Miracle-dz/UniFAR-FTUs huggingface dataset:
Nanoscale Fabrication of Microwave Detectors from Commercially-Available CVD-Grown Monolayer Graphene

Using commercially-available monolayer graphene, synthesized by means of chemical vapor deposition, microwave power sensing elements have been nanofabricated and integrated with microwave-grade test structures suitable for on-wafer probing. The graphene, situated on a thermal oxide, was first cleaned of stray contaminants in a forming gas environment briefly held at 250 degrees Celsius using a rapid thermal annealer. Immediately following this step, the graphene was passivated with a protective aluminum (Al) oxide layer (approximately 5 nm in thickness). Micrometer-scale Corbino disc test structures were then fabricated in direct contact with the graphene using a self-aligned process, which relies on the fact that tetramethylammonium hydroxide develops the photoresist while removing the Al-oxide. Graphene nanoribbons (with widths as small as 400 nm) were then fabricated across the Corbino disc gaps using electron-beam writing in conjunction with a negative tone resist. The same developer exposed the majority of the graphene while defining nanometer-scale lines of photoresist stacked upon the Al-oxide layer. These stacks served as etch-stops while the unprotected remnants of Al-oxide and graphene were etched in a CF4/ O2 plasma. Finally, the photoresist was removed leaving behind passivated graphene nanoribbons. Damage caused by the fabrication was evaluated by comparing the Raman spectra of the graphene before and after microfabrication. Current-versus-voltage traces of the nanodevices exhibited the characteristic conductance minima corresponding to the charge-neutrality point of graphene. Radio frequency power detection experiments at this DC biasing point revealed a detection sensitivity of 0.14 mV/mW.
Deep Learning for Aspect Detection on Vietnamese Reviews

In recent years, Aspect-based Sentiment Analysis (ABSA) has been extensively researched in various languages because it aims to detect the sentiment of each aspect of the text. The ABSA problem can be divided into three subtasks as follow: the aspect detection, Opinion Target Expression (OTE) and Sentiment Polarity. In this paper, we present a Convolutional Neural Network architecture for the aspect detection for Vietnamese. The aspect detection is to aim to identify of the entity E and attribute A pairs expressed in the text (Pontiki et al., 2016). The experimental results show the superiority of our model over the winning systems on datasets of the VLSP 2018 challenge for aspect detection task. Our method achieves the F1 score of 80.40% for the restaurant domain and 69.25% for the hotel domain.
Enhanced Deep Networks for Short-Term and Medium-Term Load Forecasting

Electric load forecasting (ELF) is vitally beneficial for electrical power planning and economical running in smart grid. However, the medium-term load forecasting has been rarely studied. In addition, existing ELF models mainly consider the impact of limited external factors, which are usually difficult to forecast accurately. In this paper, the characteristics of the electric loads are analyzed and used as a guideline for the design of the proposed methods. To fully exploit the quasi-periodicity with different time ranges, i.e., year, quarter, month, and week, two deep learning methods, time-dependency convolutional neural network (TD-CNN), and cycle-based long short-term memory (C-LSTM) network are proposed to improve the forecasting performance of short-term load forecasting and MTLF with a little payload of computational complexity. Both of them only utilize the historical electric load and can mine the underlying load patterns by extracting the long-term global integrated features and short-term local similar features. By representing the loads as pixels and rearranging them into a 2-D photograph, TD-CNN transforms the temporal correlation of load series into the spatial correlation and keeps the long-term memory. Specifically, the convolutional kernel with special size targeted to load data is designed to extract the local pattern with similar characteristic, while the pooling layer is removed in order to keep the finer features. Moreover, in order to extract the temporal correlation between the long-term sequences with lower complexity, the proposed C-LSTM method generates a new short series from the original long load series without information loss. The LSTM is then applied to model the dynamical relationship of the load series with shorter time steps. The experimental results show that the proposed methods outperform the existing method with greatly reduced computation complexity, whose training time is about two-to-five times shorter than the existing method.
Modelling weak avalanche multiplication currents in IGFETS and SOS transistors for CAD

A model is developed for the substrate current of an IGET operating in saturation. This current results from the impact ionization of the channel current carriers in the high field region adjacent to the drain. Due to the exponential dependency of the impact ionization process on the electric field, an accurate description of the field near the drain is essential. An expression for the field, accounting for the two-dimensional nature of the drain space charge region, is used. The result is a closed form expression for the substrate current. By using an appropriate p-n junction model for the source diffusion, the floating substrate potential in SOS devices is evaluated.
Deep Reinforcement Learning Energy Management System for Multiple Battery Based Electric Vehicles

In recent years, energy management systems have become an emerging research topic. This concept allows the distribution of energy-intensive loads among various energy sources. An appropriate resource allocation scheme is necessary for the controller to efficiently allocate its energy resources in different operating conditions. Recent advances in artificial intelligence are instrumental to solve complex energy management problems by learning large repertoires of behavioral skills. This consists of hand-engineered policy and human-like expertise representations. In this paper, a deep reinforcement learning based resource allocation scheme is proposed for electric vehicles avoiding to work at the level of complex vehicle dynamics. Using multiple energy storage devices, like batteries, in parallel increases their maintenance due to their different behavior in various operating conditions. Thus, the proposed strategy aims to learn optimal policies to equilibrate the state of charge (SOC) of all batteries extending their lifespan and reducing their frequent maintenance.
Outage Performance of Cooperative Underlay CR-NOMA With Imperfect CSI

In this letter, a decode-and-forward cooperative underlay cognitive radio (CR) non-orthogonal multiple access (NOMA) network is studied. Exact closed-form expressions for the outage probability (OP) of two NOMA secondary destination users (<inline-formula> <tex-math notation="LaTeX">$U_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$U_{2}$ </tex-math></inline-formula>) are derived in the case of imperfect channel state information. Then, we find optimal power allocation factors for different distances of <inline-formula> <tex-math notation="LaTeX">$U_{1}$ </tex-math></inline-formula> to satisfy the OP fairness for both users. Moreover, the proposed system model shows the superiority of the CR-NOMA compared with cooperative orthogonal multiple access. The derived analytical expressions are corroborated through Monte Carlo simulations.
A 40.68MHz Active Rectifier with Hybrid Delay Compensation Scheme

This paper presents an active rectifier operating at 40.68MHz for wirelessly powered implant devices. To tackle the design difficulties at high frequency, a hybrid delay compensation scheme is proposed to compensate for the turn-on and turn-off delays. Comparing to the commonly used current-injection-based compensation scheme, this scheme simplifies the control logic and results in a more power efficient comparator design. The rectifier is fabricated in a standard 0. 18μm CMOS process with a total area of 1.581mm2. With the load resistor ranging from 200Ω to 2kΩ and the input AC voltage ranging from 2.5V to 4V, peak voltage conversion ratio of 91.0%, peak power conversion efficiency of 80.9% and maximum output power of 56.6mW are achieved.
Physiolytics at the workplace: Affordances and constraints of wearables use from an employee's perspective

Wearables paired with data analytics and machine learning algorithms that measure physiological (and other) parameters are slowly finding their way into our workplace. Several studies have reported positive effects from using such “physiolytics” devices and purported the notion that it may lead to significant workplace safety improvements or to increased awareness among employees concerning unhealthy work practices and other job‐related health and well‐being issues. At the same time, physiolytics may cause an overdependency on technology and create new constraints on privacy, individuality, and personal freedom. While it is easy to understand why organizations are implementing physiolytics, it remains unclear what employees think about using wearables at their workplace. Using an affordance theory lens, we, therefore, explore the mental models of employees who are faced with the introduction of physiolytics as part of corporate wellness or security programs. We identify five distinct user types each of which characterizes a specific viewpoint on physiolytics at the workplace: the freedom loving, the individualist, the cynical, the tech independent, and the balancer. Our findings allow for better understanding the wider implications and possible user responses to the introduction of wearable technologies in occupational settings and address the need for opening up the “user black box” in IS use research.
Modelling the information-seeking behaviour of international students in their use of social media in Malaysia

Introduction. The understanding of the information-seeking behaviour of international students using social media is limited. Therefore, the purpose of this research is to model the information-seeking behaviour of international students when they use social media to find information. Method. A mixed method approach was employed to collect data from the literature. Semi-structured interviews were conducted with twenty international students of different demographic backgrounds and with eleven staff members who work with international students. Questionnaires were distributed to 205 international students from four universities in Malaysia. Analysis. Using the literature review and empirical data, we propose a model of the information-seeking behaviour of international students who use social media, as validated and verified from the questionnaire. Results. The information-seeking activities were identified as informal searching, deciding, interacting, following, verifying and saving. The sources were search engines, social media and face-to-face settings. However, social media are more dominant than search engines and face-to-face communication. The questionnaire validated the proposed model and demonstrated that demographic variables exert a significant effect on the information-seeking behaviour of international students as they use social media. Conclusion. This research enhances the previous models of information-seeking behaviour by adding the role of social media and provides valuable insights for international students, universities and higher-education institutions.
Cognitive‐affective drivers of employees' daily compliance with information security policies: A multilevel, longitudinal study

We present a model of employee compliance with information security policy (ISP) that (1) explicates stable, cognitive beliefs regarding the consequences of compliance and noncompliance as well as state‐based affective constructs, namely, positive and negative mood states and episodic, security‐related work‐impediment events, and (2) provides an expanded conceptualisation of moral considerations and normative influences regarding employees' ISP compliance. Because affect is central to this theorisation, we ensure that the model captures and explains differences in day‐to‐day affective constructs to account for the often fleeting nature of affective states. We test our multilevel model using an experience‐sampling methodology design, in which employees completed daily surveys over a 2‐week period, followed by a hierarchal linear modelling statistical assessment. Our contribution to theory is a unique account of ISP compliance that integrates affective factors with constructs from rational choice theory and theory of planned behaviour and that diverges from prior conceptualisations of ISP compliance as a purely stable and reason‐based phenomenon. For practitioners, our results suggest that a combination of cognitive and affective influences may produce discrete episodes of ISP compliance that do not coincide with prior behavioural trends.
Identifying Urban Functional Zones Using Public Bicycle Rental Records and Point-of-Interest Data

Human mobility data have become an essential means to study travel behavior and trip purpose to identify urban functional zones, which portray land use at a finer granularity and offer insights for problems such as business site selection, urban design, and planning. However, very few works have leveraged public bicycle-sharing data, which provides a useful feature in depicting people’s short-trip transportation within a city, in the studies of urban functions and structure. Because of its convenience, bicycle usage tends to be close to point-of-interest (POI) features, the combination of which will no doubt enhance the understanding of the trip purpose for characterizing different functional zones. In our study, we propose a data-driven approach that uses station-based public bicycle rental records together with POI data in Hangzhou, China to identify urban functional zones. Topic modelling, unsupervised clustering, and visual analytics are employed to delineate the function matrix, aggregate functional zones, and present mixed land uses. Our result shows that business areas, industrial areas, and residential areas can be well detected, which validates the effectiveness of data generated from this new transportation mode. The word cloud of function labels reveals the mixed land use of different types of urban functions and improves the understanding of city structures.
New Trends in Using Augmented Reality Apps for Smart City Contexts

The idea of virtuality is not new, as research on visualization and simulation dates back to the early use of ink and paper sketches for alternative design comparisons. As technology has advanced so the way of visualizing simulations as well, but the progress is slow due to difficulties in creating workable simulations models and effectively providing them to the users. Augmented Reality and Virtual Reality, the evolving technologies that have been haunting the tech industry, receiving excessive attention from the media and colossal growing are redefining the way we interact, communicate and work together. From consumer application to manufacturers these technologies are used in different sectors providing huge benefits through several applications. In this work, we demonstrate the potentials of Augmented Reality techniques in a Smart City (Smart Campus) context. A multiplatform mobile app featuring Augmented Reality capabilities connected to GIS services are developed to evaluate different features such as performance, usability, effectiveness and satisfaction of the Augmented Reality technology in the context of a Smart Campus.
Some reflections on the relation between augmented and smart musical instruments

Augmented musical instruments (AMIs) consist of the augmentation of conventional instruments by means of sensor or actuator technologies. Smart musical instruments (SMIs) are instruments embedding not only sensor and actuator technology, but also wireless connectivity, onboard processing, and possibly systems delivering electronically produced sounds, haptic stimuli, and visuals. This paper attempts to disambiguate the concept of SMIs from that of AMIs on the basis of existing instances of the two families. We counterpose the features of these two families of musical instruments, the processes to build them (i.e., augmentation and smartification), and the respective supported practices. From the analysis it emerges that SMIs are not a subcategory of AMIs, rather they share some of their features. It is suggested that smartification is a process that encompasses augmentation, as well as that the artistic and pedagogical practices supported by SMIs may extend those offered by AMIs. These comparisons suggest that SMIs have the potential to bring more benefits to musicians and composers than AMIs, but also that they may be much more difficult to create in terms of resources and competences to be involved. Shedding light on these differences is useful to avoid confusing the two families and the respective terms, as well as for organological classifications.
L'efficacité du système auditif humain pour la reconnaissance de sons naturels

L'efficacite de la reconnaissance auditive peut etre decrite et quantifiee suivant deux aspects differents : la quantite d'information necessaire pour y parvenir et sa rapidite. L'objectif de cette these est d'evaluer experimentalement ces deux aspects. Dans une premiere partie experimentale, nous nous sommes interesses a la quantite d'information en creant des representations parcimonieuses de sons naturels originaux appelees esquisses auditives. Nous avons montre qu'une esquisse auditive est reconnue malgre la quantite tres limitee d'information auditive presente dans les stimuli. Pour l'analyse des stimuli auditifs, nous avons developpe un modele de distance auditive entre categories sonores. Pour l'analyse des performances des participants, nous avons developpe un modele pour le calcul de la sensibilite par categorie sonore et tenant compte du biais, qui s'integre dans la theorie de detection du signal. Ces analyses nous ont permis de montrer qu'en realite les resultats ne sont pas equivalents entre les differentes categories sonores. La voix se demarque des autres categories testees (e.g. instruments de musique) : la technique de selection de l'information parcimonieuse ne semble pas adaptee aux indices de la voix. Dans une seconde partie experimentale, nous avons etudie le decours temporel de la reconnaissance auditive. Afin d'estimer le temps necessaire au systeme auditif pour reconnaitre un son, nous avons utilise un recent paradigme de presentation audio sequentielle rapide (RASP, pour Rapid Audio Sequential Presentation). Nous avons montre que moins de 50 ms suffisent pour reconnaitre un son naturel court, avec une meilleure reconnaissance pour la pour la voix humaine.
Conceptualising the link between information systems and resilience: A developing country field study

Resilience—the ability of systems to cope with external shocks and trends—is a topic of increasing interest to research and practice. That growing interest is reflected within information systems (IS), but a structured review of IS literature shows a number of knowledge gaps around the conceptual and empirical application of resilience. This paper investigates what the subdiscipline of information and communication technologies for development (ICT4D) can contribute; finding that it offers the IS discipline fresh insights that can be built into a new framework of resilience, and an arena within which this new framework can appropriately be field tested. Application of the resilience framework was undertaken through interviews and a survey in an urban community in Costa Rica; benchmarking both community resilience and “e‐resilience” (understood here as the contribution of ICTs to community resilience), and developing from these a set of action priorities. The paper reflects on what can be learned generally from this conceptualisation and operationalisation of resilience. It also reflects on what ICTs contribute to resilience in developing countries and on what this ICT4D‐based research specifically contributes to the identified IS knowledge gaps. This includes identification of a future research agenda on information systems and resilience.
Subjective Evaluation of a Speech Emotion Recognition Interaction Framework

In the current work, a conducted subjective evaluation of three basic components of a framework for applied Speech Emotion Recognition (SER) for theatrical performance and social media communication and interaction is presented. The multidisciplinary survey group used for the evaluation is consisted of participants with Theatrical and Performance Arts background, as well as Journalism and Mass Communications Studies. Initially, a publically available database of emotional speech utterances, Acted Emotional Speech Dynamic Database (AESDD) is evaluated. We examine the degree of agreement between the perceived emotion by the participants and the intended expressed emotion in the AESDD recordings. Furthermore, the participants are asked to choose between different coloured lighting of certain scenes captured on video. Correlations between the emotional content of the scenes and selected colors are observed and discussed. Finally, a prototype application for SER and multimodal speech emotion data gathering is evaluated in terms of Usefulness, Ease of Use, Ease of Learning and Satisfaction.