Reviewerly/exHarmony huggingface dataset:
Segmentation of CA3 Hippocampal Region of Rat Brain Cells Images Based on Bio-inspired Clustering Technique

In the area of clustering, the most common issue of obtaining the optimum number value for the clusters is still an open challenge for different application areas. It is very hard to get the optimal number of clusters because of the lack of prior knowledge. This happens due to having various dimensions of data, clusters having a wide range of shape, size & density, and overlapping exists among groups. Many approaches have been proposed by various researchers which include bio-inspired techniques like genetic algorithm, particle swarm optimization, invasive weed optimization, cat swarm optimization, ant colony optimization, etc., for addressing these issues. Also, various combinations of the hybridization of these techniques have been practices by the researchers. The superiority of evolutionary techniques over the hard clustering techniques such as k-means clustering becomes popular in clustering area. Inspired by this, a novel rat brain cell segmentation approach is proposed using the latest bio-inspired clustering technique known as “Teacher-Learner Based Optimization”. In contrast to most of the well-known clustering techniques, TLBO doesn't require any parameter tuning and is less complex. The proposed approach is validated using NISSL stained rat brain cell dataset. In experimental evaluation performance, comparisons are made, based on quantitative results as well as qualitative results. The overall result analysis shows that the proposed approach is much more capable in segmenting the cells in comparison to the other well-known clustering techniques.
Unsupervised Community Detection with a Potts Model Hamiltonian, an Efficient Algorithmic Solution, and Applications in Digital Pathology

Unsupervised segmentation of large images using a Potts model Hamiltonian is unique in that segmentation is governed by a resolution parameter which scales the sensitivity to small clusters. Here, the input image is first modeled as a graph, which is then segmented by minimizing a Hamiltonian cost function defined on the graph and the respective segments. However, there exists no closed form solution of this optimization, and using previous iterative algorithmic solution techniques, the problem scales quadratically in the Input Length. Therefore, while Potts model segmentation gives accurate segmentation, it is grossly underutilized as an unsupervised learning technique. We propose a fast statistical down-sampling of input image pixels based on the respective color features, and a new iterative method to minimize the Potts model energy considering pixel to segment relationship. This method is generalizable and can be extended for image pixel texture features as well as spatial features. We demonstrate that this new method is highly efficient, and outperforms existing methods for Potts model based image segmentation. We demonstrate the application of our method in medical microscopy image segmentation; particularly, in segmenting renal glomerular micro-environment in renal pathology. Our method is not limited to image segmentation, and can be extended to any image/data segmentation/clustering task for arbitrary datasets with discrete features.
Secure Storage and Accessing of Organ Donor Details

The area cloud computing itself is a very vast domain with increasing new technologies each day. Here, an online organ donation system is developed with the help of the aforementioned technologies. The main aim of the project is to make sure that the organs of the people, who have come forward to donate, reach the respective individual in need of the particular organ. Since everything is stored in the cloud, anybody can access it anytime, which in turn puts the data at risk. The secured sharing of donor details is necessary. Encryption is done at two levels to provide security, one while the data are entered and the other done by a third party providing proxy re-encryption. Now, when a patient in the hospital has been declared dead after thorough verification for any complications, based on the deciding parameters such as the organs that can be transplanted, blood group, HIV status, and location, a comparison is done with the cloud database using query filtering, which results in the nearest recipient available meeting the requirements. The performance enhancement of cloud data using encryption schemes is of high potential.
A Comparative Study on GA-based Scheduling on Cloud Computing

Cloud computing provides data storage and computing power based on user demand by assigning tasks to virtual resources. To deliver overall improved performance and meet challenges such as availability, resource utilization and reliability in the cloud, appropriate resource scheduling methods are needed. A number of metaheuristic optimization algorithms are used to solve the problem of resource scheduling. This work lists challenges and analyzes previous scheduling methods based on Genetic Algorithm (GA). It classifies the GA-based scheduling methods with respect to many parameters. At last, it presents the scopes of enhancement for future researchers.
The Transformation of Ideas about Ethical-Humanistic Dimensions of Science in the Context of Eastern and Western Philosophy

The article is devoted to the analysis of ethical parameters of science in the context of the philosophy of East and West. It is shown the evolution of ideas about the ethical norms of science in the philosophies of Central Asia scientists such as al-Farabi, Abu Rayhan Beruni and Ibn Sina. The problem of morality and cognition is perennial. History of science development (the development of the atomic and hydrogen bombs, modern genetic engineering, the environmental situation, cloning and ICT, etc.) confirms its relevance at all stages of human civilization development. Ethical spheres of science were developed in the proceedings of philosophers of the West: Gassendi, Boyle, Descartes and Merton. A new type of rationality, which is now approved by the post-non-classical science, resonates with ancient Eastern concepts about the relationship of truth and morality. In the strategies with complex man-sized systems as benchmarks are not only knowledge but also moral principles, imposing bans on dangerous for human beings ways of experimenting with the system and its transformation.
Imagining Future Technology of the Horse Industry and Equestrian Sports

Equestrian sports are amongst the largest sports and leisure activities in Sweden. One of the major reasons for engaging with horses is the bond to the horse. Just recently technology has found its ...
LibGuides: ASU Library Digital Credentials: Home

ASU Library is launching a new digital credentials service that will enable faculty to integrate credentials into Canvas.
An Innovative Smartphone-based Solution for Traffic Rule Violation Detection

This paper introduces a novel smartphone-based solution to detect different traffic rule violations using a variety of computer vision and networking technologies. We propose the use of smartphones as participatory sensors via their cameras to detect the moving and stationary objects (e.g., cars and lane markers) and understand the resulting driving and traffic violation of each object. We propose novel framework which uses a fast in-mobile traffic violation detector for rapid detection of traffic rule violation. After that, the smartphone transmits the data to the cloud where more powerful computer vision and machine learning operations are used to detect the traffic violation with a higher accuracy. We show that the proposed framework detection is very accurate by combining a) a Haar-like feature cascade detector at the in-mobile level, and b) a deep learning-based classifier, and support-vector machine-based classifiers in the cloud. The accuracy of the deep convolutional network is about 92% for true positive and 95% for true negative. The proposed framework demonstrates a potential for mobile-based traffic violation detection by especially by combining the information of accurate relative position and relative speed. Finally, we propose a real-time scheduling scheme in order to optimize the use of battery and real-time bandwidth of the users given partially known navigation information among the different users in the network, which us the real case. We show that the navigation information is very important in order to better utilize the battery and bandwidth for each user for a small number of users compared to the navigation trajectory length. That is, the utilization of the resources is directly related to the number of available participants, and the accuracy of navigation information.
Experimental Data Acquisition and Management Software for Camera Trap Data Studies

At present, use of camera traps is widespread, and their importance in wildlife studies is well understood. Camera trap studies produce vast amount of images and there is a need for software, which will able to manage this data and make the automatic annotations to help studies. The chapter is devoted to the description of the experimental data management system used to process the images obtained from the camera traps. The chapter considers a description of the modules of such system, as well as, methods used during software implementation. The proposed software system has the ability to automatically extract metadata from images and associate customized metadata to the images in a database. Additional metadata are formed by set of algorithms, which in automated mode allows us to detect empty images, conduct animal detection with species classification, and make a simple semantic description of observed scene.
New Methods of Forming and Measurement of Sub-pixel Shift of Digital Images

Methods of forming digital images with sub-pixel shift and measuring this shift are developed. Modification of discrete Chebyshev transformation is suggested to create the images with given sub-pixel shift. Optimal net of Chebyshev samples (secondary readings) in digital images is calculated. Samples are calculated in zeros of Chebyshev polynomials. Indicators of non-integer shift in a form of discriminators are suggested. Discriminators algorithms approximate Newton–Raphson method of estimating maximum likelihood. Estimation distribution for some types of discriminators in the presence of noise is obtained. We found that the estimation distribution is non-Gaussian, with “heavy tails”. Limiters producing the stable estimations in order to subdue big output signal values are presented. Theoretical distributions of stable estimations are obtained. By means of statistical modeling, a coincidence between experimental and theoretical characteristics is established. The suggested algorithms are easy to calculate.
Are limited editions limited to products

Purpose: The aim of this paper is to explore whether service can be implemented as limited edition in order to strengthen the brand and to drive business. Methodology/Approach: This research is based on a qualitative approach with a multi case study analyzing Visit Sweden, Finnair and Lund University. Each brand has implemented a limited service of own kind this year. Findings: The research showcases the potential of service as limited edition, and it proves that limited services can valuably shape a brand and its image. Limited services create an urgent purchase incentive that rests upon the scarcity mechanism. Research limitations/implications: This paper introduces services as limited edition with a focus on raising awareness for the topic. As such, further research must be conducted with more cases in other industries, comparative studies towards limited products, and in-depth analyses that evaluate the phenomenon step by step. Managerial Implications: Managers may find this paper useful in order to conduct persuasive marketing with limited services as alternative strategy and unique business measures compared to other sales promotion techniques. Originality/Value: This paper addresses the existent gap between limited editions and service that can be noticed in both business and research areas.
OPTIMALISASI PEMELIHARAAN DRAINASE BERDASARKAN PERSEPSI MASYARAKAT

Drainage is one element of the general infrastructure needed by the community in order to lead a clean, comfortable, and healthy area of life. In this drainage, one of the things related to the problem of drainage, among others, is the occurrence of physical damage to the drainage, namely the damage that occurs in the drainage build-ing. One of the areas experiencing drainage problems is BlangBeurandang Village, BB1 Housing, Johan Pahla-wan District, West Aceh Regency. In this village, if there is rainfall with high intensity floods often occur. This is caused by the poor performance of the existing drainage such as a lot of sedimentation, siltation in channels that are still earth channels, as well as a lot of garbage and grass. Because of these problems, the role of the communi-ty in the maintenance of drainage is very important, because with the community participation in drainage mainte-nance this will certainly be able to minimize the occurrence of various problems related to drainage. The research method used is the Likert scale method. The result of this research is the existing condition of BB1 Housing drain-age, that is, if there is rainfall with high intensity, frequent flooding occurs. This is due to the poor performance of the existing drainage, such as a lot of sedimentation, siltation of channels that are still earth channels, as well as a lot of garbage and grass. Optimization of drainage maintenance based on community perceptions is classified as good criteria, this is shown from the calculated questionnaire percentage value of 72.09%. Based on the results of the calculation of the percentage, the community is very supportive for the maintenance of drainage in BB Housing.
Data reduction based on information gain approach

Many processes of Machine Learning need to the real values and appropriate size of data, that we get through search and Data Mining, and extract the necessary knowledge to work with them. The Preprocessing techniques are basic processes that we do when using any set of data in the different areas of Data Classification or Pattern Recognition,Where the applied these processes in any machine learning field will be difficult without the basic procedure for the preprocessing of the data and reduce its size and get rid of any confusion or noise. So give us effort and time, and produce more results that are accurate. In this paper, we offer study and apply method for used to reduce the data and extract the appropriate features to work on, finally reached a satisfactory outcomes.
LECTURERS’ TEACHING STRATEGIES TO PROMOTE PRODUCTIVE CLASSROOM INTERACTIONS IN SPEAKING SUBJECT OF THE FIRST YEAR STUDENTS AT ENGLISH DEPARTMEN, UNIVERSITAS JAMBI.

ABSTRACTPratiwi, Indah (2019). Lecturers’ Teaching Strategies to Promote Productive Classroom Interactions in Speaking Subject of the First Year Students at English Department, Universitas Jambi. Thesis, Faculty of Teacher Training and Education, Jambi University in Academic Year, 2018/2019. First supervisor, Amirul Mukminin, Ph. D., and the second supervisor Masbirorotni, S.Pd. Msc,Ed.Key words: Speaking, Productivity, Speaking Strategy.This study was aimed to analyze the lecturer' teaching strategies to promote productive classroom interaction in speaking subject at one of public university in Jambi, particularly the types and frequency of speaking strategies that the lecturer' used to teach. The participants of this study were students from one English study program at Faculty of Teacher Training and Education at one public university in Jambi, taught by Mr. RD. M. Ali, S.Pd., M.Pd. The study involved 30 participants in speaking class. This study used observation as well as questionnaire consisting of 33 sentences based on 11 items. The results of this study indicated that the most common strategies applied by the lecturer succeed to trigger students to be more active at speaking in the classroom.
A phenomenological approach to investigate the pre-reflexive contents of consciousness during sound production

This article describes a listening experiment based on elici-tation interviews that aims at describing the conscious experience of a subject submitted to a perceptual stimulation. As opposed to traditional listening experiments in which subjects are generally influenced by closed or suggestive questions and limited to predefined, forced choices, elicita-tion interviews make it possible to get deeper insight into the listener's perception, in particular to the pre-reflexive content of the conscious experiences. Inspired by previous elicitation interviews during which subjects passively listened to sounds, this experience is based on an active task during which the subjects were asked to reproduce a sound with a stylus on a graphic tablet that controlled a synthesis model. The reproduction was followed by an elicitation interview. The trace of the graphic gesture as well as the answers recorded during the interview were then analyzed. Results revealed that the subjects varied their focus towards both the evoked sound source, and intrinsic sound properties and also described their sensations induced by the experience.
Image’s Dynamic Range and Color Gamut Conversion Based on Cycle GAN

Image dynamic range conversion is a hot topic in the field of image processing. This function mainly realizes the transformation of image from low dynamic range to high dynamic range, which makes the details of image brighter and darker more significant and improves the image's quality. Based on the idea of generative adversarial network, an image transform network is constructed by using cycleGAN. The network can not only transform the dynamic range of image, but also make the image change from one gamut standard to another. In this training, a new dataset is adopted, which includes six kinds of image data based on ITU-R BT.709 gamut and ITU-R BT.2020 gamut. The loss functions of gradient, color and PSNR value are added to the network, and the effectiveness of the algorithm is verified by image evaluation indexes such as HSV spatial color difference and SSIM value.
Segmentation and Classification of Microcalcifications Using Digital Mammograms

For more than a decade, in the context of Microcalcifications detection, radiologists previously use traditional techniques to analyze mammographic images, which leads to a lower precision in the detection of lesions. The effective method of treating breast cancer is to detect it in early stages to increase the chances of cure and reduce the mortality rate, and to do this we propose in this paper to develop a computer aided diagnostic system (CAD) named Earlier Breast Cancer Computer Aided Detection (EBCCAD) which aim is to detect and classify breast cancer images and to replace the previous techniques already used to enhance radiologists performance in determining the pathologic-disease stage of Mccs and to discriminate between normal and abnormal tissues. The results obtained are promising given the rate of good classification obtained by the approaches proposed in the classification phase which lead us to evaluate the proposed system for real cases.
Mining of Removable Closed Patterns in Goods Dataset

Production factories have many items produced with various parts to bring out the complete product at the end. Every product that is produced in the factory obtains the company some amount of income. The manufacturing of these products also costs a huge sum for purchase and production. When a situation arises for the company to shut down its manufacturing or when there is some crisis, the industry will not have money to buy the parts to finish the product. The problem of removable pattern mining is to identify the patterns which can be removed to reduce the loss to the factory’s profit under certain conditions. In such situations, the production managers need an alternate to reduce the cost of production without decreasing the profit rate. This paper proposes removable closed patterns to represent and compress the mined removable patterns without much profit reduction. Erasable closed pattern mining algorithm is implemented to mine all the erasable closed patterns by reducing the resources needed for production without affecting the information loss. The removable itemset with one pattern along with their element occurrence pair is taken into consideration. Further the erasable patterns are mined and used for useful extraction of members. Then the erasable closed patterns are mined using the module that checks the closeness of the patterns in the product datasets.
Pengajaran Bahasa Inggris Menggunakan Media Audio Visual di Raudhatul Atfal (RA) Dharma Wanita Persatuan Simpang

This Community Service aims to provide assistance in learning English about pronunciation of vocabulary using Audio Visual and providing knowledge about the use of Audio Visual media for teachers and learning for students. The chosen partners in this service are children an teachers of Raudhatul Atfal (RA) Dharma Wanita eersatuan Simpang, Buay Pemaca, OKU Selatan, with the consideration that they rarely learn English because there is no English teacher in the RA in using learning methods. In addition, based on observations in the RA, it shows that children's lack of interest in learning English and even English for them is too ordinary / foreign. The target in this service is to help the children of RA Dharma Wanita Persatuan Simpang to learn English so that they do not feel unfamiliar / unfamiliar and increase student motivation in learning English and minimize errors in speaking vocabulary in English. From the results of this service activity, it can be concluded that learning English by utilizing audio-visual media in students of RA Dharma Wanita Persatuan Simpang is proven to contribute significantly to the mastery of the English vocabulary of students.
LibGuides: Language and Literacy Education: Videos

This guide is for the subject Language and Literacy Education. It contains resources available in the NCU Library.
CUTTING EDGE FUTURISTIC ENTERPRISE TECHNOLOGY FRAMEWORK FOR GLOBAL CASH LOGISTICS INDUSTRY USING IOT

AutomatedTeller Machine (ATM) is one broadly preferred choice for money withdrawal for consumers. Getting an ATM with required amount and section has constantly demonstrated to be an obstacle for consumers looking for money. One approach to counter this is to introduce progressively number of ATMs in region to guarantee the client's necessities are met. Anyway this demonstrates to be an overhead for bank utility as far as support and activity cost.Here in this paper we are involving IoT which can get noteworthy cost reserve funds in the board and tasks of ATMs. It is regularly observed ... By interfacing the sensors to a focal cloud-based revealing and the executives dashboard, banks can take preventive activities in time, inevitably sparing a great deal on vitality and upkeep costs. Modern money arranging, keen checking of ATM occurrences, and defense of the ATM impression, banks can cut their absolute expense of overseeing money by as much as 10 to 15 percent. The subsequent wave is re-appropriating. The objective is to accomplish critical cost decrease and improve money the executives tasks, with the scope covering branch, the values are compared with the database and the lock is allowed to open using a random One Time Password (OTP).