Miracle-dz/UniFAR-FTUs huggingface dataset:
“If you don’t agree to be available 24/7, then you have nothing to do in journalism”: the boundary work tactics of precarious journalists

ABSTRACT A satisfactory work–home balance in journalism is valuable for employees and organisations but for citizens as well because the work of journalists in a democracy is vital in terms of keeping the public informed and shaping public debate. Drawing on boundary theory, this study aims to examine how precariously employed journalists manage their role boundaries as they negotiate their work and home life demands. In-depth interviews conducted in 2017 and 2021 allowed us to obtain a longitudinal perspective on boundary work and to detect the tactics to create and maintain the preferred work-home role boundaries. The results show that work comes first with respect to the work–home balance in journalism. In the early years of their career, immersed in a labour-of-love ethic, journalists preferred to integrate work–home boundaries by being journalists 24 hours a day. Over time, a preference for the segmentation of work–home roles emerged along with different boundary work tactics. However, it is often impossible to segment boundaries due to the nature of the profession, and thus many of them leave the profession for public relations. The study makes an original contribution by adding a new boundary work tactic to the previously established typology.
Joint Angle and Range Estimation in Monostatic FDA-MIMO Radar via Compressed Unitary PARAFAC

In this paper, we study the joint range and angle estimation problem based in monostatic frequency diverse-array multiple-input multiple-output (FDA-MIMO) radar, and propose a method for range and angle estimation base on compressed unitary parallel factor (PARAFAC). First, the received complex signal matrix is stacked into a third-order complex signal tensor. Then, we can transform the obtained third-order complex signal tensor into a third-order real-valued signal tensor by employing forward–backward and unitary transformation techniques. Next, a smaller third-order real-valued signal tensor is composed by using compressing the third-order real-valued signal tensor. After that, PARAFAC decomposition is applied to obtain the direction matrix. Lastly, the angle and range are estimated by employing the least square (LS) fitting. The estimation error of the proposed method is about 10% lower than that of the traditional PARAFAC method under the low number of snapshots. When the number of snapshots is high, the performance of the two methods is close. Moreover, the computational complexity of the proposed method is nearly 96% less than those of the traditional PARAFAC methods in the case of low snapshots, while the gap is larger in the case of high snapshots. The superiority and effectiveness of the method are proved by complexity analysis and simulation experiments.
What Are the Necessary Conditions for Wisdom? Examining Intelligence, Creativity, Meaning-Making, and the Big-Five Traits

We investigated whether intelligence, creativity, meaning-making, and the Big-Five traits are necessary conditions for wisdom. We used Amazon’s TurkPrime to recruit 298 participants who ranged from 20 to 73 years of age. Participants completed measures of intelligence, creativity, meaning-making, and the Big-Five traits, along with a battery of self-report and performance wisdom measures. We used principal component analyses to reduce the wisdom battery into self-report and performance wisdom components, followed by necessary condition analysis and segmented regressions to examine whether the cognitive and personality variables under consideration here were necessary conditions for each wisdom component. We found that intelligence was necessary for the performance wisdom component whereas the Big-Five traits were necessary for the self-report wisdom component. This study is the first to demonstrate that high levels of wisdom are unlikely without some level of intelligence and adaptive personality traits.
Nonlinear time series prediction method based on multi-dimensional Taylor network and its applications

For the nonlinear time series, a time series prediction method based on multi-dimensional Taylor network is proposed. The characteristics of this method is that based on the observed data of the nonlinear time series, n-variables first order polynomial difference equations of the system are obtained by the multi-dimensional Taylor network, and the dynamic characteristics can be described without prior knowledge and mechanism of the system, thus realizing the prediction of the nonlinear time series. The Lorenz chaotic time series and the structural vibration response data in lift-up construction safety monitoring are used to show the effectiveness of this method.
Monitoring of specific activities of long-lived radioactive elements along the Mini- Okoro/Oginigba Creek, Port Harcourt

Presence and concentrations of radionuclides could be as a result of natural and human activities. This study examined the associations and differences among soil, sediment and water specific activities of long-lived radioactive element (LLRE). Gamma spectroscopy was used to measure the concentration of the LLRE along the Mini Okoro/Oginigba Creek, Port Harcourt. Specific activities of three selected LLRE were derived. Correlation analysis was carried out to examine associations among the specific activities across different substrates. A strong and a significant negative correlation exists between the specific activities of Water 40K and Soil 232Th (r =-0.721, p 0.05) for 40K, 232Th and 238U respectively. The concentration of solid samples (soil and sediment) is different from the liquid (water) samples. These associations can be attributed to some specific underlying factors. And in other to understand them there is need for more studies. © JASEM Keywords : Radionuclide; environmental monitoring; Niger Delta; Gamma spectroscopy; Environmental management
A Comparative Study of the Impact of teaching methods on Teaching Effectiveness and Pupils’ Skills Acquisition in Home Economics in Upper Basic Classes in Itu Local Government Area of Akwa Ibom State, Nigeria

The study examined the impact of teaching methods on Teaching Effectiveness and Pupils’ skills acquisition in Home Economics in Upper Basic Classes in Itu Local Government Area of Akwa Ibom State, Nigeria. A comparative survey design was adopted for the study. Purposive sampling technique was used to identify the schools that actively teach and practice Home Economics, while one hundred and Eighty-Four (184) teachers (110 Females, 74 Males) were used as respondents in the study, selected through stratified sampling technique. A 15-item Questionnaire tagged: “Teaching Effectiveness for Skills Acquisition in Home Economics Questionnaire (TESAHEQ)”, was used for data collection in the study, arranged in a 4-point Likert Scale of Strongly Agree (SA), Agree (A), Disagree (D) and Strongly Disagree (SD). The questionnaire was validated by one (1) experts in test and measurement and one (1) expert in home economics. Test-retest reliability index was used to ascertain the reliability of the Questionnaire and it produced a reliability coefficient of 0.88, proving the instrument reliable enough for use in the study. Two research questions and 2 hypotheses were formulated to guide the study. Descriptive statistics of mean was used to answer the research questions. Independent T-test was used to test the hypothesis formulated at 0.05 level of significance. The findings in the study revealed that learnercentered methods of teaching such as discussion, guideddemonstration, role-play, and dramatization method are highly effective in facilitating skills acquisition in pupils than teachercentered method of teaching such as descriptive, lecture, demonstration/flipped classroom method. The result also proved a significant impact of Teaching Effectiveness on Pupils’ Skills Acquisition in Home Economics in Upper Basic Classes based on gender of the pupils. Based on the study, it was recommended among others that workshops, seminars and other modes of training be given to teachers to update their teaching skills for higher teaching effectiveness.
Screening for Diabetic Nephropathy by Measuring Micro-Albuminuria on 24 Hour Urine

Micro-albuminuria is an early marker of diabetic nephropathy (DN) but also a predictor of morbidity and mortality. The aim of our work was to assess the frequency of DN by measuring micro-albuminuria (mA) on 24h urine. This was a retrospective study carried out in 2018 in 450 diabetic patients having undergone an mA assay in the multipurpose laboratory of the ABASS NDAO hospital, in Dakar, Senegal. The mA was assayed on 24h urine with the BTS-350 (Barcelona, Spain). The median age in our study population was 56.31 ± 12.75 years with a predominance of the 40-59 years age group (48%). We found a predominance of women (62%) with a sex ratio of 0.61. The prevalence of DN was 26% in the general population with a predominance in subjects aged over 60 (15.8%), female (16.9%) and in T2D (21.6%) with a statistically significant difference (p <0.05). Proteinuria was found in 6% of patients (n = 27) with a predominance in women aged over 60 with type II diabetes. Diabetic nephropathy is a frequent complication of diabetes mellitus. Its prevention requires an early diagnosis by the dosage of micro-albuminuria. Its dosage should be popularized especially in subjects over 50 years to improve the quality of care.
ML Reproducibility Challenge 2021 [Re] Differentiable Spatial Planning using Transformers

This report covers our reproduction effort of the paper ‘Differentiable Spatial Planning using Transformers’ by Chaplot 3 et al. [1]. In this paper, the problem of spatial path planning in a differentiable way is considered. They show that 4 their proposed method of using Spatial Planning Transformers outperforms prior data-driven models and leverages 5 differentiable structures to learn mapping without a ground truth map simultaneously. We verify these claims by 6 reproducing their experiments and testing their method on new data. We also investigate the stability of planning 7 accuracy with maps with increased obstacle complexity. Efforts to investigate and verify the learnings of the Mapper 8 module were met with failure stemming from a paucity of computational resources and unreachable authors
From Russell and Carnap to Stone

The first aim of this paper is to elucidate Russell’s construction of spatial points, which is to be considered as a paradigmatic case of the "logical constructions" that played a central role in his epistemology and theory of science. Comparing it with parallel endeavours carried out by Carnap and Stone it is argued that Russell’s construction is best understood as a structural representation. It is shown that Russell’s and Carnap’s representational constructions may be considered as incomplete and sketchy harbingers of Stone’s representation theorems. The representational program inaugurated by Stone’s theorems was one of the success stories of 20 century’s mathematics. This suggests that representational constructions à la Stone could also be important for epistemology and philosophy of science. More specifically it is argued that the issues proposed by Russellian definite descriptions, logical constructions, and structural representations still have a place on the agenda of contemporary epistemology and philosophy of science. Finally, the representational interpretation of Russell’s logical constructivism is used to shed some new light on the recently vigorously discussed topic of his structural realism.
The Blood Gas Lactate and Serum Glucose Levels of the Patients Admitted to the Pediatric Emergency Department with Seizure

Aim: In this study, we aimed to retrospectively evaluate the patients presenting to the pediatric emergency service with seizure, to compare blood gas lactate and serum glucose levels according to the seizure types and to determine the effects of these values on prognosis. Material and Methods: The patients presenting to the pediatric emergency service with seizure and aged between 1 month and 18 years who were admitted to Emergency Department (ED) between January 2012 and December 2016 were evaluated retrospectively. Results: In this study 69 (43, 4%) of the patients were female and 90 (56, 7.6%) were male. The mean age of the patients was 69.8 ± 62.8 months. When the type of seizure was assessed, 73 (45, 9%) patients had febrile seizure, 75 (47, 2%) had afebrile seizure and 11 (6, 9%) patients had status epilepticus. There was a significant statistically relationship between the type of seizure and the serum glucose examined at the time of admission (p=0.015). There was no significant difference between seizure type, blood gas lactate, seizure history, family history of seizures, duration of stay in emergency department, need for hospitalization and seizure recurrence. The mean seizure time of the patients was 3.8 ± 3.1 min. There was a negative correlation between seizure duration and blood gas pH and positive correlation between seizure duration and serum glucose. 84 (52, 8%) patients had a blood gas lactate value of ≥ 2 mmol/L at admission. The 41 patients (25, 7%) had hyperglycemia at admission. There were no patients with hypoglycemia. The mean emergency department observation duration was 8 ± 2.9 h. It was observed that 12 (7, 5%) patients were hospitalized. The mean length of stay in hospital was 3 ± 1.8 days. The mortality rate was 0%. Seizure recurrence was observed in 27 (17%) patients. There was a significant statistically relationship between the seizure recurrence and the CO2 in the blood gas (p=0.04). ROC analysis of seizure recurrence revealed lactate cut off is 1.9 mmol/L and blood glucose cut off is 110 mg/dL. This study contributes to literature in terms of illuminating efficient factors on clinical courses of seizures. Pediatric Emergency doctors should maintain seizure patients with high lactate and glucose levels at the time of admission under close clinical follow-up after discharge in terms of seizure recurrence.
Simulation Study of New-type EPS Power Operating Process

As a kind of reliable and green-line emergency power supply,EPS acts more important effect in the field of elevator,fire protection,emergency illumination.Base on the power bidirectional transfers by PWM rectifier,advance a new type EPS emergency power supply with the simple structure,easy control and great efficiency,of which charging and discharging finished by one PWM rectifier.The paper analyzes the operating process of the new-type emergency power supply profoundly,and shows out the result of Matlab simulation.And it will take a important meaning in the process of the profoundly studying and designing EPS emergency power supply.
An Extensive Study for Development of Browser Extensions in Secure Browser Environment

: Browser extensions are small piece of software that are used to extend the capabilities of browsers. Today all major browsers support extensions to extend their functionalities. However, extensions are also prone to several attacks due to their close relationship with the browser environment. Extensions have been abused to gather private information, password theft etc. Thus, to protect the system and user data from malicious attacks, several practices have been introduced which must be followed by extensions developer to develop a safe extension. This paper presents several of such practices adopted by major browsers to counter various attacks which are done either by targeting extensions or by using extensions.
DETECTION AND EXTRACTION OF BUILDING FOOTPRINT FROM HIGH- RESOLUTION SATELLITE IMAGERY

Remote sensing satellite images are developed rapidly regarding quantity, quality, and applications to detect and extract various natural and artificial features on earth's surface like(vehicles, buildings, trees, roads, water, aircraft, ships). These satellite images provide important information in various applications like urban planning, disaster management, and environmental management. Different algorithms and methods are introduced by researchers to extract the specified features from satellite images. In an urban scenario, buildings are one among the foremost elementary structures and play an important role in the field of urban development, urban planning, climate studies, disaster management, map making, land use analysis, and change detection. The study aims are to extract building footprints employing a machine learning algorithm. various methods for extraction of building footprints are discussed in various studies However, extraction of rooftop of buildings from an metropolitan place has been a difficult task, since building rooftops have different shape, size, and spectral properties. In addition to that, other urban features, such as, road, barren land, etc. exhibit spectral properties similar to that of building rooftops. so that building extraction technique has become a vital and tough research problem receiving better recognition. The proposed technique uses a Machine Learning algorithm for classifying the building and non-building pixels. To eliminate false-detected building pixel use median filtering, morphological operator, and connected component labelling are used . the technique has been evaluating by pixel and object-based criteria with the consideration of precision, recall, quality of building(urban object). Index Term: a satellite image, machine learning, connected component, filtering
Food Preservative Characteristics of Dehydrated Murunga (Moringa Oleifera) Leaf Powder

Murunga (Moringa oleifera) is an underutilized plant in Sri Lanka with food, nutritional and medicinal value. This study was carried out to evaluate the food preservative characteristics of dehydrated Murunga leaf powder. Soya meat (textured soy protein) and Dhal curries (cooked) and boiled rice (Suwandel variety and red rice) treated with different levels of Murunga leaf powder (1.5, 2.5, 4.5 and 6%) were selected for this experiment. Sensory evaluation was conducted with the help of 30 untrained panelists using a five point hedonic scale in order to identify the acceptable level of Murunga leaf powder in these foods. Protein content (Kjeldahl method) and pH (using a pH meter) of these food samples in each experiment were determined just after cooking and after 24 hours. Microbial counts and sensory acceptability of the product were determined in eight hours interval in ambient conditions. Significant differences (P<0.05) were observed in the reduction of protein content and increase of pH of above cooked meals with Murunga leaf powder when compared with the control at the end of storage period. There were significant differences (P<0.05) in the total plate counts in all cooked food samples with Murunga leaf powder when compared to the control. However, no coliform counts were reported for any of the food samples. After 16 hours, colour, flavour, odor and overall acceptability of Murunga leaf powder treated food samples were significantly different (P<0.05) compared to the control samples. This study revealed that the dehydrated Murunga leaf powder could be potentially used to extent the shelf life of cooked food products such as rice and curry.
Commonsense Reasoning for Question Answering with Explanations

Commonsense reasoning is an important capability for a range of AI applications such as text understanding. Neural models for commonsense reasoning QA often directly predict answers based on learned representations of language. In this work, we consider the challenge of producing an explicit reasoning step for a commonsense QA system. We propose a latent-variable model that identifies what type of knowledge from an external knowledge base may be relevant to answering the question, computes the commonsense inferences, and predicts the answer. Our method can therefore learn to provide posterior rationales for why a certain answer was chosen. Experimental results show that the model can identify the correct reasoning step in twice as many examples compared to an existing unsupervised approach for producing explanations, while still maintaining comparable accuracy to end-to-end pretrained models.
Content-based product image retrieval using squared-hinge loss trained convolutional neural networks

Convolutional neural networks (CNN) have proven to be highly effective in large-scale object detection and image classification, as well as in serving as feature extractors for content-based image retrieval. While CNN models are typically trained with category label supervision and softmax loss for product image retrieval, we propose a different approach for feature extraction using the squared-hinge loss, an alternative multiclass classification loss function. First, transfer learning is performed on a pre-trained model, followed by fine-tuning the model. Then, image features are extracted based on the fine-tuned model and indexed using the nearest-neighbor indexing technique. Experiments are conducted on VGG19, InceptionV3, MobileNetV2, and ResNet18 CNN models. The model training results indicate that training the models with squared-hinge loss reduces the loss values in each epoch and reaches stability in less epoch than softmax loss. Retrieval results show that using features from squared-hinge trained models improves the retrieval accuracy by up to 3.7% compared to features from softmax-trained models. Moreover, the squared-hinge trained MobileNetV2 features outperformed others, while the ResNet18 feature gives the advantage of having the lowest dimensionality with competitive accuracy.
ECE594 Final Project Report: Automatic Detection of Machine-Generated Text

Modern large-scale generative language models (GLMs) are capable of generating multi-paragraph texts that are virtually indistinguishable from human-written text. The task of automatic machine-generated text detection has garnered much research and social interests. However, the current approaches are heavily dependent on the decoding strategy used to generate the text: the classiﬁer that works for one decoding strategy does not work at all for the other. Hence, in this project, we explore to use Fisher Information Matrix to create hidden representations for the textual input and utilize representation learning, one-class techniques, outlier detection algorithms and nov-elty detection algorithms to achieve a more generalizable detector for machine-generated texts. As a result, our method achieve a gain of 0 . 18 in AUC for the cross decoding strategy performance while still maintaining a acceptable performance for same decoding strategy performance.
International Journal of Electrical and Computer Engineering (IJECE)

Received Nov 3, 2021 Revised Jan 4, 2022 Accepted Jan 25, 2022 Risk management in software development has always been one of the necessities of software project management. The logical nature of software projects and products has caused several challenges and risks in these projects. On the other hand, with the emergence of agile methodologies, especially Scrum, and extreme programming (XP) methodologies, in recent years, this issue has become more serious. This is mainly because emphasizing limited documentation in these methodologies has caused these methods to pay little attention to some aspects of project management, particularly risk management. Concentrating on this challenge, the current study has proposed a risk management model in the hybrid methodology, combining Scrum and XP. Using this model in a case study shows this model's success in achieving risk management purposes. The results of this study indicate an appropriate reduction in the number of reworks, change requests, identified risks, and occurred risks. Moreover, the number of eliminated risks and team productivity have increased.