Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of the Placement Success in the Undergraduate Placement Examination According to Decision Trees with Bagging and Boosting Methods

The purpose of this study is to classify the data set which is created by taking students who placed to universities from 81 provinces, in accordance with Undergraduate Placement Examination between the years 2010-2013 in Turkey, with Bagging and Boosting methods which are Ensemble algorithms. The data set which is used in the study was taken from the archives of Turk-Stat. (Turkish Statistical Institute) and OSYM (Assessment, Selection and Placement Center) and MATLAB statistical software program was used. In order to evaluate Bagging and Boosting classification performances better, the success rates of the students were grouped into two groups. According to this, the provinces that were above the average were coded as 1, and the provinces below the average were coded as 0 and dependent variables were created. The Bagging and Boosting ensemble algorithms were run accordingly. In order to evaluate the prediction abilities of the Bagging and Boosting algorithms, the data set was divided into training and testing. For this purpose, while the data between 2010-2012 yearrs were used as training data, the data of the year 2013 were used as testing data. Accuracy, precision, recall and f-measure were used to demonstrate the performance of the methods in the study. As a result, the performance in consequence of "Bagging” and “Boosting” methods were compared. According to this; it was determined that in all performance measure marginally “Boosting” method produced better results than the “Bagging” method.
Framework design on Bridge Monitor system with IOT Sensor

:- Now-a-days most of the bridges cross the world build rivers and oceans, which are subjected to maintain it for the life time but at a certain time it is going to expired. Though it is dangerous but they are still in use. Due to rapid occurrence of cyclonic conditions or heavy vehicle loads these bridges may collapsed where the water level is increased and leads to destruction. This may harm the users. So, these bridges required a special care without manual network. So these bridges require a weight sensor, water level point sensor and WIFI module. This system protects from heavy loads ,water level and pressure. If any issue takes place then it generates the signals (alarm) through buzzer with IOT device and auto barriers which is connected to the serve. The achievements have brought a real time monitors systems by using IOT.
Responding to FASD: what social and community service professionals do in the absence of diagnostic services and practice standards

Purpose The purpose of this paper is to ascertain the experiences and practices of social and community service professionals working with Fetal Alcohol Spectrum Disorder (FASD) or suspected FASD in New Zealand. Design/methodology/approach The research examined professionals’ empirical experience of working with FASD or suspected FASD. In total, 21 participants working in practice settings relevant to dual diagnosis were interviewed, with each interview being of 1–2 h duration. The interview data were transcribed and coded using the constant-comparative method in order to derive themes. Findings Participants identified a number of barriers to understanding or working with FASD: the limited knowledge of FASD and the absence of FASD from training contexts, difficulty obtaining institutional support and the challenge of working without a diagnosis. Participants also articulated a range of strategies for approaching FASD within the identified barriers of their practice contexts, specifically: working with alternative diagnoses, focussing on adjusting expectations, adopting a strengths-based approach, actively advocating for clients when working with other sectors and systems and focussing on prevention education. Originality/value This research expands the knowledge base for social and community service practice in contexts where FASD maybe a factor contributing to poor health and well-being outcomes for clients. It clarifies the challenges that professionals face when encountering FASD or suspected FASD in their work, reveals key gaps in individual and systemic knowledge about FASD and provides new insight into what professionals do to address these challenges. It also adds to the body of research concerning FASD in the Australasian context more broadly.
A Generic Solver Combining Unsupervised Learning and Representation Learning for Breaking Text-Based Captchas

Although there are many alternative captcha schemes available, text-based captchas are still one of the most popular security mechanism to maintain Internet security and prevent malicious attacks, due to the user preferences and ease of design. Over the past decade, different methods of breaking captchas have been proposed, which helps captcha keep evolving and become more robust. However, these previous works generally require heavy expert involvement and gradually become ineffective with the introduction of new security features. This paper proposes a generic solver combining unsupervised learning and representation learning to automatically remove the noisy background of captchas and solve text-based captchas. We introduce a new training scheme for constructing mini-batches, which contain a large number of unlabeled hard examples, to improve the efficiency of representation learning. Unlike existing deep learning algorithms, our method requires significantly fewer labeled samples and surpasses the recognition performance of a fully-supervised model with the same network architecture. Moreover, extensive experiments show that the proposed method outperforms state-of-the-art by delivering a higher accuracy on various captcha schemes. We provide further discussions of potential applications of the proposed unified framework. We hope that our work can inspire the community to enhance the security of text-based captchas.
Formal concept analysis in information science

Formal Concept Analysis (FCA) is a method for data analysis, knowledge representation and information management that is widely unknown among information scientists in the USA even though this technology has a significant potential for applications. FCA was invented by Rudolf Wille in the early 80s (Wille, 1982). For the first 10 years, FCA was developed mainly by a small group of researchers and Wille’s students in Germany. Because of the mathematical nature of most of the publications of that time, knowledge of FCA remained restricted to a group of “insiders”. Through funded research projects, FCA was implemented in several larger-scale applications, most notably an implementation of a knowledge exploration system for civil engineering in cooperation with the Ministry for Civil Engineering of North-Rhine Westfalia (cf. Eschenfelder et al. (2000)). But these applications were not publicized widely beyond Germany. During the last 10 years, however, FCA has grown into an international research community with applications in many disciplines, such as linguistics, software engineering, psychology, AI and information retrieval. This shift is due to a variety of factors (cf. Stumme (2002)). A few influential papers stirred interest for FCA in several fields. For example, Freeman and White’s (1993) paper on social network analysis initiated an interest for the use of FCA software among sociologists. In software engineering, several FCA papers (such as Fischer (1998) and Eisenbarth et al. (2001)) won Best Paper Awards at conferences (Snelting, to appear) because FCA happened to facilitate a type of analysis which was previously not available in that field. As Stumme (2002) explains, FCA shifted emphasis to applications in computer science partly due to a merger with the Conceptual Graphs community (Sowa, 1984). An overview of the relationship between Conceptual Graphs and FCA is provided by Mineau et al. (1999). Some of the structures of FCA appear to be fundamental to information representation and were independently discovered by different researchers. For example, Godin et al.’s (1989) use of concept lattices (which they call “Galois lattices”) in information retrieval is based on an independent discovery by
Trust based energy efficient data collection with unmanned aerial vehicle in edge network

Large‐scale sensing devices spread over a wide area and compose the supervisory control and data acquisition (SCADA) system to remotely control and monitor a specific process through collecting the sensing data from the working field. However, the trustworthy and energy efficient data collection is still a challenging issue for large‐scale Internet of thing systems. In this article, a trust based energy efficient data collection with unmanned aerial vehicle (TEEDC‐UAV) scheme is proposed to prolong lifetime with trustworthy style. First, in TEEDC‐UAV scheme, an ant colony based unmanned aerial vehicle (UAV) trajectory optimization algorithm is proposed in which form the most data anchors in the working field with the trajectory as short as possible. Thus, the sensor nodes in SCADA system can be responsible for the least amount of data and greatly extend network life. Second, a trust reasoning and evolution mechanism is proposed to identify the trust degree of sensor nodes, and only trusted data will be collected so that the quality of data collection can be proved. In our proposed trust mechanism, the UAV can sense and collect data itself, so that data can be used as the baseline to identify the trust degree of sensor nodes. Finally, proved by sufficient experiment results, our proposed TEEDC‐UAV scheme can find an optimized data collection trajectory efficiently, which helps the energy consumption of the network become much more balanced. Compared with previous strategies, the network life is greatly improved by 48.9%. Meanwhile, the trust mechanism proposed in this article can also greatly improve the identification accuracy of node trust degree, which reached 91% when consuming only 8% network life.
Theoretical studies on electronic and magnetic properties of ultrathin Mo nanowires

We present a detailed theoretical study on electronic and magnetic properties of Mo nanowires with different structures. The ultrathin nanowires of this 4d transition metal show a unique behavior for the stability. We notice that zigzag structure is stable at the lower values of nearest neighbor distance. On slightly stretching the nanowire, the ladder structure is preferred while the dimerized structure, with the highest value of cohesive energy, is the most stable structure at larger nearest neighbor distances. This work suggests that magnetic ordering of Mo nanowires can be tuned with structure. The linear and ladder structures of Mo nanowires show antiferromagnetic ordering. Equilateral zigzag structure prefers a nonmagnetic state whereas the planar zigzag structure is ferromagnetic. The dimerized structure stands out showing degenerate nonmagnetic and ferromagnetic states. The highest value of magnetic moment (∼1.16 μB/atom) is predicted for linear chains. Relative break force values suggest that the.
Sharing research with academia and beyond: Insights from early career researchers in Australia and Japan

Quality scholarly research outputs, such as peer reviewed journal articles published in reputable journals, are essential for early career researchers' (ECRs) vocational success while also offering benefits for their institutions. Research outputs destined for audiences beyond academia are also increasingly valued by funders, end users, and tertiary institutions. While there is an expectation that ECRs may create diverse research outputs for an array of audiences, the kinds of research output texts produced by ECRs for varied audiences warrants further investigation. In addition, the routes of dissemination that ECRs use to share their academic research outputs to secure impact beyond academia are not well understood. Drawing on semi‐structured interviews of 30 respondents in Australia and Japan, we explore the research‐sharing practices of ECRs, finding that ECRs may potentially create a wide range of research‐informed texts for end users beyond academia, using an array of methods for dissemination. The examples of the output text types and dissemination routes we provide in this paper can be used to inspire ECRs and also more senior academics to share their research more broadly, and perhaps more effectively, and can be used by publishers to improve research impact and support ECRs' research translation.
A Deep Research in Classic Classification Network

This article is a further study and reflection of the classification network after the completion of the article “The Comparison and Analysis of Classic Convolutional Neural Network in the Field of Computer Vision”. Different from previous work comparing network performance of VGGNet and ResNet, this paper compares and analyzes the importance of depth, breadth and residual learning of GoogLeNet and ResNet in convolutional neural networks. And considering that most of the time, the basic classification network nowadays appears in the form of backbone in the detection and segmentation network, this paper compares the advantages and disadvantages of several major classification networks as backbones.
The Effect of Spirulina Supplementation and Combined Aerobic-Strength Training on Serum Homocysteine of Sedentary Females

Background: The increasing rate of mortality due to cardiovascular disease has been reported in people with low physical activity worldwide. Objectives: The purpose of the present study was to investigate the effect of spirulina supplementation and combined aerobicstrength training on serum homocysteine of inactive females. Methods: It was a semi experimental study with pre and post-test design. The subjects consisted of 37 sedentary women who participated voluntarily in the study and were randomly divided into supplement; training and supplement+training groups. Measuring the study variables including serum homocysteine and anthropometry variables were done in pre-test and posttest phases. The data were analyzed using paired t test and ANOVA. Results: The results suggested that spirulina supplementation and combined aerobic-strength training had a positive influence on serum homocysteine levels in sedentary women (P = 0.003, P = 0.04 respectively) with a greater influence by the combined effect of exercise and supplement (P = 0.001). Conclusions: The results indicate that homocysteine as an independent cardiovascular disease risk factor in sedentary women can be modified by spirulina and combined strength and aerobic exercise.
Leveraging the growth of open access in library collection decision making

A primary goal for collection management is assessing the relative value of continuing information resources. A variety of new environmental factors and data are pertinent to relative value. One of the emerging metrics is the degree to which the articles within a subscription journal are also available open access (OA). That OA level directly affects the value of a journal subscription. This paper outlines a theoretical model for accounting for open access in decision making by proposing an Open Access-adjusted Cost per Download metric. Refinements to the metric are also discussed, as well as how it can be applied, and the broader scholarly communication implications of leveraging open access in library decision making.
Sedation or Anesthesia Before Cervical Cancer Brachytherapy

Cervical cancer (CC) is a neoplasm with great potential for prevention, but it is still a public health problem in most developing countries. There are no significant differences between the use of high dose rate (HDR-BT) and low dose rate (LDR-BT) brachytherapy when considering overall, disease specific and recurrence free survivals. Cervical dilatation is mandatory for the insertion of intrauterine tandems in any of the techniques cited above. Pain and discomfort may eventually be limiting factors of the outpatient procedure and may lead to unsatisfactory results in terms of adequate position of the applicator set. We reviewed the current sedation and anesthetic options for comfort and safety procedures when performing HDR-BT. Int Gyn & Women’s Health Copyrights@ Antonio Cassio Assis Pellizzon. Citation: Antonio C A P. Sedation or Anesthesia Before Cervical Cancer Brachytherapy. Int Gyn & Women’s Health 2(5)2018. IGWHC. MS.ID. 000146. DOI: 10.32474/IGWHC.2018. 02.000146. 197 are suitable for inpatients. In Brazil and probably worldwide, besides US and Europe, it is underused due to the shortage of anesthesiologists.
Data annotation system for intelligent energy conservator in smart building

The concept of smart building includes the optimization of energy usage in a building. One of the possible solutions for this is to adaptively adjust appliances utilization according to activity level in the building. Thus, an intelligent activity estimation system needs to be developed. However, massive annotated dataset is necessary to train the system. Therefore, we propose a system that enables rapid data annotation to collect the massive dataset. With the proposed system, an image can be annotated within 4.8 seconds in average.
Security of IoT Application Layer Protocols: Challenges and Findings

IoT technologies are becoming pervasive in public and private sectors and represent presently an integral part of our daily life. The advantages offered by these technologies are frequently coupled with serious security issues that are often not properly overseen or even ignored. The IoT threat landscape is extremely wide and complex and involves a wide variety of hardware and software technologies. In this framework, the security of application layer protocols is of paramount importance since these protocols are at the basis of the communications among applications and services running on different IoT devices and on cloud/edge infrastructures. This paper offers a comprehensive survey of application layer protocol security by presenting the main challenges and findings. More specifically, the paper focuses on the most popular protocols devised in IoT environments for messaging/data sharing and for service discovery. The main threats of these protocols as well as the Common Vulnerabilities and Exposures (CVE) for their products and services are analyzed and discussed in detail. Good practices and measures that can be adopted to mitigate threats and attacks are also investigated. Our findings indicate that ensuring security at the application layer is very challenging. IoT devices are exposed to numerous security risks due to lack of appropriate security services in the protocols as well as to vulnerabilities or incorrect configuration of the products and services being deployed. Moreover, the constrained capabilities of these devices affect the types of security services that can be implemented.
OLEUM Project. Sensory panels analysis of commercial virgin olive oil samples. Results from the first and second year of the OLEUM project

A set of 334 commercial virgin olive oil (VOO) samples were evaluated by six sensory panels during the H2020 OLEUM project. Sensory data were elaborated with two main objectives: i) to classify and characterize samples in order to use them for possible correlations with physical-chemical data and ii) to monitor and improve the performance of panels. After revision of the IOC guidelines in 2018, this work represents the first published attempt to verify some of the recommended quality control tools, in order to increase harmonization among panels. Specifically, a new "decision tree" scheme was developed, and some IOC quality control procedures, were applied. The adoption of these tools allowed for reliable classification of 289 of 334 VOOs; for the remaining 45, misalignments between panels of first (on the category, 21 cases) or second type (on the main perceived defect, 24 cases) occurred. In these cases, a “formative reassessment” was necessary. At the end, 329 of 334 VOOs (98.5%) were classified, thus confirming the effectiveness of this approach to achieve a better proficiency. Panels showed good performance, but the need to adopt new reference materials that are stable and reproducible to improve the panel’s skills and agreement also emerged.
Sleep Quality in Psoriasis Patients and its Relations with Possible Affecting Factors

Objectives: Psoriasis (PS) is a chronic, immune-mediated inflammatory skin disease. PS may lead to significant effects on health-related quality of life (HRQoL) and other outcomes. In the present study, an investigation into sleep quality (SQ), and its possible relations with factors which may affect SQ were aimed. Methods: A total of 74 subjects from both sexes were enrolled in this study, between January and July 2017. Patients were evaluated with their demographics, body mass index (BMI), Psoriasis Area Severity Index (PASI), Pittsburgh Sleep Quality Index (PSQI), Psoriasis Quality of Life Index (PQLI), Self Perception Scale (SPS). Additionally, accompanying chronic diseases, disease duration and severity of pruritus were recorded. Obtained PSQI values were compared with the mentioned parameters concerning the significance of their relations with it. SPSS version 24, 2016 was used to analyse the data, and significance was evaluated with p-values of <0.05, 0.01, and 0.001, and rho (r) values of <0.2, =0.2-0.4, =0.4-0.6, =0.6-0.8 and >0.8. Results: Thirty-seven female and 37 male were studied. The mean age of total of the study population was 47.21±13.91. Mean BMI and mean duration were 30.09± 4.68 kg/m2, and 10.58±9.1 months. Mean values of PASI, PSQI, SPS, and PQLI of the study group were 19.79±16.99, 9.14±5.09, 142.12±23.83, and 21.94±16.31, respectively. Approximately thirty-one percent of them had at least one chronic disease. Alcohol and smoking rates were 17.56%, 50%. PASI was positive/strongly correlated with PQLI and negative/weakly correlated with SPS. No correlation was detected between PSQI values and age, gender, BMI, and SPS values. PSQI was moderately correlated with PQLI, diabetes mellitus (DM), and pruritus severity, whereas it was weak correlated with PASI, hypertension (HT), thyroid diseases and disease duration. PASI and DM showed a predictive effect on SQ. Conclusion: SQ is affected by certain factors, such as QoL, disease severity, disease duration, pruritus severity, accompanying disorders, such as HT, DM and thyroid diseases, in which disease severity and DM have predictive effects on SQ in PS patients. Controls of disease activation and prevention of progression in DM may provide to keep SQ in PS.
Improvement of Faster-RCNN Detection Algorithms for Small Size Line Accessory Equipment

Aiming at the problem that the image of patrol inspection is affected by angle and distance, the target of small size accessory equipment in the image is small, and the recognition rate of current algorithm is low. An improved Faster-RCNN detection algorithm for small size accessory equipment of patrol image is proposed. By adjusting the structure of Faster-RCNN network, we can overcome the shortcomings of the current algorithm. Firstly, the deep residual network ResNet50 is used to replace VGG16 network. At the same time, the bottom and high-level features of convolution network are applied to the selection of candidate regions, so as to improve the utilization of effective information of targets, and then to improve the detection precision of small-sized targets. The results show that the detection accuracy of the improved Faster-RCNN model is 4.2% higher than that of the original algorithm.