers. The designed SPs are then iteratively solved using interior point method. In summary, the tradeoff between SFR and EV's bidirectional energy demands has been investigated in this paper. Moreover, battery degradation issues induced due to frequent charging and discharging cycles of EVs are also explored. Optimal dispatch of regulation signals among the aggregators and charging stations also takes into account the advantages of conventional droop mechanism. Lastly, widely accepted Pennsylvania–New Jersey–Maryland and ERCOT regulation data have been used to perform extensive simulations. The results obtained demonstrate that the proposed scheme achieves 22.6% and 6.8% better performance in comparison with the existing schemes based on colored petri net and proportional integral derivative controller, respectively.
DEFT: A Distributed IoT Fingerprinting Technique

Identifying IoT devices connected to a network has multiple security benefits, such as deployment of behavior-based anomaly detectors, automated vulnerability patching of specific device types, dynamic attack mitigation, etc. In this paper, we look into the problem of IoT device identification at network level, in particular from an ISP’s perspective. The simple solution of deploying a supervised machine learning algorithm at a centralized location in the network neither scales well nor can identify new devices. To tackle these challenges, we propose and develop a distributed device fingerprinting technique (DEFT), a distributed fingerprinting solution that addresses and exploits the presence of common devices, including new devices, across smart homes and enterprises in a network. A DEFT controller develops and maintains classifiers for fingerprinting, while gateways located closer to the IoT devices at homes perform device classification. Importantly, the controller and gateways coordinate to identify new devices in the network. DEFT is designed to be scalable and dynamic—it can be deployed, orchestrated, and controlled using software-defined networking and network function virtualization. DEFT is able to identify new device types automatically, while achieving high accuracy and low false positive rate. We demonstrate the effectiveness of DEFT by experimenting on data obtained from real-world IoT devices.
Simulation of counter drugs operations based on geospatial technology for use in a military training simulator

This article reports the adoption of potential surface analysis (PSA) to model drug trafficking routes in Thailand for military training scenarios of a small-arms simulator. GIS data layers were used for analytic hierarchy process. Experts were gathered to brainstorm for potential factors and their ratings based on knowledge, expertise and judgments. Physical, drug-aspect and socio-demographic factors were main factors and further separated into sub-factors. The consistency ratio (CR) was calculated at 0.09, the significance of the factors became less than 0.10, making the consistency acceptable. The PSA revealed potential drug-trafficking routes that were cross-checked with genuine routes. The created military training scenarios were validated by experienced military and security officials. The averaged satisfaction of 3.92 with the standard deviation of 0.75 confirms that the integrated methodology of modelling drug trafficking routes using potential surface analysis is another optional approach to upgrade the military small-arms training simulator.
Fate of Antibiotic Resistant Pseudomonas putida and Broad Host Range Plasmid in Natural Soil Microcosms

Plasmid conjugation is one of the dominant mechanisms of horizontal gene transfer, playing a noticeable role in the rapid spread of antibiotic resistance genes (ARGs). Broad host range plasmids are known to transfer to diverse bacteria in extracted soil bacterial communities when evaluated by filter mating incubation. However, the persistence and dissemination of broad range plasmid in natural soil has not been well studied. In this study, Pseudomonas putida with a conjugative antibiotic resistance plasmid RP4 was inoculated into a soil microcosm, the fate and persistence of P. putida and RP4 were monitored by quantitative PCR. The concentrations of P. putida and RP4 both rapidly decreased within 15-day incubation. P. putida then decayed at a significantly lower rate during subsequent incubation, however, no further decay of RP4 was observed, resulting in an elevated RP4/P. putida ratio (up to 10) after 75-day incubation, which implied potential transfer of RP4 to soil microbiota. We further sorted RP4 recipient bacteria from the soil microcosms by fluorescence-activated cell sorting. Spread of RP4 increased during 75-day microcosm operation and was estimated at around 10-4 transconjugants per recipient at the end of incubation. Analysis of 16S rRNA gene sequences of transconjugants showed that host bacteria of RP4 were affiliated to more than 15 phyla, with increased diversity and shift in the composition of host bacteria. Proteobacteria was the most dominant phylum in the transconjugant pools. Transient transfer of RP4 to some host bacteria was observed. These results emphasize the prolonged persistence of P. putida and RP4 in natural soil microcosms, and highlight the potential risks of increased spread potential of plasmid and broader range of host bacteria in disseminating ARGs in soil.
Bright Light as a Personalized Precision Treatment of Mood Disorders

Background: The use of light for its antidepressant action dates back to the beginnings of civilization. Three decades ago, the use of bright-light therapy (BLT) for treating Seasonal Affective Disorder (SAD) was officially proposed. Since then, a growing scientific literature reports its antidepressant efficacy in both unipolar and bipolar disorders (BD), with or without seasonal patterns. This review aims to examine the management of BLT as a personalized and precision treatment in SAD, unipolar, and BD. Methods: We conducted a narrative review using Medline and Google Scholar databases up to June 2018. Results: BLT has physiological effects by resynchronizing the biological clock (circadian system), enhancing alertness, increasing sleep pressure (homeostatic system), and acting on serotonin, and other monoaminergic pathways. Effects of BLT on mood depend on several factors such as light intensity, wavelength spectrum, illumination duration, time of the day, and individual circadian rhythms. A growing body of evidence has been generated over the last decade about BLT evolving as an effective depression treatment not only to be used in SAD, but also in non-seasonal depression, with efficiency comparable to fluoxetine, and possibly more robust in patients with BD. The antidepressant action of BLT is fast (within 1-week) and safe, with the need in BD to protect against manic switch with mood stabilizers. Side effects might be nausea, diarrhea, headache, and eye irritation, and are generally mild and rare. This good safety profile may be of particular interest, especially in women during the perinatal period or for the elderly. The management of BLT needs to be clarified across mood disorders and future studies are expected to compare different dose-titration protocols, to validate its use as a maintenance treatment, and also to identify predictive biomarkers of response and tolerability. We propose clinical guidelines for BLT use in SAD, non-seasonal depression, and BD. Conclusions : BLT is an efficient antidepressant strategy in mono- or adjunct-therapy, that should be personalized according the unipolar or bipolar subtype, the presence or absence of seasonal patterns, and also regarding its efficacy and tolerability.
Cosmological constraint from the 2dF QSO spatial power spectrum

We obtain constraints on the cosmological density parameters, Ω m and Ω b , by comparing the preliminary measurement of the quasi-stellar object (QSO) power spectrum from the Two-Degree Field (2dF) QSO Redshift (2QZ) survey with results from an analytic technique of power spectrum estimation, described herein. We demonstrate the validity of the analytic approach by comparing the results with the power spectrum of an N-body simulation. We find a better fit to the shape of the QSO power spectrum for low-density models with Ω m = 0.1-0.4. We show that a finite baryon fraction Ω b /Ω m = 0.2, consistent with observations of the cosmic microwave background anisotropies and nucleosynthesis, fits the observational result of the 2QZ survey better, though the constraint is not very tight. By using the Fisher matrix technique, we investigate just how large a survey would be required before a significant constraint on the density parameters could be obtained. We demonstrate that the constraint could be significantly improved if the survey was four times larger.
Improvement of Deep Brain Stimulation in Dyskinesia in Parkinson's Disease: A Meta-Analysis

Background: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) or globus pallidus internus (GPi) have been proven to be equally effective in improving motor-symptoms for advanced Parkinson's disease (PD) patients. However, it is unclear that which target stimulation is more effective in reducing dyskinesia. We conducted the meta-analysis to evaluate the efficacy of STN and GPi-DBS in the dyskinesia. Methods: A systematic search was performed in PubMed, Embase, and the Cochrane Library databases. Controlled trials about the dyskinesia comparing the efficacy of GPi and STN DBS were included. Clinical data of dyskinesia and levodopa equivalent doses (LED) were collected for the meta-analysis. Results: Eight eligible trials containing a total of 822 patients were included in this meta-analysis. Our results showed that GPi DBS offered a greater reduction of dyskinesia than STN DBS at 12 months after surgery, with an overall pooled SMD of 0.32 (95% CI = 0.06 to 0.59, P = 0.02). Treatment of STN DBS was associated with a greater reduction of LED compared with GPi DBS, with a change score of −320.55 (95% CI = −401.36 to −239.73, P < 0.00001). Conclusion: GPi DBS is superior to reduce dyskinesia than STN DBS at 12 months after surgery for advanced PD patients. Further studies should focus on the different mechanism for dyskinesia reduction by GPi or STN DBS.
Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication

A transmit power control strategy using a deep neural network (DNN) is proposed for underlay device-to-device (D2D) communication where D2D user equipment (DUE) shares radio resources with cellular user equipment (CUE). In this scheme, a transmit power control strategy for DUE is found with the aid of a newly proposed DNN structure. Both the spectral efficiency (SE) of the DUE and the amount of interference at the CUE are taken into account, such that the SE of the DUE can be improved while alleviating any deterioration in the cellular transmission. Using simulations, we show that the proposed scheme can achieve a high SE of the DUE while properly regulating the interference caused to the CUE, with a low computation time.