Miracle-dz/UniFAR-FTUs huggingface dataset:
Adoption of electronic medical records in developing countries—A multi-state study of the Nigerian healthcare system

Electronic medical records (EMR) are extensively used in developed countries to manage patient records and facilitate consultations and follow-up of treatment. This has resulted in centralised databases where different services and clinicians can quickly access patient data to support healthcare delivery. However, adoption and usage of EMR in developing countries is not common and, in most cases, non-existent. Clinicians are dependent on patients keeping their own records manually with no centralised database to manage and control the patient medical history. The key objective of this study was to investigate the propensity of clinicians and senior management personnel in healthcare facilities to adopt EMR and evaluate the contextual factors that impact or impede adoption. Using Davis's technology adoption model extended with other factors, this study determined if contextual or situational factors are associated with barriers that impede adoption of EMRs in developing countries. Using a cross-sectional quantitative research approach, a questionnaire was designed to collect data across four states in the Niger Delta region of Nigeria. Stratified random sampling was used to select healthcare facilities that participated in the survey and selection of respondents from each healthcare facility. Data was collected by trained research assistants and a total of 1,177 valid responses were received and analysed using factor analysis and multiple regression analysis. The results from the analysis show that usefulness, critical success factors, awareness and relative advantage significantly influence clinicians' intention to adopt EMRs. Surprisingly, infrastructure availability was not statistically significant. Meanwhile, risk and data security both negatively influence adoption, indicating that user perception of risk and safety of their data decreases their propensity to adopt EMRs. The results from this study suggests that usefulness and anticipated success factors in facilitating operations within healthcare facilities have a great influence on user adoption of EMRs. Awareness, training and education of users on the effectiveness of EMRs and their usefulness will increase adoption. The results will be beneficial in helping government and healthcare leaders formulate policies that will guide and support adoption of EMR. Other policy recommendations and suggestions for future research were also proffered.
Pediatric Nurse Students ’ Attitudes towards the Use of Disposable Toys : A Phenomenological Study

Background: As individual toys are recommended for each in-pat ient in pediatric settings to prevent crosscontamination. Aim: The present study aims to investigate pediatric nu rsi g student’s attitudes towards using disposable toys made from medication packaging in order to assess i ts potential as a viable nursing intervention. Methodology: This qualitative study employs a phenomenological approach to sample Turkish nursing students’ (n=80) attitudes towards using unused, clean medica tion packaging made of paper/cardboard to create th eir own disposable toys for child patients. Descriptive ana lysis was used to categorise the types of disposabl e toys created, while thematic analysis was used to aggreg ate the nursing student’s attitudes towards their p rceived usefulness. Results: From a total of 85 disposable toys, 6 different ty pes were identified (i.e. general, humanoid, animal like, vehicles, education material and medical obje cts). Three main themes emerged from the participan t’s opinions (i) the value of recycling unused medical p ckaging (ii) the benefit of providing sick childr en with specialised toys, and (iii) the benefits for primar y nursing. Conclusion: In conclusion, this intervention was viewed as bene ficial by reducing the risk of cross-infection, providing nurses with a means of creating better re lationships with patients, and improving hospital’s environmental impacts.
Improved End-to-End Speech Emotion Recognition Using Self Attention Mechanism and Multitask Learning

Accurately recognizing emotion from speech is a necessary yet challenging task due to the variability in speech and emotion. In this paper, we propose a speech emotion recognition (SER) method using end-to-end (E2E) multitask learning with self attention to deal with several issues. First, we extract features directly from speech spectrogram instead of using traditional hand-crafted features to better represent emotion. Second, we adopt self attention mechanism to focus on the salient periods of emotion in speech utterances. Finally, giving consid-eration to mutual features between emotion and gender classiﬁcation tasks, we incorporate gender classiﬁcation as an auxiliary task by using multitask learning to share useful information with emotion classiﬁcation task. Evaluation on IEMOCAP (a commonly used database for SER research) demonstrates that the proposed method outperforms the state-of-the-art methods and improves the overall accuracy by an absolute of 7.7% compared to the best existing result.
Auditory and Visual Reaction Time - A Tool for Early Detection of Neuropathy in Diabetics. -

Introduction: Chronic hyperglycemia in diabetes is associated with neuropathy. It affects peripheral nerves in the somato-sensory system and slows psychomotor responses which may affect reaction time. Reaction time is a simple and non-invasive test for peripheral and central neural structures. In view of this the present study is done to assess and compare auditory and visual reaction time in diabetics and normal adults. Materials and methods: The study was conducted at Basaveshwar teaching and general hospital, Gulbarga. 60 well controlled diabetic subjects without complications (duration of disease1-5 years as group 1 and 5-10 years as group-2) comprised the study group and 30 age and sex matched normal healthy individuals were included in the control group The visual reaction time for green and red light and auditory reaction time for tone and click sound was measured by using reaction time apparatus 2x4. Results: Both visual and auditory reaction times were prolonged in diabetics with P<0.0001, which is statistically highly significant. Discussion and Conclusion: The raised blood glucose in diabetes causes chemical changes in nerves and damages blood vessels that carry oxygen and nutrients to the nerves. Hyperglycaemic pathological changes intrinsic to the neurons and ischemic induced neuronal damage by decreased neurovascular blood flow may be responsible for reduced nerve conduction. Long standing hyperglycemia will also lead to a metabolic cascade that causes peripheral nerve injury. The axonal degeneration, axonal shrinkage, and thickening of basement membrane are responsible for delayed motor conduction and hence, delayed reaction time. Delayed reaction time in diabetics without clinical neuropathy can be taken as a noninvasive, low cost, sensitive indicator of early nerve damage without clinical signs and symptoms and hence, can be used as a routine screening test.
Optimizing Differentiated Discretization for Audio Circuits Beyond

One goal of Virtual Analog modeling of audio circuits is to produce digital models whose behavior matches analog prototypes as closely as possible. Discretization methods provide a systematic approach to generate such models but they introduce frequency response error, such as frequency warping for the trapezoidal method. Recent work showed how using different discretization methods for each reactive element could reduce such error for driving point transfer functions. It further provided a procedure to optimize that error according to a chosen metric through joint selection of the discretization parameters. Here, we extend that approach to the general case of transfer functions with one input and an arbitrary number of outputs expressed as linear combinations of the network variables, and we consider error metrics based on the L and the L norms. To demonstrate the validity of our approach, we apply the optimization procedure for the response of a Hammond organ vibrato/chorus ladder filter, a 19-output, 36th order filter, where each output frequency response presents many features spread across its passband.
Dynamically Finding Minimal Eviction Sets Can Be Quicker Than You Think for Side-Channel Attacks against the LLC

Minimal eviction sets are essential for conflict-based cache side-channel attacks targeting the last-level cache (LLC). In the most restricted case where attackers have no control over the mapping from virtual addresses to cache sets, finding rather than computing minimal eviction sets becomes the only solution. It was believed that finding minimal eviction sets is a long process until a recent discovery that it can be done in linear time. This paper focuses on improving the existing algorithms and finding minimal eviction sets with the minimal latency. A systematic analysis of the existing algorithms has been done using an ideal cache. Our analysis shows: The latency upper bound of finding minimal eviction sets can be further reduced from O(w2n) to O(wn); the average latency is seriously less than the upper bound; the latency assumption used by recent defenses is significantly overestimated. Overall, the latency is significantly shorter than we ever expected. Practical experiments are produced on three modern processors. Using a handful of new techniques proposed in this paper, including using concurrent multithread execution to circumvent the thrashing resistant cache replacement policies, we demonstrate that minimal eviction sets can be found within a fraction of a second on all processors, including a latest Coffee Lake one. It is also the first time to show that it is possible to find minimal eviction sets with totally random addresses without fixing the page offset bits, which provides a starting point towards a viable attack against fully randomized LLCs if they are ever adopted in the future.
The Althingi ASR System

All performed speeches in the Icelandic parliament, Althingi, are transcribed and published. An automatic speech recognition system (ASR) has been developed to reduce the manual work involved. To our knowledge, this is the ﬁrst open source speech recognizer in use for Icelandic. In this paper the development of the ASR is described. In-lab system performance is evaluated and ﬁrst results from the users are described. A word error rate (WER) of 7 . 91% was obtained on our in-lab speech recognition test set using time-delay deep neural network (TDNN) and re-scoring with a bidirectional recurrent neural network language model (RNN-LM). No further processing of the text is included in that number. In-lab F -score for the punctuation model is 80 . 6 and 61 . 6 for the paragraph model. The WER of the ASR, including punctuation marks and other post-processing, was 15 . 0 ± 6 . 0% , over 625 speeches, when tested in the wild. This is an upper limit since not all mismatches with the reference text are true errors of the ASR. The transcribers of Althingi graded 77% of the speech transcripts as Good. The Althingi corpus and ASR recipe, constitute a valuable resource for further developments within Icelandic language technology.
Strain improvement through chemical mutagenesis for enhanced production of erythromycin using Saccharopolyspora erythraea MTCC 1103

Strain improvement is an essential part of process development for fermentation products. Erythromycin is most commercially used antibiotics in day to day basis and is widely subjected to chemical mutagenesis to enhance the production of erythromycin. In this study (EMS) ethyl methane sulphonate was used as mutagen. The efficiency of EMS mutagen treated isolates showed effective rate of erythromycin production on the duration of exposure. The selected mutants of Saccharopolyspora erythraea MTCC 1103 isolates were grown in production medium. Erythromycin production was found highest for the mutant Saccharopolyspora erythraea MTCC 1103 with 0.48 g/L. The erythromycin production by the mutant strain using chemical mutagenesis showed higher yeild than the wild strain. HPLC analysis was performed in order to confirm the presence of erythromycin in the partially purified samples. The preliminary results indicate that erythromycin could be used for the commercial production as antibiotic which has significant industrial application and warrant more consideration as prospective antimicrobials.
A Survey On : E-mail Spam Messages and Bayesian Approach for Spam Filtering

Email has become one of the fastest and most economical forms of communication. However, the increase of email users has resulted in the dramatic increase of spam emails during the past few years. As spammers always try to find a way to evade existing filters, new filters need to be developed to catch spam. Generally, the main tool for email filtering is based on text classification. A classifier then is a system that classifies incoming messages as spam or legitimate(ham) using classification methods. The most important methods of classification utilize machine learning techniques. In this paper we compared some machine learning techniques and we discussed about Bayesian Algorithm for Spam detection with implementation results.
M3B corpus: multi-modal meeting behavior corpus for group meeting assessment

This paper is the first trial to create a corpus on human-to-human multi-modal communication among multiple persons in group discussions. Our corpus includes not only video conversations but also the head movement and eye gaze. In addition, it includes detailed labels about the behaviors appeared in the discussion. Since we focused on the micro-behavior, we classified the general behavior into more detailed behaviors based on those meaning. For example, we have four types of smile: response, agree, interesting, sympathy. Because it takes much effort to create such corpus having multiple sensor data and detailed labels, it seems that no one has created it. In this work, we first attempted to create a corpus called "M3B Corpus (Multi-Modal Meeting Behavior Corpus)," which includes 320 minutes discussion among 21 Japanese students in total by developing the recording system that can handle multiple sensors and 360-degree camera simultaneously and synchronously. In this paper, we introduce our developed recording system and report the detail of M3B Corpus.
Measuring Functioning and Well-Being: The Medical Outcomes Study Approach

Measuring Functioning and Well-Being is a comprehensive account a broad range of self-reported functioning and well-being measures developed for the Medical Outcomes Study, a large-sale study of how patients fare with health care in the United States. This book provides a set of ready-to-use generic measures that are applicable to all adults, including those well and chronically ill, as well as a methodological guide to collecting health data and constructing health measures. As demand increases for more practical methods to monitor the outcomes of health care, this volume offers a timely and valuable contribution to the field. The contributors address conceptual and methodological issues involved in measuring such important health status concepts as: physical, social, and role functioning; psychological distress and well-being; general health perceptions; energy and fatigue; sleep; and pain. The authors present psychometric results and explain how to administer, score, and interpret the measures. Comprising the work of a number of highly respected scholars in the field of health assessment, Measuring Functioning and Well-Being will be of great interest and value to the growing number of researchers, policymakers, and clinicians concerned with the management and evaluation of health care.
Spectral and Radiometric Calibration of the Next Generation Airborne Visible Infrared Spectrometer (AVIRIS-NG)

We describe advanced spectral and radiometric calibration techniques developed for NASA’s Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). By employing both statistically rigorous analysis and utilizing in situ data to inform calibration procedures and parameter estimation, we can dramatically reduce undesirable artifacts and minimize uncertainties of calibration parameters notoriously difficult to characterize in the laboratory. We describe a novel approach for destriping imaging spectrometer data through minimizing a Markov Random Field model. We then detail statistical methodology for bad pixel correction of the instrument, followed by the laboratory and field protocols involved in the corrections and evaluate their effectiveness on historical data. Finally, we review the geometric processing procedure used in production of the radiometrically calibrated image data.
Advances in Automatic Speech Recognition for Child Speech Using Factored Time Delay Neural Network

Automatic speech recognition (ASR) has shown huge advances in adult speech; however, when the models are tested on child speech, the performance does not achieve satisfactory word error rates (WER). This is mainly due to the high variance in acoustic features of child speech and the lack of clean, labeled corpora. We apply the factored time delay neural network (TDNN-F) to the child speech domain, finding that it yields better performance. To enable our models to handle the different noise conditions and extremely small corpora, we augment the original training data by adding noise and reverberation. Compared with conventional GMM-HMM and TDNN systems, TDNN-F does better on two widely accessible corpora: CMU Kids and CSLU Kids, and on the combination of these two. Our system achieves a 26% relative improvement in WER.
Influence of Various Polishing Protocols on Polyether-Ether-Ketone Removable Partial Frameworks Fabricated By CAD / CAM ( Scanning Electron Microscope Study )

Purpose: This in vitro scanning electron microscope study was performed to evaluate the influence of various polishing protocols on polyether-ether-ketone removable partial frameworks surface topography that fabricated by CAD / CAM. Materials and Methods: an epoxy resin model represent maxillary class I Kennedy classification with remaining natural teeth extending from the first premolar on one side to the first premolar teeth on the other side were used in the study. First premolars and canines of epoxy resin model were prepared to receive full ceramo-metal crowns with mesial occlusal and cinglum rest seat, distal guiding planes, and first premolars mesio-buccal retentive undercuts of 0.50 mm depth. 3 specimens of themodified polyetheretherketoneRDP frameworks were fabricated by CAD/CAM techniques. According to polishing protocols the themodified polyetheretherketoneRDP frameworks were divided into three groups: Group I (control group): modified polyetheretherketoneRDP frameworks without polishing, Group II: modified polyetheretherketoneRDP frameworks was polished by using Abraso-Starglanz polishing system and Group III: modified polyetheretherketoneRDP frameworks was polished by using Acrypol polishing system. Surface changes between groups before and after polishing by different polishing protocols were evaluated by scanning electron micro-scope (SEM). Results: It was noted that (group I) showed the highest surface roughness while the lowest surface roughness and highly smooth surface was showed in (group II). By using Student’s t-test for comparing between groups, group II showed significance decrease in surface roughness compared to that in (group I) and group III where (p<0.001). Conclusions: Within the limitations of this study, it was concluded that: Polishing PEEK RPD framework with abraso-starglaz paste was the most effective and promising polishing technique in production of highly smooth surface than acrypol polishing paste. For the laboratory-based protocols, both abraso-starglaz and acrypol polishing pastes produce a significantly smoother PEEK surface than that of the non-polished PEEK.
Exploring the potential for using seaweed (Ulva lactuca) as organic fertiliser

The focus of this project is on P recovery by using seaweed (Ulva lactuca) as organic fertiliser. The possibilities for using seaweeds as fertiliser, depend on two components. First, to determine whether seaweed is suitable as organic fertiliser, it is necessary to analyse the nutrients taken up by the seaweed, to gain insight in the amounts of nutrients that could be applied to the soil. Phosphorus content of Ulva lactuca grown under different initial P concentrations in the water (Pi) was measured. Phosphorus content in relation to Pi concentration showed Michaelis-Menten kinetics with Pmax = 0.46% DW and Km 1.19 μM. N content was also measured and P and N content were found to be related. Second, it was analysed how Ulva lactuca material decomposed over time in an agricultural sandy soil when incorporated in June (2013). The mesh bag method was used to monitor the dry matter decomposition. The relative decomposition rate of Ulva lactuca was found to be 0.0413 d. In addition, the effects of seaweed fertiliser on two crops, lettuce and mustard, was studied. Mustard plant height and lettuce diameter were measured throughout the course of the experiment. After harvest, shoot and root dry weight were determined and P and N content were measured. Ulva lactuca application increased N content (p<0.05) and P content (p<0.10) of mustard plants, either due to Ulva lactuca application or the reduced dry matter production. Ulva lactuca application had a negative effect on crop appearance in both crops and reduced shoot dry matter in lettuce (p<0.05) and mustard (p<0.10). A delay in crop growth and development was visible under Ulva lactuca treatment, but the exact functioning of Ulva lactuca in crop performance remains unknown. Table of
Face Detection with a portable architecture in Real Time

Nowadays, security is a top priority. In fact, biometrics uses cutting-edge technologies to identify terrorists and criminals. But the practice of distinguishing humans based on intrinsic physical or behavior traits goes back thousands of years. With the widespread use of computers in the late 20th century, new possibilities for digital biometrics emerged and new technologies were generously used. Among these, we remember high resolution security video cameras and drones. So, the aim of the present project is to study and explain the features of these technologies, especially the ones of the the Phantom 4 Pro+ aircraft and analyze its operating methods in order to identify human faces during live streaming of videos. For this purpose, it will be used Paul Viola and Michael Jones’ face detection algorithm, which includes Haar features and cascade classifiers to identify faces, eyes and ears of an individual.