Miracle-dz/UniFAR-FTUs huggingface dataset:
بررسی ویژگیهای روانسنجی نسخه فارسی شاخص بهزیستی پنج سوالی سازمان بهداشت جهانی

مقدمه: رواج رویکرد مثبت­گرا به سلامت و بهداشت روانی، موجب اقبال محققان به کاربرد مقیاس­های ساخته­شده مبتنی بر این رویکرد شده است. یکی از این مقیاسها، شاخص بهزیستی پنج سوالی سازمان بهداشت جهانی (WHO-5) است. پژوهش حاضر با هدف بررسی خصوصیات روانسنجی نسخه فارسی این شاخص انجام شد. روش: روش پژوهش توصیفی و از نوع همبستگی بود. نمونۀ پژوهش، شامل 498 نفر از دانشجویان بود که با استفاده از روش نمونه­گیری داوطلبانه انتخاب شدند که به شاخص WHO-5، فرم کوتاه پرسشنامه افسردگی بک و پرسشنامه شادکامی آکسفورد (OHQ-SF) پاسخ دادند. یافته­ها: نتایج تحلیل عاملی اکتشافی و تاییدی نشان داد که ساختار عاملی این شاخص تک عاملی است. همسانی درونی سوالات این شاخص بالا و میزان آلفای کرونباخ آن برابر با 89/0 بوده، همچنین ضریب بازآزمایی آن برابر با 82/0 بود. همبستگی معنی­دار بین نمرات این شاخص با نمرات شادکامی بیانگر روایی همگرای مناسب (57/0) با نمرات افسردگی نشان­دهندۀ روایی واگرایی مناسب (61/0-) آن بود. به­طور کلی نتایج پژوهش نشان داد که شاخص بهزیستی این شاخص، ابزاری معتبر و روا در اندازه­گیری بهزیستی ذهنی و کیفیت زندگی افراد بهنجار است. نتیجه­گیری: استفاده از شاخص معتبر جهانی، ناوابسته به فرهنگ، تک بعدی و کوتاه برای ارزیابی بهزیستی ،تسهیل کنندە انجام مطالعات بالینی و پژوهشی در حوزه­های مختلف پزشکی، روانشناختی و روانپزشکی است.
Analysis Refactoring with Tools

The drive for this report is to inaugurate the innumerable techniques espoused by the refactoring tools in coding development. The software product is a very complex and time-consuming process of development. Difficulty understanding and maintaining poorly designed software systems Software maintenance can take up to 50% of total development costs for software production. As a modus operando, the refactoring tools purpose ultimately to amend the basis codes into an easier and more comprehensible way. Moreover, refactoring succors to check the trifle of the coding procedure. This is apparent through having deliberation on the program catalog, precision and the use of the deconstruct trees. Refactoring tools are convenient for innumerable observes done by the human beings. Software refactoring has a direct impact on reducing the cost of software maintenance by changing the internal structure of the code without changing its external behavior. So the time taken to process as well as doing a critical analysis of complex codes is reduced. This report proposes to have a precarious scrutiny on the various use including the pluses of using refactoring tools.
High-Fidelity Spatial Signal Processing in Low-Power Mixed-Signal VLSI Arrays

Author(s): Joshi, Siddharth | Advisor(s): Cauwenberghs, Gert | Abstract: Machine learning and related statistical signal processing are expected to endow sensor networks with adaptive machine intelligence and greatly facilitate the Internet of Things (IoT). As such, architectures embedding adaptive and learning algorithms on-chip are oft-ignored by system architects and design engineers, and present a new set of design trade-offs. We focus on topologies efficiently implementing mixed-signal matrix-vector multiplication for applications in spatial filtering for IoT, where substantial processing gain in the analog domain alleviates the need for highly accurate and energy-consuming analog-to-digital conversion. We present a micropower, high-dynamic-range multichannel multiple-input multiple-out (MIMO) mixed-signal linear transform system, with analog signal path and digital coefficient control, composed of an array of 14-bit Nested Thermometer Multiplying DACs (NTMDACs) implementing analog multiplication, and variable gain amplifier (VGA) implementing accumulation. Implemented in 65nm CMOS, the NTMDAC MISO system-on-chip measures 84 dB in interference suppression at 2 pJ of energy per mixed-signal multiply-accumulate. We demonstrate state-of-the art performance on two tasks, spectrally oblivious interference suppression in communication signals and EEG signal separation. We then provide experimental demonstration of the use of a MIMO mixed-signal linear-transform system within a radio-frequency receiver chain. Over-the-air experiments demonstrating signal separation for two broad-band modulated signals further validate the adaptive beamforming capabilities under severe multipath conditions even in the absence of line-of-sight communication path.In order to mitigate adverse effects of radix errors and capacitive mismatch encountered in compact low-power realizations of high-resolution, high-dimensional MIMO analog processing systems, we introduce Stochastic Successive Approximation, or S2A, as an on-line adaptive optimization algorithm amenable to efficient implementation in massively parallel analog hardware. S2A offers a direct alternative to stochastic gradient descent overcoming several of its shortcomings, such as its sensitivity to analog mismatch model errors, while improving on the rate of convergence for high-dimensional analog computation. The S2A algorithm enables convergence to values closer to the optimal when facing non-convex optimization landscapes induced by mismatch in capacitive multiplying digital-to-analog converter components when applied to adaptive analog signal processing. We experimentally demonstrate, in fewer than 25 iteratations of S2A, 65 dB of processing gain in adaptive beamforming, over-the-air, multipath interferer suppression.
Applications Research of Machine Learning Algorithm in Translation System

In recent years, machine translation has made outstanding achievements, but there are also the problems such as fuzzy rules, insufficient data and improper order in the machine translation practice, which makes it still unsatisfactory. Machine learning can abstract learning features, and establish complex mapping relationship between input and output signals, and effectively improve the problems existing in the translation system. The concepts and common models of machine learning and deep learning are given in this paper. Based on the neural model, the paper analyses the rules, structure and models of machine translation system. According to the requirement of the translation system, this paper develops a translation system based on machine learning, and gives the design process of the pre-processing module, the coding module, the attention module and the decoding module. Practice has proved that the system has a good performance in translation performance.
Control system for doubly fed induction generator

A controller (28) for a doubly fed induction generator (12, 20) adjusts control signals to a rotor side converter (24) and line side converter (22) to adjust rotor current when a voltage transient ona utility grid (10) occurs, so that the doubly fed induction generator can ride through the transient. The controller can also turn off the transistors of the rotor side converter (24) to reduce rotorcurrent and/or activate a crowbar (42) to reduce the voltage of the DC link (26) connecting the converters (22, 24) when significant voltage transients occur on the grid (10). This permits continuedoperation of the DFIG system without disconnecting from the grid.
Monitoring Release Logs at Adyen: Feature Extraction and Anomaly Detection

Monitoring the release logs of modern online software is a challenging topic because of the enormous amount of release logs and the complicated release process. The goal of this thesis is to develop a pipeline that can monitor the release logs and find anomalous logs, automating this step with anomaly detection and reducing the required manual effort. We improve the pipeline from the recent work of Microsoft, enabling it to monitor logs with different severity levels and extremely long sequences. We first use IPLoM and its reconciling step for raw logs to obtain log events and then use log event sets, a simplified version of log sequences, for anomaly detection. The outlier scores of log event sets are calculated using anomaly detection algorithms, and those with an outlier score higher than the threshold are clustered to reduce the number of output. In the final output result, we propose two ranking functions to sort the potential anomalous clusters and only show the top 10 results. Another complementary step beside anomaly detection is designed to capture recurrent anomalies in known clusters that have seen before. By finding the optimal parameters for hierarchical clustering, nearest neighbor distance, and LOF, we test the performance of pipeline on Adyen log data and make our suggestions. Finally, we also test the robustness of the pipeline with two types of artificial data sets.
Power Side Channel Analysis and Anomaly Detection of Modular Exponentiation Method in Digital Signature Algorithm Based Fpga

In this study, digital signature application was performed on FPGA with classical RSA and Chinese Remainder Theorem (CRT). The power consumption of the system was observed when the digital signature process was performed on the FPGA. In order to distinguish the modular exponentiation methods as the classical RSA and the Chinese Remainder Theorem (CRT), the anomaly detection method was applied to the digital signature application using the power side channel analysis of the system. According to the obtained result, it is proved that information about the structure of the algorithm executing in the system can be obtained by using the power information consumed by a cryptographic device.
Management of Impacted Maxillary Central Incisor by Sequential Appliance Therapy- A Clinical Case Report

Aim: Presentation of case with successful management of impacted maxillary central incisor. Background: The impaction of permanent central incisors is a well-recognized entity and is usually associated with trauma to one or more primary anterior teeth early in life. Case Description: We report a case in which a buccally displaced impacted central incisor was brought in the arch by biological tooth movement using a combination of removable and fixed orthodontic traction. Conclusion: The chances of non-vitality are naturally much lower when the treatment is initiated at a younger age, due to the presence of a wide apical foramen. Clinical Significance: It is a challenging task for a pediatric dentist to restore esthetics by moving the impacted maxillary incisor back to its natural position. Orthodontic correction, though challenging, is more desirable as the person retains his natural tooth in the arch.
An exploration of the nursing role in a telehealth based stroke secondary prevention program

This research study set out to explore a specialist nursing role in the field of Telehealth for chronic disease management. This study aimed to explore the role of the nurse through measurement of nursing activity during the one-year period of participant follow-up. The study aimed to effect long-term secondary prevention of stroke through an evidence based approach to the management of modifiable cardiovascular risk factors and post stroke depression in the community setting. Research suggests that up to 80% reduction of risk of successive stroke can be achieved if recommendations from evidence-based guidelines are implemented. Notwithstanding these findings a gap exists in the implementation of preventative strategies for stroke survivors in the community. Results from previous research indicate that Telehealth is cost effective and potentially may significantly reduce socioeconomic burden and the probability of successive stroke. A small number of studies have highlighted potential mechanisms through which Telehealth can benefit the stroke survivor, carers, families and health professionals. Researchers have recommended more research into Telehealth in order to develop and to define effective interventions. A pilot randomised controlled trial (RCT) conducted at two metropolitan hospitals showed positive results in risk factor and depression outcomes. Integral to this model of care was the role of the nurse. In the current nurse-led, nested multicentre RCT, study nurses gained increased telephone access, to follow-up and support stroke survivors and their General Practitioners. There were 93 participants (43 intervention) recruited from four metropolitan hospitals over a period of two years. Nursing staff were integrated with the multidisciplinary team in designated stroke centres and General Practitioners in the community. Specialists were available to NURSE – LED TELEHEALTH STROKE PREVENTION 3 participate in shared care. Telephone follow-up was initially attenuated by risk for second stroke, with high-risk individuals receiving a greater frequency of follow-up. The results indicated that nurses engaged in both fundamental nursing process and advanced activities to assist stroke survivors through transitions across the health landscape after stroke. Case management, assessment and care planning were frequent fundamental activities and occupied a relatively large percentage of the nurse’s time. The mean difference in outcome systolic blood pressure was significantly improved in the intervention group. The mean difference in post stroke depression screening score was significantly better for the intervention group. Lifestyle modification in particular physical activity was better in the intervention group. The results add support to the place of nursing in the ongoing care of stroke survivors in the community setting. NURSE – LED TELEHEALTH STROKE PREVENTION 4
High-Volume Testing and DC Offset Trimming Technique of On-Die Bandgap Voltage Reference for SOCs and Microprocessors

Since the VLSI chips were invented, as predicted by Moore’s law, the performance, the power, and the cost of the VLSI chips have been improved, which brought a significant benefit to the economy. However, some of the analog circuits do not get a full benefit from the scaling, due to the increased device variability with transistors in smaller dimension. Under such circumstance, the calibration and trimming techniques are essential to overcome the sensitivity to the process variation. This paper presents the trimming technique to correct the direct current (dc) offset error of the bandgap voltage reference circuit, which complies with the high-volume manufacturing (HVM) requirements. The proposed trimming method consists of the combination of two different sequences, the coarse and fine trimming. The accuracy of the dc offset trimming is evaluated by the newly invented method that complies with the HVM requirements. With a compact silicon area of only 700 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}^{2}$ </tex-math></inline-formula>, the dc offset trimming circuit achieved an accuracy of ±5 mV (<inline-formula> <tex-math notation="LaTeX">$4\sigma$ </tex-math></inline-formula>) as a result of the coarse and fine trimming operations.
Exploration and Mining of Educational data for Analyzing Student's Engagement

In the recent years data mining and analytical tools are used to thoroughly observe and find unanticipated relationships with in the data. They are customized into useful, novel ways for the data owners by extracting insights. A lack of appropriate methods, findings for exploring the student-learning data using advanced feature selection methods has paved way for this idea. Events occurring in the most common sources of diverse learning platforms were identified where ever common engagements are observed facilitating Student participation. Completely anonymous data is used for analysis keeping ethical concerns in mind. LIWC is used for breaking down the text into numerical measures, helped to pop out any interesting patterns in the review comments. This research showcases different available advanced feature selection options while trying to exclude predictors with least impact on the dependant variable. Best subsets, Lasso, Ridge and Elastic net were evaluated with lowest MSE of 0.46 achieved for Ridge regression validated by k-fold technique. The data depicting most common student personalities is classified into 3 groups based on discrete learning events. The contribution of this work is an accurate prediction of linear models entities via validating different feature selection techniques. Student personalities were identified based on most common student participation platforms where both numerical and textual measures were considered for analysis.
Digital simulation of speckle patterns

Digital simulations of speckle patterns are reported. Wavefront propagation and scattering by a diffusely reflecting surface is represented by a generalized Huygens principle for scalar light waves. The surface roughness is expressed by a set of random numbers that are uniformly distributed within variable range. No approximations for the propagation distances are employed between a point source and a surface point as well as between the surface point and an observation one. The 3-dimensionall intensity distributions in the diffraction field of the object that is illuminated by a spherical wave are calculated by adding many wavelets in complex amplitudes of the scattered waves at the observation point. The intensity distributions on the planes parallel and perpendicular to the object are calculated. For analyzing the speckle patterns illuminated by a multimode laser and multiple wavelength lasers incoherent superposition of the speckle patterns arising from superposition from various wavelengths or various source positions are calculated to investigate the effect of speckle suppression by addition of many speckle patterns.
Memory retention of education regarding endotracheal and laryngeal tube intubation -A manikin study-

Purpose: The purpose of this study was to investigate the education retention effect of endotracheal and laryngeal tube intubation using a manikin study. Methods: The study consisted of measuring intubation time, intubation success rate, and confidence of intubation after education. The evaluation o.
A systematic review of usability quality attributes for the evaluation of mobile learning applications for children

The accelerated development of new technology, particularly on mobile application has increased teacher’s responsibility as a learning facilitator. Since technology is improving, many applications keep on coming into the market to fulfill user demands. Mobile learning application is very useful in enriching the mobile learning experience of children. However, some applications fail to assist and increase children knowledge on the subject they studied. Moreover, some children are not interested in mobile applications due to issues such as poor interfaces. In this paper, some previous studies on quality attributes used to evaluate mobile learning applications were reviewed. A systematic literature review approach was employed to search previous papers and studies on usability quality attributes, particularly for mobile learning applications. The search strategy identified numerous studies on usability for mobile learning applications for children. The result showed that the top four usability quality attributes are efficiency, effectiveness; learnability and user satisfaction. The studies reviewed showed that usability evaluations were conducted during implementation phase only, and evaluation is often built as a description of the case study. This study explored the current literatures on the subject matter and stimulates further studies that will lead to improved usability and design of mobile learning applications for children.
Increasing knowledge retention of business statistics through discipline-specific case studies

Abstract The authors perform a classroom experiment to test the effectiveness of discipline-specific case studies on knowledge acquisition and retention in advanced business statistics courses. The results indicate that although students are more engaged in the case studies, overall there is no impact on acquisition or retention of statistics knowledge by having students engage in discipline-specific case studies relative to traditional case studies. However, for high-performing students, discipline-specific case studies have a positive and significant impact on statistical knowledge retention. Moreover, our research reaffirms past research, which has found greater retention for students with higher grade point average, and there exists a significant negative correlation between retention and time.
Extractive document summarization using complex networks

VALVERDE, J. A. Extractive document summarization using complex networks. 2018. 116 p. Dissertação (Mestrado em Ciências – Ciências de Computação e Matemática Computacional) – Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos – SP, 2018. Due to a large amount of textual information available on the Internet, the task of automatic document summarization has gained significant importance. Document summarization became important because its focus is the development of techniques aimed at finding relevant and concise content in large volumes of information without changing its original meaning. The purpose of this Master’s work is to use network theory concepts for extractive document summarization for both Single Document Summarization (SDS) and Multi-Document Summarization (MDS). In this work, the documents are modeled as networks, where sentences are represented as nodes with the aim of extracting the most relevant sentences through the use of ranking algorithms. The edges between nodes are established in different ways. The first approach for edge calculation is based on the number of common nouns between two sentences (network nodes). Another approach to creating an edge is through the similarity between two sentences. In order to calculate the similarity of such sentences, we used the vector space model based on Tf-Idf weighting and word embeddings for the vector representation of the sentences. Also, we make a distinction between edges linking sentences from different documents (inter-layer) and those connecting sentences from the same document (intra-layer) by using multilayer network models for the Multi-Document Summarization task. In this approach, each network layer represents a document of the document set that will be summarized. In addition to the measurements typically used in complex networks such as node degree, clustering coefficient, shortest paths, etc., the network characterization also is guided by dynamical measurements of complex networks, including symmetry, accessibility and absorption time. The generated summaries were evaluated by using different corpus for both Portuguese and English language. The ROUGE-1 metric was used for the validation of generated summaries. The results suggest that simpler models like Noun and Tf-Idf based networks achieved a better performance in comparison to those models based on word embeddings. Also, excellent results were achieved by using the multilayered representation of documents for MDS. Finally, we concluded that several measurements could be used to improve the characterization of networks for the summarization task.
USING THE FLIPPED CLASSROOM MODEL IN TEACHER EDUCATION – IS TURNING THE TABLES THE WAY FORWARD?

This paper presents a flipped classroom project carried out on the Primary Education degree in the University of Cadiz.The initiative responded to the need to introduce a new way of working as students react with a higher level of interest and involvement to practical dynamics than to the traditional theory class. Difficulty in maintaining their focus during a theory class, trying to assimilate the concepts at the same time as they take notes and losing the thread during the explanations because they work at a slower or faster rate than the rest of the group were some of the problems our students were facing. These students would benefit from working with the material before the class at home and at their own pace, freeing up class time for collaborative, practical activities to exemplify and expand the concepts. We analyse the key aspects of this methodology, paying special attention to the advantages and difficulties we faced during its implementation.