Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Care for the Frail in Nova Scotia: An Implementation Evaluation of a Frailty Portal in Primary Care Practice

Background: Understanding and addressing the needs of frail patients has been identified as an important strategy by the Nova Scotia Health Authority (NSHA). Primary care (PC) providers are in a key position to aid in the identification of, and response to frailty as part of routine care. Unlike singular chronic conditions such as diabetes and hypertension which garner a disease-based approach and identification as part of standard practice, frailty is only just emerging as a concept for PC. The web-based Frailty Portal was developed to aid in the identification of, assessment and care planning for frail patients in PC practice. In this study we assess the implementation feasibility and impact of the Frailty Portal by: (1) identifying factors influencing the Frailty Portal’s use in community PC practice, and (2) examination of the immediate impact of the ‘Frailty Portal’ on frail patients, their caregivers and PC providers. Methods: A convergent mixed method approach was implemented among PC providers in community-based practice in the NSHA, Central Zone. Quantitative and qualitative data were collected concurrently over a 9-month period. A sample of patients who underwent assessment and/or their caregiver were approached for survey participation. Results: Fourteen community PC providers (10 family physicians, 4 nurse practitioners) completed 48 patient assessments and completed or begun 41 care plans; semi-structured interviews were conducted among 9 providers. Nine patients and 5 caregivers participated in the survey. PC providers viewed frailty as an important concept but implementation challenges were met, primarily with respect to the time required for use and lack of fit with traditional practice routines. Additional barriers included tool usability and accessibility, training and care planning steps, and privacy. Impacts of the tools use with respect to confidence and knowledge showed early promise. Conclusion: This feasibility study highlights the need for added health system supports, resources and financial incentives for successful implementation of the Frailty Portal in community PC practice. We suggest future implementation integrate the Frailty Portal to practice electronic medical records (EMRs) and target providers with largely geriatric practice populations and those practicing within interdisciplinary, collaborative primary healthcare (PHC) teams.
Promoting mobility in patients with dementia: some suggested strategies for physiotherapists

Mobility problems are not uncommon among dementia patients. This paper aims to encourage physiotherapists to accept the challenge posed by these patients; to seek solution to their mobility problems and to support and advise their carers.A brief outline of the dementing process is introduced, followed by an account of the problemi associated with the dementia, which may affect the outcome of remobilisation efforts. Particular points for consideration during the assessment are mentioned and some examples are given of the physiotherapy intervention which might follow the identification of certain problems.Improvements in the patients' functional mobility can be expected, if the problems associated with the dementia can be successfully managed. Strategies for this purpose are therefore suggested in Tome detail and examples are given to illustrate their use. Particular emphasis is placed on the need to communicate effectively and to exploit the use of sub-cortical responses and cues.
Ground Vehicle Classification Using Hidden Markov Models

Abstract : Ground vehicle classification is performed using hidden Markov modelling of cepstral coefficients. The hidden Markov model (HMM) is used to represent audio signals. These signals are obtained as the vehicles travel past audio sensor arrays. Well known HMM training algorithms are applied to train models from training data. The trained models are used in two classification rules: the MAP rule, and a list-based rule due to Forney. Under some general assumptions, these approaches can be regarded as optimal. Using recordings from the ACID database, over 96% recognition rate on single vehicle classification is achieved.
Image Augmentation for Object Image Classification Based On Combination of Pre-Trained CNN and SVM

Neural networks are a powerful means of classifying object images. The proposed image category classification method for object images combines convolutional neural networks (CNNs) and support vector machines (SVMs). A pre-trained CNN, called Alex-Net, is used as a pattern-feature extractor. Alex-Net is pre-trained for the large-scale object-image dataset ImageNet. Instead of training, Alex-Net, pre-trained for ImageNet is used. An SVM is used as trainable classifier. The feature vectors are passed to the SVM from Alex-Net. The STL-10 dataset are used as object images. The number of classes is ten. Training and test samples are clearly split. STL-10 object images are trained by the SVM with data augmentation. We use the pattern transformation method with the cosine function. We also apply some augmentation method such as rotation, skewing and elastic distortion. By using the cosine function, the original patterns were left-justified, right-justified, top-justified, or bottom-justified. Patterns were also center-justified and enlarged. Test error rate is decreased by 0.435 percentage points from 16.055% by augmentation with cosine transformation. Error rates are increased by other augmentation method such as rotation, skewing and elastic distortion, compared without augmentation. Number of augmented data is 30 times that of the original STL-10 5K training samples. Experimental test error rate for the test 8k STL-10 object images was 15.620%, which shows that image augmentation is effective for image category classification.
CTI4AI: Threat Intelligence Generation and Sharing after Red Teaming AI Models

As the practicality of Artificial Intelligence (AI) and Machine Learning (ML) based techniques grow, there is an ever increasing threat of adversarial attacks. There is a need to red team this ecosystem to identify system vulnerabilities, potential threats, characterize properties that will enhance system robustness, and encourage the creation of effective defenses. A secondary need is to share this AI security threat intelligence between different stakeholders like, model developers, users, and AI/ML security professionals. In this paper, we create and describe a prototype system CTI4AI, to overcome the need to methodically identify and share AI/ML specific vulnerabilities and threat intelligence.
Iron-Deficiency Anemia Detection From Hematology Parameters By Using Decision Trees

In this study a decision support system has been designed from the biochemistry blood parameters which will be very helpful for and will make everything easier for the physicians in the diagnosis of Iron- deficiency anemia. Based on Pattern Recognition Process, the system operation is achieved via the decision trees structure which is related as one of the data mining techniques. The basic characteristic of the hematology parameters that is, Serum iron, Serum iron-binding capacity and Ferritin parameters are used in the process of entering the system and finally Anemia (+) and Anemia (-) results have been evaluated at the end of this process. Data of 96 patients are evaluated in the projected system. The results of the decision support system have completely matched with those of the physicians' decisions.
TOWARDS SOCIAL GAMIFICATION - IMPLEMENTING A SOCIAL GRAPH IN AN XMOOC PLATFORM

High dropout rates and poor user participation are some of the major challenges for xMOOC providers. Similar to others, openHPI also faces these problems. Gamification is a mechanism that recently has been successfully employed to raise user motivation in web applications. A concept to gamify the openHPI platform has been proposed and implemented. Parts of it are already published on the platform. It is proposed that further gamification elements could be improved by applying the factor relatedness . Introducing a social graph to the platform, allowing users to establish connections to other users, is seen as a means to increase this relatedness. For example, the gamification element of a leaderboard gains a lot of relevance when a user finds herself amongst a list of her friends than a list of random strangers. The paper at hand introduces the implementation of a social graph in the context of openHPI’s MOOC platform.
Un aperçu féminin sur la restructuration de l’emploi et sur la gentrification : le cas de Montréal

Dans le debat sur les causes de la gentrification, la geographie urbaine d'inspiration marxiste a recemment commence a analyser les liens entres les transformations economiques globales et la production d'une « nouvelle classe moyenne » dans les quartiers centraux des grandes metropoles. Cependant, cette approche continue de releguer au second plan la composante sexuelle de la restructuration de la force de travail. Nous tentons de montrer que les rapports de sexe sont un facteur constitutif des nouvelles fractions et des « nouveaux modes d'habiter » observes dans les quartiers en voie de gentrification. Pour mieux saisir ces transformations, il importe d'analyser de maniere plus approfondie les pratiques de reproduction des menages. Nous illustrons nos constatations a l'aide de donnees du recensement sur l'insertion des professionnels et des professionnelles des quartiers centraux dans les secteurs de l'economie montrealaise en 1971 et en 1981, et sur la composition des menages dans lesquels ils et elles vivent. Il appert que les gentrifications se scindent en fractions distinctes au lieu de constituer un groupe dont le comportement et le mode d'appropriation de l'espace sont homogenes.
Musica Universalis or the Music of the Spheres

The Music of the Spheres was a model of the universe proposed by Pythagoras and Aristotle, which explained cosmology in terms of spheres to which the sun, the moon and the planets were pinned, while their motion was driven by something akin to music. Modern thinking, related to ecology and industrial ecology, has metaphorically breathed life back into this old model by speaking about spheres again: biosphere, geosphere, anthroposphere, technosphere, hydrosphere, cryosphere, atmosphere, etc. Sustainable development also speaks about its three pillars (economy, environment, society) represented in a Venn diagram as intersecting circles (or spheres). All these models differ from the models of physicists, as they are more conceptual diagrams than a representation of the world as it is. Thus, they remind us of the old Music of the Spheres model. They also stress connections, exchanges, equilibria between the spheres – or the lack of them –, like Pythagoras’ music. The presentation will discuss these various approaches, see how they match to some extent, but also how they do not show a perfect fit. Analyzing what happens at the boundaries of the spheres, where they overlap or penetrate into each other, is a powerful way to analyze the connection between technology, society, life and ecosystems. It can also help discuss pollution, ecotoxicology and explore global solutions.
Applying Regression Analysis to Predict and Classify Construction Cycle Time

Regression techniques are commonly used for addressing complicated prediction and classification problems in civil engineering thanks to its simplicity. For a given dataset, the linear regression from the input space to the output variables can be achieved by using the “least square error” approach, which minimizes the difference between the predicted and actual outputs. The “least mean square” rule can also be used as a generic approach to deriving solutions on linear or non-linear regressions. The paper addresses the fundamental algorithms of “least square error” and “least mean square” in order to facilitate the prediction and classification of cycle times of construction operations. The classic XOR problem is selected to verify and validate their performances. A viaduct bridge was installed by launching precast girders with a mobile gantry sitting on two piers. The effectiveness of regression techniques in classifying and forecasting the cycle time of installing one span of viaduct considering the most relevant input factors in connection with operations, logistics and resources are demonstrated.
An Approach for Rapid Establishment of Patient Effective Dose During Radiology and Results for Two Large University Hospitals

Patient dosimetry and assessment of effective dose was implemented under the condition that measurements involve commonly available instrumentation. Four categories of examination were distinguished: radiographs with manual techniques, radiographs with automatic exposure control (AEC), examinations involving fluoroscopy, and computed tomography (CT). For projection radiography the dose-area product (DAP) and for CT the computed tomography dose index were measured. Databases of conversion factors were consulted for the calculation of effective dose. Effective doses varied from less than 0.001 to over 100 mSv. Only 5-7% of the total examinations consist of CT scans, the contribution of CT to the collective effective dose for the patient population, however, was 40-50%. Whereas simple radiographs constitute almost 84-89% of the number of examinations the contribution to the collective dose was only 2-11%.
Successful Accessing Rate Based Routing Model in Peer-to-Peer Control Network

Peer-to-peer control network relies on location mechanism that conducive to resources sharing among robotics. The commonly using overlay network topology method based on interesting group in the existing peer-to-peer control network should be accomplished collaboratively, which increases system traffic and occupies large bandwidth. The paper brought out a routing model in peer-to-peer control network based on Successful Accessing Rate (SAR). The model organizes user group based on SAR and conducting resource searching in high-low order. In this model, each node runs independently, which decrease system maintenance cost and provides new idea to solve free-rider problem. Simulation results show that the model effectively improve system recall rate and solve fairness problem.
Concept development in relation to the biology of reproduction in secondary science : a Vygotskyan perspective.

The study of concept development in biology comes from a professional concern that arose in my classroom teaching - an inability, after many years practice of the techniques of ‘discovery science’, to accept the theoretical base of the individual secondary school pupil constructing science concepts mainly through experiment. From this concern arose my research question - the extent to which a developmental space could be defined in which the development of science concepts could take place. The instance for examining such a space would be science concepts of reproduction. To develop a model for such a space I looked at the work of Jean Piaget, Lev Vygotsky and a number of other theorists concerned with the issue of innate learning mechanisms and of constructivism and social constructivism. I adopted Vygotsky’s dialectical materialist approach in assessing the concepts involved. To test the emerging model I decided on a case study approach, defined in terms of both the case as instance and the case as issue - instance in that the focus would be a year 8 (Britain year 7) science class, with interviews across the year group The study would involve the case as issue in that I intended to walk through the process of concept formation in plant and human reproduction with the pupils. I applied a mixture of semi-structured interview and classroom observation, linked through the development of ‘structured situations’ in which models and materials were made available in specific social settings in the expectation that the pupils would construct meaning. The outcome of the investigation did not support the initial model, but it did suggest in outline a new model of a developmental space. This suggested that the different goals of teacher and pupils expressed themselves as implicit and explicit elements of the situation, often resolved by the pupils completing tasks without thought. This indicated that a common outcome of schooling was ‘encapsulation’, formal mental constructs lacking meaning. Elements of a new model would be the role of emotion and of the goal of activity.
Australian guidelines for the treatment of acute stress disorder and posttraumatic stress disorder

Military personnel are confronted with a range of experiences that may contribute to posttraumatic stress disorder (PTSD), including both military-specific events and traumas that also affect the general population. Research with the Australian Defence Force (ADF) suggests that the most common traumatic event experienced by serving members is seeing someone badly injured or killed, or unexpectedly seeing a dead body. In terms of developing PTSD, experiences such as witnessing atrocities and accidentally injuring or killing another person, in addition to other interpersonal traumas such as rape, domestic violence, being stalked, and being kidnapped or held captive, pose the most significant risk. [1]
A Review Paper on Botnet and Botnet Detection Techniques in Cloud Computing

Recently, botnets are the most radical of all cyberattacks and becoming the key issue in cloud computing. Botnets are the network of different compromised computers and/or smartphones. These devices are infected with malicious code by botmaster and controlled as groups. The attackers use these botnets for criminal activities such as DDoS, click fraud, phishing, spamming, sniffing traffic and spreading new malware. The main issue is how to detect these botnets? It becomes more interesting for the researchers related to cyber-security? This motivates us to write a review on botnets, its architecture and detection techniques.
Multiple Aspect Ranking using Sentiment Classification

--Generally a product can have a many aspects. Some aspects will be more important than other aspects. Identifying the important product aspect will improve usability of numerous review and also beneficial to consumer and firms. Consumer can make wise purchasing decision by making more attentions to product aspect. The shallow dependency parser was use to identify product aspect ranking . In this paper for identify aspects use sentiment classification method. The document-level sentiment classification and extractive review summarization were use for product aspect ranking. The ranking are done based on frequent commented review and consumer overall opinion about the product. A probabilistic aspect ranking algorithm is use for calculating overall opinion about the product from multiple site. In this paper 4 product are taken and there domain are classified based on the different reviews. Here there is a possible of identifying false detection graph which can be identified when equal number of pros and cons are present for one aspect then that aspect is identified so we can select a product using other than that feature.
On the growth of larval plaice in the North Sea

Dit proefschrift gaat over de groei van larvale schol op open zee, en over de mogelijke invloed van de temperatuur daar op. Een belangrijke rol werd daarbij gespeeld door de otolieten(gehoorsteentjes), waarin dagelijks gevormde, microskopisch kleine groeiafzettingen te zien zijn. Het aantal afzettingen levert een indicatie op van de leeftijd, en de breedte ervan houdt verband met de groei van de larven. Voor deze studie werden deze groeiringen geanalyseerd met behulp van door een lichtmikroskoopgemaakte fotos. Zie: Samenvatting en conclusies
Protective effect of regular aerobic training on inflammatory and toxicity markers of lung tissue in L-NAME-induced hypertension

Hypertension is a multi-factorial, life-threatening disease. The present study was designed to investigate protective effect of aerobic regular training on inflammatory and toxicity markers in lung tissue of male wistar rats exposed to chronic nitro-L-arginine-methyl ester(L-NAME)-induced hypertension. Thirty two adult male Wistar rats are randomly classified into 4 groups; aerobic training, L-NAME, saline and control groups. Hypertension was induced by administration of L-NAME (10 mg/kg) for 8 weeks and 6 sessions a week. Aerobic training was performed between 25 to 64 minutes and the speed 15 to 22 m/min for 8 weeks and 5 sessions a week. Chronically administration of L-NAME cause a significant increase in angiotensin converting enzyme (ACE), interleukin 6 (IL-6) levels, and a significant decrease in superoxide dismutase (SOD) and nitric oxide (NO) levels, as compared to saline and control groups. In contrast, aerobic training for 8 weeks caused a significant increase in SOD, NO and a significant decrease in IL-6 and protein carbonyl (PC), as compared to LNAME and saline groups. Our study suggests aerobic regular exercise provided significant protection against L-NAME-induced toxicity in lung tissue by up-regulation of antioxidant systems and down-regulation of the inflammatory and vasoconstrictor factors in hypertensive rats. These results suggest that aerobic regular exercise may be considered as a potentially useful strategy to limit toxicity in lung tissue.
Is there a clinical benefit of anti-PD-1 in patients older than 75 years with previously treated solid tumour?

3070Background: The immune system undergoes profound transformations with age, and response patterns to immune challenges are therefore highly age dependent. Nivolumab, an anti-PD-1 immune checkpoint inhibitor, showed promising activity and tolerability in several solid tumours. However, in our knowledge, few data are available in patients older than 75 years. Methods: We performed an age subgroup analysis of published randomized control trials concerning nivolumab versus standard therapy in previously treated patients with advanced solid tumours. Overall Survival (OS) among the elderly ( ≥ 75 years) was compared with that of younger patients ( ≥ 65 to < 75 years). Hazard ratios (HRs) with their 95 % confidence interval (CI) were collected from the studies and pooled. A fixed-effect model was used. Results: Few studies have been published corresponding to our inclusion criteria. Two studies (CheckMate 017 for Squamous and CheckMate 057 for Non-Squamous) assessed nivolumab versus docetaxel in Non-Small Cel.
Analytic‐based product opinion detection algorithm for twitter microblogging network

Twitter, with an ever‐increasing user base, has greatly influenced the opinion and purchase habits of the common masses. This has in turn forced the product firms to get involved with sentiment analysis which enables them to mine the actual opinion about their product and make business decisions accordingly. Even though a majority of the existing methods detect sentiment of the tweet with a reasonable accuracy, few ignore emoticons while others consider them as stop words. Emoticons have enabled the users to express their emotion more accurately which eliminates the ambiguity that can arise with usage of words. The trending popularity of emoticons among the users combined with its ease of usage makes it highly lucrative in sentiment analysis. Hence, mining the product opinion without considering the emoticons will severely undermine the accuracy and reliability of the opinion. Moreover, sarcasm detection is still an uncharted territory in opinion mining and is exceedingly difficult to factor it in. Sarcastic tweets when left undetected will affect the accuracy of the opinion. Therefore, the polarity of the individual words and emoticons of the tweets are computed using linguistic analysis. The sarcastic tweets are then classified and eliminated based on their anomalous polarity. By placing a higher emphasis on emoticons, the proposed emoticon‐based linguistic opinion algorithm yields satisfactory results when compared with other traditional and state of the art approaches.