Miracle-dz/UniFAR-FTUs huggingface dataset:
29Si NMR Investigation of the Effect of Acetic and Oxalic Acids on Portland-Limestone Cement Hydration

The use of carboxylic acids in mix design alters the hydration process of cement, the resulting pore structure of the obtained cement paste, and, consequently, the mechanical properties of concrete. All these changes are directly related to the structure of the calcium silicate hydrate phase. In the present study, the effect of acetic acid and oxalic acid on the hydration of Portland-limestone cement was monitored using solid state 29Si NMR spectroscopy. The results showed that acetic acid facilitated alite and belite hydration, however, the formation of polymerized silicate chains, incorporating Q2p species, begun later than in pure cement paste. Oxalic acid accelerated the polymerization, but slowed down alite and belite hydration. Such behaviors may correspond to decreased porosity (acetic acid addition) and increased strength (oxalic acid addition). Both acids accelerated belite hydration, compared to the pure paste, likely due to an increased acidity of the pore solution. The findings provide structural information about C─S─H phase, to be considered for thaumasite sulfate attack investigations on Portland-limestone cement pastes containing carboxylic acids.
Providing scientifically defensible evidence and correct calibrated thresholds for risk screening non-native species with second-generation Weed Risk Assessment-type decision-support tools

Abstract. The risk screening of non-native species that are likely to be invasive in a defined risk assessment area is crucial for implementing strategies of rapid response and mitigation to protect native biodiversity and socio-economic activities. However, for successful risk-ranking of the screened species, scientifically defensible evidence in support of the screening outcomes must be provided, and computation of a correctly calibrated threshold to distinguish between medium-risk and high-risk species must be achieved. This paper reviews published applications of the “second-generation” Weed Risk Assessment-type decision support tools (i.e. the Aquatic Species Invasiveness Screening Kit and the Terrestrial Animal Species Invasiveness Screening Kit) and evaluates them in terms of the above two requirements. Several procedural errors were identified that involved: i) lack of provision of the report with details of the species-specific screenings; ii) incomplete justifications for the responses in the toolkit questionnaire; iii) incomplete details of the protocol used for the a priori categorisation of the screened species for threshold computation; iv) unaccepted or non-existent taxonomic names for the screened species (including typographical errors). Guidelines are provided for both assessors and reviewers to ensure that these procedural errors are avoided in future applications of these risk screening toolkits.
Longitudinal user experience studies in the IoT domain: a brief panorama and challenges to overcome

User Experience (UX) is affected by users' emotions and motivations, among other subjective factors. Hence, changes in UX occur over time, according to the different contexts, and behavioral and internal user aspects. With the rising development of Internet of Things (IoT) systems to support daily activities and, at the same time, the increasing abandonment of these systems, the importance of ensuring good UX levels over time has become an even greater need. In this context, we conducted a snowballing procedure and systematic literature mapping to understand whether and how long-term UX evaluations have been conducted in IoT and the challenges and opportunities in conducting longitudinal studies in this field. We analyzed 55 papers that reported longitudinal evaluations in different IoT systems and 14 papers that reported UX evaluations in the IoT context. We also interviewed five software engineering professionals to learn about their IoT user-testing and evaluation perspectives. Our results provide a panorama of the conduction of longitudinal IoT studies and a glance at the directions the research on UX evaluation for IoT systems is taking. Hence, we indicate future research opportunities for evaluating long-term UX in IoT systems through the longitudinal approach, considering the complexity of these systems and their impact on users' everyday lives.
Investigating the use of UAUDG-VI in the mobile applications design for visually impaired considering accessibility, usability and UX: a feasibility study and an evaluation with experts

In the early stages of the APP development process, it is possible to consider quality aspects as Accessibility, Usability and User eXperience (UX) before the application is developed. This action reduces costs and avoids wasting time with rework. Moreover, part of the users of APPs are People who have some Visually Impairment (VIP). Therefore, to support designers to consider these quality aspects together during the design phase of APPs for VIP, we proposed the Usability, Accessibility and User eXperience Design Guidelines about Visual Impairment (UAUDG-VI) technique. To evaluate the 1st version of UAUDG-VI, a feasibility study was carried out with 24 students from a Requirements Engineering class. The study results identified suggestions for improvement of UAUDG-VI, generating a 2nd version of UAUDG-VI. After, an evaluation with 07 experts in HCI was carried out. Improvement suggestions were also identified, generating the 3rd version of UAUDG-VI. The main contribution of this paper is to encourage the scientific community to understand how the guidelines for new design technologies on quality have been proposed and evolved.
End-to-end Multipath Optimization for Reliable Aerial Connectivity

Next-generation Aerial Vehicles (AVs) demand multipath Air-to-Ground (A2G) connectivity to maintain reliable communications for safety-critical services. While utilizing redundant links improves the achievable Quality of Service (QoS), the link usage must be optimized to minimize the overall cost of connectivity. In this paper, we provide an optimization framework that minimizes the communication cost while ensuring to meet the QoS demands of the aerial application. Given the available access links and their QoS capabilities, the model selects a set of links that can jointly meet the required flow capacity, reliability and latency constraints with redundant transmission. Studying the Remote Piloting (RP) use case, we realistically implement the scenario in a flight and channel simulator, and feed the optimizer with the measured link quality from the simulation. As the optimization model is non-convex, we also linearize it, and benchmark them against conventional heuristic and brute-force algorithms. The results show that while the linearized model is favorable for online optimization scenarios due to fast computation times, the nonlinear model with Taylor approximations achieves more accurate latency behavior, and can be suitable for latency-critical applications in offline scenarios. Although dual cellular connectivity can suffice the QoS demands most of the time, excessive number of link switches imply a demand to orchestrate the access networks in a unified way to minimize connectivity disruptions.
Using Decentralized Aggregation for Federated Learning with Differential Privacy

Nowadays, the ubiquitous usage of mobile devices and networks have raised concerns about the loss of control over personal data and research advance towards the trade-off between privacy and utility in scenarios that combine exchange communications, big databases and distributed and collaborative (P2P) Machine Learning techniques. On the other hand, although Federated Learning (FL) provides some level of privacy by retaining the data at the local node, which executes a local training to enrich a global model, this scenario is still susceptible to privacy breaches as membership inference attacks. To provide a stronger level of privacy, this research deploys an experimental environment for FL with Differential Privacy (DP) using benchmark datasets. The obtained results show that the election of parameters and techniques of DP is central in the aforementioned trade-off between privacy and utility by means of a classification example.
How Accurate is LoRa Positioning in Realistic Conditions?

The LoRa communication technology has been developed for data transmission, but recent research papers showed that it could also be used to estimate the transmitting devices' location. The location can be found with reasonable accuracy, with median error as low as tens of meters; however, in many cases, it is evaluated in very optimistic transmission conditions, e.g. using line-of-sight communication and on a small data set. We estimate how LoRa localization performs in a realistic scenario, using an extensive data set of a telemetric network of a few thousand devices to calculate the location using the trilateration and estimate the error of the calculation. Within the analysed dataset the true-range multilateration provides highly inaccurate location estimation, with a median error of 588.64 m and an average error of 808.57 m. We argue that the LoRa localization in real-life conditions is ineffective, as the estimation of the distance between the node and the gateway changes heavily depending on the location of the node and radio channel attenuation.
Assessment of visual aesthetics through human judgments: a systematic mapping

Visual aesthetics is an essential factor of the perceived usability, credibility, and general quality of graphical user interfaces (GUIs). Its assessment as part of the software process is critical but highly subjective, making it difficult to obtain reliable and valid results. This article presents a systematic mapping analyzing measures and data collection instruments to understand how humans can assess GUI visual aesthetics with reliability and validity. We encountered 148 studies assessing visual aesthetics through human judgments, either as an unidimensional or a multidimensional construct. Most studies that assess visual aesthetics as a composition of multiple dimensions apply the classical/expressive or the VisAWI questionnaire. Yet, we observed large variability regarding research design, including the number of assessed GUIs, ratings, and raters. Although most studies report demographic data, very few analyze their influence on the results. Less than half of the studies evaluate reliability or validity. We expect the results to contribute to subjective assessments of GUI visual aesthetics, help practitioners select and use appropriate methods, and point out research opportunities in this area.
LoRaWAN-based Smart Parking Service: Deployment and Performance Evaluation

The dramatic increase of the urban population has put a lot of pressure in modern urban transportation systems. This not only implies noteworthy air pollution, and waste in time and energy, but also has led to the critical issue of the parking spots scarcity. Finding a parking spot has become one of the most common problems mentioned by drivers due to the day-by-day increase in number of vehicles. To overcome this problem, smart parking services based on sensors to detect parking spot occupancy status and inform drivers about their availability have been proposed. With the successful deployment of this kind of solutions, costs associated with traffic jams or wasted gas can be considerably reduced. However, one of the main challenges to be addressed when planning and deploying this kind of services is the communication technology employed by parking sensors, which are typically buried under the asphalt, and has to be able to cover large areas of the city in the most cost-efficient way. In this paper, we analyze the behavior and performance of a LoRaWAN network employed for supporting a Smart Parking service in the city of Santander. The sensors and network deployment are described in the paper and the thorough experimental assessment and evaluation of the network behavior is presented. The goal of this evaluation is to provide a better understanding of the key factors affecting the communication of outdoor parking spots. Through the continuous monitoring of two parking sensor deployments in the city, we derive some conclusions and lessons learnt that could benefit the planning and deployment of city-scale IoT infrastructures employing LoRaWAN networks as their wireless access technology.
Interaction paths between users on the autistic spectrum and technologies: scope review of the Brazilian symposium IHC

The development of software technologies to support the treatment of Autistic Spectrum Disorder (ASD) has been intensifying, enabling the emergence of new forms of education and treatment. Recent research in the Human-Computer Interaction (HCI) area addresses interaction paths between users on the autistic spectrum and these technologies. In this context, this paper presents a review of scope that covered the research published in the Brazilian Symposium on Human Factors in Computing Systems (IHC), from 2013 to 2021, in order to identify and analyze the interaction paths between users on the autistic spectrum and technologies: HCI practices adopted, types of software developed, and the abilities to be developed by the support of these technologies. As results, 18 articles were selected, of which 55% of HCI practices deal with the Interface Design stage, 37% of the software are mobile application, and also 67% these technologies are destined to children. As contribution, trends and opportunities for future research in the HCI and Accessibility areas were identified.
Unmanned aerial vehicle-aided edge networks with ultra-reliable low-latency communications: A digital twin approach

A digital twin (DT) framework for Internet ‐ of ‐ thing (IoT) networks is proposed where unmanned aerial vehicles (UAVs) acting as flying mobile edge computing (MEC) servers support the task offloading on the fly. The considered DT model is very well suitable for industrial automation with the strict constraints of mission ‐ critical services' ultra ‐ reliable low ‐ latency communication (URLLC) links. To support low ‐ latency IoT devices, we formulate the end ‐ to ‐ end (e2e) latency minimisation problem of digital twin ‐ aided off-loading UAV ‐ URLLC. Specifically, the minimised latency is obtained by jointly optimising both communication and computation parameters, namely power, offloading factors, and the processing rate of IoT devices and MEC ‐ UAV servers. Due to the highly non ‐ convex optimisation problem, we first consider the K ‐ means clustering algorithm to optimally deploy the on ‐ demand UAVs. Then, an alternative optimisation approach combined with appropriate inner approximations is effectively exploited to tackle this challenge. We demonstrate the effectiveness of the proposed DT framework through representative numerical results.
Quantum speed-up transition in open system dynamics

The rate of the trace distance is used to evaluate quantum speed-up for arbitrary mixed states. Compared with some present methods, the approach based on trace distance can provide an optimal bound to the speed of the evolution. The dynamical transition from no speed-up region to speed-up region takes on in the spontaneous decay of an two-level atom with detuning. The evolution is characteristic of the alternating behavior between quantum speed-up and speed-down in the strong system-reservoir coupling regime. Under the o? ff-resonance condition, the dynamical evolution can be accelerated for short previous times and then decelerated to a normal process either in the weak or strong coupling regime. From the time-energy uncertainty relation, we demonstrate that the potential capacity for quantum speed-up evolution is closely related to the energy flow-back from the reservoir to the system. The negative decay rate for short time intervals leads to the speed-up process where the photons previously emitted by the atom are reabsorbed at a later time. The values of the spontaneous decay rate becomes positive after a long enough time, which results in the normal evolution with no speed-up potential.
Ideological, semantic and geographic proximity drive following behaviour on Twitter

The set of followed accounts substantially determines the content Twitter algorithmically delivers to a given timeline. Previous studies suggest that users can get trapped in echo chambers in which they are only exposed to content aligned to their own ideological stance (Kwak et al. 2010). However, one may wonder whether ideological alignment is the sole and unique determinant of social ties in social media. This work aims at identifying alternative mechanisms that bind users together and to measure how determinant they are. We show that geographical proximity as well as shared interests also play a significant role in the formation of links, jeop-ardizing the classical model of social media as a place where people only exchange in-between politically aligned individuals. To
Unsupervised training data re-weighting for natural language understanding with local distribution approximation

One of the major challenges of training Natural Language Understanding (NLU) production models lies in the discrepancy between the distributions of the ofﬂine training data and of the online live data, due to, e.g., biased sampling scheme, cyclic seasonality shifts, annotated training data coming from a variety of different sources, and a changing pool of users. Consequently, the model trained by the ofﬂine data is biased. We often observe this problem especially in task-oriented conversational systems, where topics of interest and the characteristics of users using the system change over time. In this paper we propose an unsupervised approach to mitigate the ofﬂine training data sampling bias in multiple NLU tasks. We show that a local distribution approximation in the pre-trained embedding space enables the estimation of importance weights for training samples guiding resampling for an effective bias mitigation. We illustrate our novel approach using multiple NLU datasets and show improvements obtained without additional annotation, making this a general approach for mitigating effects of sampling bias.
Open Research Online The mother who cannot provide liberation: family atom analysis of women victims of domestic violence

The present contribution presents the discussion about the analysis carried out on family atoms that were completed in the first psychodrama group meetings carried out in all the Empower Daphne III program partner countries. The issue of the relationship with the mother is central to the aim of the project, in that we hypothesise that the mothers of victims are incapable of educating their daughters about personal auton-omy in relation to men, due to the traditional culture in which they grew up in. The article presents information about the use and processing of the survey tool “Family atom ” created by Jacob Moreno and the analysis of the data that emerged in parallel to the reports sent periodically by the psychodramatists to the monitoring and analysis team. From the results three types of maternal relationships emerge (positive, negative and incongruent) that enable us to confirm the initial hypothesis of this action research.
Affine image registration guided by particle filter

Image registration is a central task to different applications, such as medical image analysis, biomedical systems, stereo computer vision and optical flow estimation. There are many methods described in the literature for resolving this task, but they are mainly based on the minimisation of some cost function. These methods, depending on the complexity of the function to optimise, use different strategies for localising a minimum which explain the alignment between images or volumes, such as linearising the cost function or using multiscale spaces. In this work, a particle filter method, also known as sequential Monte Carlo strategy, is proposed to settle these difficulties by estimating the probability distribution function (PDF) of the parameters of affine transformations. Using the reconstructed PDF, it is possible to obtain an accurate estimation of the transformation parameters in order to register unimodal and multimodal data. The proposed method proved to be robust to noise, partial data and initialising parameters. A set of evaluation experiments also showed that the method is easy to implement, and competitive to estimate affine parameters in two-dimensional (2D) and 3D.
Design and Development of IOT based Smart Door Lock System

In today's world of the internet, security and comfort are the two eyes. The internet of things (IoT) helps us achieve this as this provides comfort and security to the user. The only thing that stands between us and the world outside us is the door so having proper security to safeguard our houses is important. Through the years, door locks have gone all the way from big, chunky complicated locks to contactless smart locks. Many doors in traditional systems had mechanical locks that had a limited number of keys. This article proposes Smart Door Unlock System based on Face Recognition, Fingerprint, RF card, Password and IoT to enhance the security. A camera sensor is used in this system to capture the face, and an image matching algorithm is used to detect the authenticated faces. Only the person whose face is matched can open the door. This system Fingerprint sensor is used to capture the finger and matching algorithm will be used to detect the fingerprint. So, limitation of managing keys will be resolved. The security system is also designed with the elderly in mind, as it is difficult for them to open the door manually.
Logic and lexicon - the semantics of the indefinite

Preface to the German Edition. Preface to the English Edition. Notes on the Translation. Introductory Remarks. Part I. 1. Semantic Indefiniteness. 2. Prerequisites and Fundamental Concepts. 3. The Phenomenon of Precisification. 4. Vagueness and Ambiguity. Part II. 5. On the Logic of the Indefinite. 6. Precisification Semantics. Part III. 7. Vagueness and Imprecision. 8. Dynamic Semantics for Vague Expressions. Concluding Remarks. References. Statements and Definitions. Index of Names. Subject Index.
Towards Real-Time Synchronization of mBot's Motion with Musical Songs

For more than a decade, researchers have been attempting to synchronize humanoid robot motion with musical beats, including incorporating ‘emotion’ of the music into the movement of robots. However, the cost the humanoid robots capable of executing motions using real-time recognition of musical notes is typically in the order of thousands of US$, which is extremely difficult for students to explore as ‘education robots', especially for students from the developing or underdeveloped countries around the world. We report here our work on exploring the possibility of applying the ‘robot-music synchronization’ technology to an affordable robot, i.e., ~US$50, to proliferate the educational value of this concept. We have chosen the mBot as the educational robot platform and showed that it can recognize the frequency of the musical sound signals in real-time and subsequently control the robot's LED (light-emitting diode) colors and motor functions for three different musical sound sources: 1) individual musical notes, 2) a rock-n-roll song, and 3) a classical music song. The limitations of the sound sensor used to collect real-time musical sound input and the deficiencies of the mBot's computing power are discussed in this paper.