Miracle-dz/UniFAR-FTUs huggingface dataset:
The Conceptual Idea of Smart City: University, Industry, and Government Vision

During the latest twenty years, urbanization has been accelerating all over the world. People move from country to cities to find better opportunities for living, working, studying, developing their entrepreneurial ideas. However, urbanization produces also several diseases such as pollution, traffic, congestion, waste and social exclusion [1, 2]. The smart city movement was born just from these opposite circumstances: the pivotal role of cities and the urban problems deriving from urbanization. A smart city is conceived like an urban strategy using high technology and especially ICT for supporting a participated social and economic development of the urban area, preventing pollution and reducing the environmental footprint. Until now, smart cities have been implemented especially applying a spontaneous, bottom-up process; municipalities, companies, not-for-profit organizations and the citizens themselves pursue the smartness of their city suggesting or directly implementing smart projects, initiatives, solutions. The final aim of this trend is to improve the citizens’ quality of life and the environmental preservation. Universities all over the world have been the first actor interested in studying and experimenting smart city pilots, starting this wave now interesting a very large set of heterogeneous stakeholders. As technology is the core component of a smart city, solution vendors are also first movers in designing and suggesting smart city solutions. Municipalities are involved as both players and coordinators of smart city plans interesting the city as a whole. The involvement of universities, industries and (local) governments in the smart city implementation responds to the triple helix idea [3, 4]; where citizens or their representatives are involved too, it becomes a quadruple helix. A smart city emerges therefore like an innovation ecosystem, exploiting social and economic
Automatic adaptation in self-monitoring systems for MCI patients: conceptual approach and a software prototype

With the growth of access to mobile devices, more applications are emerging for the most diverse purposes. In the health area, applications for monitoring patients with some type of dementia facilitate the day-to-day tasks of both patients and caregivers. However, a remaining challenge is to maintain patient loyalty in using the application so that their data can be constantly collected and analyzed, and treatments and follow-ups can be effective. One approach to trying to improve this situation is to customize and adapt the system, bringing it closer to the preferences of each user. This paper presents the conceptual approach and a prototype of a mobile self-monitoring system for patients with Mild Cognitive Impairment (MCI), a pre-dementia condition, which aims to maintain user loyalty in using the system.
Howdy Y’all: An Alexa TaskBot

In this paper, we present Howdy Y’all, a multi-modal task-oriented dialogue agent developed for the 2021-2022 Alexa Prize TaskBot competition. Our design principles guiding Howdy Y’all aim for high user satisfaction through friendly and trustworthy encounters, minimization of negative conversation edge cases, and wide coverage over many tasks. Hence, Howdy Y’all is built upon a rapid prototyping platform to enable fast experimentation and powered by four key innovations to enable this vision: (i) First, it combines a rules, phonetic matching, and a transformer-based approach for robust intent understanding. (ii) Second, to accurately elicit user preferences and guide users to the right task, Howdy Y’all is powered by a contrastive learning search framework over sentence embeddings and a conversational recommender for eliciting preferences. (iii) Third, to support a variety of user question types, it introduces a new data augmentation method for question generation and a self-supervised answer selection approach for improving question answering. (iv) Finally, to help motivate our users and keep them engaged, we design an emotional conversation tracker that provides empathetic responses to keep users engaged and a monitor of conversation quality.
Recognizing Building Group Patterns in Topographic Maps by Integrating Building Functional and Geometric Information

Recognizing building group patterns is fundamental to numerous fields, such as urban landscape evaluation, social analysis, and map generalization. Despite the increasing number of algorithms available for building group pattern recognition, there is still a lack of satisfactory grouping results due to insufficient information and only geometric features being provided to recognition methods. This study aims to provide a novel building grouping method that combines building function and geometric information. We specifically focus on the process of recognizing building groups in topographic maps as a prerequisite to subsequent map generalization. First, the building functions are inferred using the dynamic time warping (DTW) algorithm based on Tencent user density data and POIs (points of interest). Then, two types of constrained Delaunay triangulations (CDTs) are created for each building block, from which several spatial indices, such as the continuity index (SCI), direction, and distance of every two adjacent buildings, are derived. Finally, each building block is modeled as a graph on the grounds of derived matrices and building function information, and a graph segmentation approach is proposed to extract building groups. A case study is conducted to test the proposed approach. The experimental results indicate that the proposed approach can produce satisfactory results, given that the correctness value is above 81.63% for our study area. Comparative studies reveal that the method without building function information is an ineffective grouping method when buildings with different functions are close to each other. In addition, generalization results derived from the proposed method are more in line with those of maps for daily use, as they provide users with more accurate spatial divisions of urban buildings.
Effects of Organizational Communication on Employee Performance: A Case of the Agricultural Development Bank, Tamale.

Whenever there is an aggregation of people, there i s bound to be communication. Communication is the only way information can be passed on from one person to the other; through the use of both verbal and non- verbal means. The organization, like every other social gr oup uses communication in their daily activities; i n interactions between superiors and subordinates, as well as among colleagues, both formally and inform ally. The objective of this study was to identify the effect organizational communication has on the performance of employees and the Agricultural Development Bank; that is, the three branches in the Tamale Metropolis, Tamale Main, Aboabo and Kaladan were selected for the study. The design used for this study was the case stu dy. Employees of all three branches were sampled with t he combination of both the simple random sampling and stratified sampling. The simple random sampling was used to acquire 30 respondents out of a population of 50 for the administering of interviews, while the stra tified sampling was used to sample 6 respondents fr om among both senior level and junior level employees to be interviewed. Therefore a combination of questionnai res and interviews were used to collect data from employees of the bank. Both quantitative and qualitative met hods were used for analyzing the data that was collected, wit h the use of tables and figures to represent the da ta. The study showed that information about the bank is published on the website and so employees have a lot of know ledge about the performance of the bank. Results also sho wed that formal, top-down channel of communication, is predominantly used in the bank. Recommendations were therefore recommended that the bank also makes use of the bottom-up channel of communication.
Polycyclic aromatic hydrocarbons (PAHs) exposure through cooking environment and assessment strategies for human health implications

Abstract The polycyclic aromatic hydrocarbons’ (PAHs) exposure through day to day cooking activities has been a serious concern for human health due to their carcinogenic nature. Given the Covid-19 conditions, where people are spending extended time indoors, the likely exposure to these compounds will increase for the members involved/not involved in cooking. In this context, this review summarizes different studies undertaken worldwide on PAHs from cooking activities, the sources of exposure (fumes/emissions, dust/depositions), effect of scale (households/restaurants/neighborhoods) of cooking, monitoring process, risk assessment (air sampling and urinary metabolites), global distribution pattern. Proportionately higher number of studies was focused on cooking fumes and emissions while very limited studies aimed at kitchen depositions and dust. Most of the studies have not reported the size of particulate matter considered for determining PAHs exposure from cooking fumes and emissions. The evaluation of reported data becomes more complicated due to difference in sampling and expression units, the number and types of PAHs (parent, oxygenated-PAHs, i.e., o-PAHs, nitro-PAHs, i.e., n-PAHs) studied/found, lack of other intrinsic information (site and control parameters), lack of specific regulations etc. Therefore, such studies require method standardization for future policy development. This review also highlights the gaps and challenges in existing knowledge and future prospects. Graphical Abstract
Cyber hate speech on twitter: Analyzing disruptive events from social media to build a violent communication and hate speech taxonomy

The attack against the Charlie Hebdo weekly in Paris, in the year 2015, was a disruptive event that generated an important public reaction in social networks, creating the opportunity to study the phenomenon of violent communication and hate messages on Twitter. In the days after the attack (between January 7 and January 12), a sample of more than 255,000 tweets with the hashtags #CharlieHebdo, #JeSuisCharlie and #StopIslam was collected. An analysis was made using qualitative and quantitative approaches to contrast the level of agreement between the different methods used. In the first place, messages were classified as tweets that contained violent and hate speech or general messages, following the inclusion criteria that based on experience and the scientific literature were defined by the Principal Investigator. Then, three pairs of judges classified the sample using the excluding criteria previously defined, according to which ten types of violent speech communication were identified, which were reduced to five essential categories. After the qualitative analysis, the methods of Data Mining were used with the purpose of extracting systems of rules for the classification of the type of speech, beginning with 18 variables derived from each tweet, including date, favorites or the type of software used for the tweet, among others. The results show that disruptive events are followed by communications that show spatial temporal and textual patterns clearly identifiable; this allows the authors to propose a methodology to classify in a very precise way, those messages that contain hate or violent speech.
Response of Common Bean (Phaseolus vulgaris L.) to NP Fertilizer Rates and Plant Population Density at Jimma Zone, Southwest Ethiopia

Field experiments were carried out for 2018-2020 main cropping seasons at kersa and Omonada Jimma on farmers’ fields. The treatments consisted of factorial combinations of 23/23, 23/46, 46/46 and 69/69 NP kg ha-1 levels with inter and intra (333333, 250000, 200000 and 166667 plant density ha-1) respectively laid down in a randomized complete block design (RCBD) with three replications using (Nassir) common bean variety. Across season and location data analysis of ANNOVA showed that all parameters were significantly affected by different densities and NP fertilizer rates including above ground biomass, harvest index and grain yield except harvest index was not affected by NP fertilizer rates. The highest plant height (72.51cm) was recorded from the highest (69/69 kg ha-1) NP fertilizer rate. The highest above ground biomass and grain yield of 6.40 and 2.45 t ha-1 were obtained from the highest (333333) plant density ha-1. Also, it showed still there was an increase in plant density increases with 26.29 and 6.52% grain yield advantage over the lowest density and control recommendation respectively. Also from economic and sensitivity analysis results treatments (333333) ha-1 plant population density, (23/23) and (46/46) kg/ha NP fertilizer rates gave the highest yield response and net benefit with MMR 1035, 111 and 224% respectively. In conclusion, intra and inter row spacing 3*10cm or equivalent (333333) ha-1 plant population density with applications of one of the two (23-23) or (46/46) kg/ha NP fertilizer rates based on their resource was recommended to farmers of study areas and similar agro ecology.
A case study of the use of the Hattie and Timperley feedback model on written feedback in thesis examination in higher education

Abstract Effective feedback is a powerful educational intervention to support learning. Hattie and Timperley have developed a feedback model in which they define three different functions and four different levels of feedback. Although the model is widely used in educational practice, there is little known about how the model is used in education nor about the optimal distribution of the different types of feedback. In the current case study, we used an explanatory sequential mixed method design to investigate how lecturers use the model and how its use can be optimized. For this, 349 feedback comments from 22 lecturers were analysed, and 5 feedback experts participated in a focus group interview. The results show that most of the feedback given by the lecturers is task-oriented feedback and that current feedback practices can be improved by focusing more on the combination of feedup, feedback, and feedforward. In addition, results indicate that the level (task, process, self-regulation, person) of the feedback is more difficult to determine before hand and should be tailored to the learning goal. The design of the assessment form plays an important role in this alignment.
Antibiotic susceptibility of bacteria isolates from ward environment of a hospital in Tema, Ghana

Community and hospital-acquired antimicrobial resistance is on the increase worldwide and threatens the ability to treat patients effectively. This can result in high levels of morbidity and mortality from microbial infections. Susceptibility patterns help track microbial resistance potentials in order to enhance antibiotic prescription and use. The susceptibility patterns of Staphylococcus aureus and Salmonella typhi from the wards of a major hospital in the Tema Metropolis of the Greater Accra region of Ghana were studied. Fifty-seven S. aureus and 12 S. typhi isolates were confirmed from 150 samples collected from the various parts of the hospital wards. The isolates were evaluated for their susceptibility/resistance against five antibiotics namely: Cefuroxime, gentamicin, tetracycline, ciprofloxacin, and erythromycin using the Kirby-Bauer disc diffusion method. Results revealed that hospital door handles had the highest number of microbes as compared to other sites. Of the S. typhi isolates, 66.67% were resistant to cefuroxime but completely susceptible to gentamicin. Also, 75.44% of S. aureus isolates were resistant to cefuroxime but highly susceptible to ciprofloxacin, gentamicin and tetracycline. The results indicate that S. aureus and S. typhi are gradually developing resistance to cefuroxime which is currently a major antibiotic in the health delivery system of Ghana.
Hi viz learning: the transformation of on-job assessment for carpentry apprentices

In 2010 New Zealand’s Building and Construction Industry Training Organisation (BCITO) transformed its assessment system by creating “assessment teams” of assessors, carpentry apprentices, moderators, and evaluators (employers). This realigned the assessment process with the on-job learning environment for carpentry apprentices. It also created new partnership demands, especially in relation to creating learning environments and facilitating judgements about evidence of learning. This presentation is based on just completed research designed around on-site observations, interviews, and an analysis of apprentice learning records. The findings show how the mechanisms of the BCITO’s system work holistically and how they promote deeper learning for all members of the assessment team.
PARO’s Stress-Reduction Potential for Older Adults

As the proportion of older adults in the United States is growing, there is a need to develop methods that enable older adults to maintain well-being in their own homes. Stress, if not managed properly, can hinder older adults’ ability to maintain well-being. Efforts focusing on the development of novel methods of stress reduction have identified pet-type robots as being potentially efficacious. The present research will test this effect in one such robot absent of other social interactions and will disentangle the robot attributes that contribute to stress-reduction. Data collection is ongoing, but current results suggest that PARO may reduce stress, but the specific stress-reducing attributes are still unclear. Upon study completion, the results of this research will provide insights into PARO’s potential to be a therapeutic tool for older adults and inform design of such robots to maximize their stress-reduction capabilities.
GROUPS FIXING GRAPHS IN SWITCHING CLASSES

A permutation group G on a finite set Q is always exposable if whenever G stabilises a switching class of graphs on 0, G fixes a graph in the switching class. Here we consider the problem: given a finite group G, which permutation representations of G are always exposable? We present solutions to the problem for (i) 2-generator abelian groups, (ii) all abelian groups in semiregular representations, (iii) generalised quaternion groups and (iv) some representations of the symmetric group S n .
Nonlinear Characterization and Modeling of Radio-Frequency Devices and Power Amplifiers with Memory Effects

Despite the fast development of telecommunications systems experienced during the last two decades, much progress is expected in the coming years with the introduction of new solutions capable of delivering fast data-rates and ubiquitous connectivity. However, this development can only happen through the evolution of radio-frequency systems, which should be capable of working at high-power and high-speed. At the same time, the power dissipation of these systems should be minimized. In this dissertation, methods for the characterization and modeling of transistors and power amplifiers are presented, along with the necessary nonlinear measurements techniques. In particular, dynamic electrical effects, originated by the properties of the semiconductor materials and by the system configurations, are investigated. Consequently, these phenomena, which cannot be ignored to obtain the wanted performance, are empirically identified and included in models for Gallium Nitride (GaN) transistors and power amplifiers driven by a dynamic voltage supply.
Cloud Management with Reinforcement Learning

Cloud Computing, hiding the complexity of managing clusters from the developers, poses the challenges to cloud service providers to improve the quality of services while maintaining low operating cost. Through the years, many researchers have been dealing with these issues from different aspects, including network optimization, resource management, workload scheduling, etc. But the difficulty also arises with the growing scale of clusters and the heterogeneity of applications. Inspired by the recent advances in artificial intelligence problems, the idea of applying reinforcement learning techniques to assisting the management of cloud systems is increasingly attractive. Instead of manually exploring vast configuration space in response to various workloads, cloud service providers can deploy learning agents to collect the data, interact with complex environments automatically, and improve the system’s efficiency to the human expert-level or even beyond. This survey serves a review of selected cloud management topics addressed by reinforcement learning approaches. We first give a brief introduction of fundamental concepts and advanced models of reinforcement learning. Then we present several series of applications in particular fields, i.e., network optimization, virtual machine configuration, dynamic power management, and cluster scheduling. After showing how researchers formulate system optimizations in different settings as reinforcement learning problems and design learning agents tackling real-world issues, we conclude the survey by discussing the common characteristics and challenges of these applications, motivating further research and industrial-oriented solutions.
Validation of a fornix depth measurer: a putative tool for the assessment of progressive cicatrising conjunctivitis

Background/aims Documentation of conjunctival forniceal foreshortening in cases of progressive cicatrising conjunctivitis (PCC) is important in ascertaining disease stage and progression. Lower fornix shortening is often documented subjectively or semi-objectively, whereas upper forniceal obliteration is seldom quantiﬁed. Although tools such as fornix depth measurers (FDMs) have been described, their designs limit upper fornix measurement. The purpose of this study was to custom-design a FDM to evaluate the upper fornix and to assess variability in gauging fornix depth. Methods A polymethylmethacrylate FDM was constructed using industry-standard jewellery computer software and machinery. Two observers undertook a prospective independent evaluation of central lower fornix depth in a heterogeneous cohort of patients with clinically normal and abnormal conjunctival fornices both subjectively and by using the FDM (in mm). Upper central fornix depth was also measured. Agreement was assessed using Bland e Altman plots. Results Fifty-one eyes were evaluated. There was 100% intraobserver agreement to within 1 mm for each observer for lower fornix measurement. The mean difference in fornix depth loss using the FDM between observer 1 and 2 was 1.19%, with 95% conﬁdence of agreement ( 6 2SD) of (cid:1) 15% to +20%. In total, 86% (44/51) of measurements taken by the two observers agreed to within 10% of total lower fornix depth (ie, 6 1 mm) versus only 63% (32/51) of the subjective measurements. Mean upper fornix difference was 0.57 mm, with 95% conﬁdence of agreement of between (cid:1) 2 and +3 mm. Conclusions This custom-designed FDM is well tolerated by patients and shows low intraobserver and interobserver variability. This enables repeatable and reproducible measurement of upper and lower fornix depths, facilitating improved rates of detection and better monitoring of progression of conjunctival scarring.
Multimodal robotic system for upper-limb rehabilitation in physical environment

This article researches the feasibility of use of a multimodal robotic system for upper-limb neurorehabilitation therapies in physical environments, interacting with real objects. This system consists of an end-effector upper-limb rehabilitation robot, a hand exoskeleton, a gaze tracking system, an object tracking system, and electromyographic measuring units. For this purpose, the system architecture is stated, explaining the detailed functions of each subsystem as well as the interaction among them. Finally, an experimental scenario is designed to test the system with healthy subjects in order to check whether the system is suitable for future experiments with patients.