Reviewerly/exHarmony huggingface dataset:
Transfer Learning of Mixture Texture Models

A transfer learning approach for multidimensional parametric mixture random field-based textural representation is introduced. The proposed transfer learning approach allows alleviating the multidimensional mixture models requirement for sufficiently large, but not always available, learning data sets. These compound random field models consist of an underlying structure model that controls transitions between several sub-models, each of them has different characteristics. The structure model proposed is a two-dimensional probabilistic mixture model, either of the Bernoulli or Gaussian mixture type. Local textures are modeled using the fully multispectral three-dimensional Gaussian mixture sub-models. Both presented compound random field models allow the reproduction of, compresses, edits, and enlarges a given measured color, multispectral, or bidirectional texture function (BTF) texture so that ideally, both measured and synthetic textures are visually indiscernible.
Research on Human Computer Interaction

Human-Computer Interactions (HCI) were established in the early 1980s with a strong computer science specialization. The relationship between human-computer engineering and cognitive science encompassed human factors. In the last thirty years, human-computer experiences have been expanding rapidly and swiftly, drawing practitioners from other fields and allowing them to integrate their different methods and concepts. More than 80% of human computing experiences are anticipated by speech recognition during the next five years. In the last two centuries, computer use has increased exponentially in all the social fields easing operations and enhancing human life. The most critical elements of HCI are usability and functionality, which enhances the interactions.
Inpainting via sparse recovery with directional constraints

Image inpainting is a particular case of image completion problem. We describe a novel method allowing to amend the general scenario of using sparse or TV-based recovery for inpainting purposes by an efficient use of adaptive one-dimensional directional sensing into the unknown domain. We analyze the smoothness of the image near each pixel on the boundary of the unknown domain and formulate linear constraints designed to promote smooth transitions from the known domain in the directions where smooth behavior have been detected. We include a theoretical result relaxing the widely known sufficient condition of sparse recovery based on coherence, as well as observations on how adding the directional constraints can improve the well-posedness of sparse inpainting. The numerical implementation of our method is based on ADMM. Examples of inpainting of natural images and binary images with edges crossing the unknown domain demonstrate significant improvement of recovery quality in the presence of adaptive directional constraints. We conclude that the introduced framework is general enough to offer a lot of flexibility and be successfully utilized in a multitude of image recovery scenarios.
The Study of Audiences and Media Practitioner Behavior Towards the Information Related to Disclosure of Victim Information in Facebook

This project is to study on Malaysia audiences and media practitioner behaviortowards the information related to disclosure of victim information in Facebook. Inthis new era of technology, more and more people rely on social media to access newsand information. Social media users can easily access news and information thatcontain victims information, the spreading of news that contain victims informationwill bring more exposure to victims and it might leads to consequence such as victimblaming and further harm. Social media users especially young adults playing animportant role in this case as young adults are the most number of social media userstoday. Therefore, news and information that involved victims’ personal informationand identity should be protected and not expose to the public. Media practitionerplaying a role in dealing with the dilemma in publishing audience interest content orto follow the journalist code of ethics, the judgement for media to publish news thatcontain victims information still facing with a grey ethical area. Hence, the projectwill be focus on how the two party deal with news and information that disclosevictims information. Thereafter, quantitative and qualitative method is used toconduct the research. The research have involved 100 respondents from Klang Valleyand two experience media practitioners from the news industry. The results of datacollected shows that young adults users and media practitioner are not prefer to spreadthe news that contain victims information.
Demonstration of ElaStick: A Variable Stiffness Display for Rendering Handheld Flexible Object

We present ElaStick, a handheld variable stiffness controller capable of simulating the kinesthetic sensation of deformable and flexible objects when swung or shaken. ElaStick is capable of rendering gradual changes of stiffness along two independent axes over a wide continuous range. Two trackers on the controller enable a closed-loop feedback that allows to accurately map the device’s deformations to the visuals of a Virtual Reality application.
Fall risk among older adults residing in Bharatpur, Chitwan, Nepal

Background: Falls in older people is a common serious health problem that has profound im­pact on overall health and quality of life of older people. The aim of this study was to assess the fall risk among older adults.&#x0D; Methods: The descriptive cross-sectional study was carried out among older adults residing in Bharatpur, Chitwan. A total of 98 older adults were selected by using simple random sampling technique. The data were collected by using structured interview schedule and fall risk was as­sessed by Timed Up and Go (TUG) test. Data were collected from 23rd June, 2019 to 7th July, 2019. Obtained data were analyzed using descriptive and inferential statistics.&#x0D; Results: The study findings revealed that more than half (60.2%) of the older adults were from the age group of ≤79 years, male (63.3%) and almost half (50%) were illiterate. Nearly all (96.9%) older adults were living with their family however, 50% were undernourished (BMI- &lt;22.9). Ma­jorities (66.3%) were suffering from chronic diseases and had been taking medicine. Majority of older adults reported vision problem (64.3%) and hearing problem (60.2%). However, only 8.2% reported history of fall within last 6-12 months. More than half (59.2%) of the older adults had high risk of fall and found significant association with age (p=0.039) and vision problem (p=0.043).&#x0D; Conclusions: More than half of the older adults are in risk of falls. Therefore, more emphasis should be given in screening the older people for fall risk factors as preventive measures.
Σχεδιασμός και ανάπτυξη παράλληλου αλγόριθμου συσταδοποίησης στο Apache Spark

Clustering is a common technique for data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the process of grouping similar objects into different groups. One of the biggest problems nowadays is the increasing volume of data which must be analyzed. This thesis addresses the problem by implementing DBSCAN, a well-known clustering algorithm, using the Apache Spark framework in order to allow its execution on a computer cluster. This approach aims to overcome the issues state of the art implementations face when they deal with high volumes of data. The basic ideas of how DBSCAN works are presented and the proposed modifications are thoroughly explained along with the reasons they were necessary. Furthermore, a series of experiments are executed to evaluate the performance of the algorithm in comparison to other clustering algorithms which are provided by Spark such as GMM and K-means. Limitations of our approach are also discussed
BLOCK CHAIN BASED DECENTRALISED TWITTER DAPP

The block chain is an innovative technology which opened doors to new applications for solving numerous problems in distributed environments. In this work, we design a block chain based data storage and access framework Twitter application to remove its total dependence on a centralized repository. We use the public block chain and tools like Ganache, Metamask, Truffle and Ethereum IDE for deploying the contracts. In the proposed work, metadata of the files are stored on the block chain and we use the networks like Ropsten and Rinkebey for occurring the Transactions using a peer -to-peer networks. This will provide decentralized storage, distributed processing, and efficient lookup capabilities.
Intuitive and Analytic Decision Making in Employee Selection Process : Intuitive Decision

This experimental study aimed to investigate intuitive and analytic decision making approach when assessing job applicants during employee selection process, and feedback related to employee select ...
SEMANTIC MAPPING TO INCREASE TECHNICAL VOCABULARIES FOR MIDWIFE AT UNIVERSITAS PRIMA INDONESIA IN ACADEMIC YEAR 2019/2020

This study was conducted to increase midwife students’ Vocabularies at Universitas  Prima Indonesia by using Semantic Mapping Technique. This research was conducted to find out whether the Semantic Mapping Technique gave a significant effect on midwife students’ achievement in vocabularies at Universitas Prima Indonesia Medan. This research was intended to find out whether there were any differences between the midwife students’ vocabulary after being taught by using Semantic Mapping Technique. Semantic Mapping is Technique of visual for the exspansion of vocabularies and knowledge extension by displaying in words of categories related to another. This research applied one group for pre-test and post-test. The sample of this research was midwife students at Universitas  Prima Indonesia Medan consist of 40 midwife students’ in academic year 2019/2020. This study was an experimental design.  The population of the research was midwife students at Universitas  Prima Indonesia. There were two classes as the sample and all of the midwife students as a sample is 42. The classes were divided into two groups, experimental and control group. The experimental group was the class taught by applying Semantic Mapping Technique, while control was taught without using Semantic Mapping Technique. In this research, a multiple-choice test was used as the instrument of collecting data. Both the experimental and control group were asked to answer the question in pre-test and post-test the data obtained were analyzed statistically by t-test formula. The result got that t-observed (〖t_〗_(obs )) was higher than t-table (4.41 > 2.0211) at the level significant 0.05 with the degree of freedom (df) 40. It means that the alternative hypothesis (H_a) is accepted and Semantic Mapping Technique significantly affects midwife students’ achievement to increase vocabularies.
On the Application of Embedded Computer System in Radar Scanning System

The development of computer technology is extremely rapid in today's world. The faster it develops, the more security risks it is exposed to and the stronger computing power of the computing systems and software it requires. There are many advantages of embedded computer systems, including not only good reliability, but also strong practicability. Therefore, embedded computer systems are widely applied in business and industry. Open or commercial applications allow computers to embed radar systems in many industries. Therefore, the computer embedded radar operating system has bright development prospects.
Bayesian network learning with nominal, ordinal and continuous data

Bayesian networks are a type of probabilistic graphic models composed of nodes and directed edges that serve to represent dependencies between the di erent random variables of the problem, as well as the conditional probability distributions associated to said variables. Originally, both the structure of the Network and the probability distributions were thought for discrete data, but due to the wide eld of application of Bayesian Networks, methods have been developed for other types of variables, as well as for problems with variables of various types. In this Master's Degree Dissertation, we seek to understand the operation of the di erent methods that have been developed to deal with the learning of Bayesian Networks with not only discrete variables, but also continuous, ordinal and mixed variables. Although one of the most common methods to treat Bayesian Networks with continuous variables is discretization, and some methods will be brie y explained, an attempt will be made to explain alternative methods to avoid the loss of information and the error with respect to the modelling of reality that it entails. After the completion of the thesis, it is expected to have an understanding of the subject, and to be able to transmit this knowledge to the reader in a clear and concise way. Structure learning methods will be explained, as well as the conditional probability distributions used to represent each kind of variable, together with the parameters to be learned. Also, methods of study of independence such as Mutual Information, and their estimation from a training database will be explained, as well as the Jonckheere-Terpstra Test for ordinal variables.
Research Guides: Welcome to Using Primo: Article Searching - Advanced

A resource guide for students and faculty about the transition to the Primo VE discovery service.
A systematic framework for categorising IoT device fingerprinting mechanisms

The popularity of the Internet of Things (IoT) devices makes it increasingly important to be able to fingerprint them, for example in order to detect if there are misbehaving or even malicious IoT devices in one’s network. However, there are many challenges faced in the task of fingerprinting IoT devices, mainly due to the huge variety of the devices involved. At the same time, the task can potentially be improved by applying machine learning techniques for better accuracy and efficiency. The aim of this paper is to provide a systematic categorisation of machine learning augmented techniques that can be used for fingerprinting IoT devices. This can serve as a baseline for comparing various IoT fingerprinting mechanisms, so that network administrators can choose one or more mechanisms that are appropriate for monitoring and maintaining their network. We carried out an extensive literature review of existing papers on fingerprinting IoT devices – paying close attention to those with machine learning features. This is followed by an extraction of important and comparable features among the mechanisms outlined in those papers. As a result, we came up with a key set of terminologies that are relevant both in the fingerprinting context and in the IoT domain. This enabled us to construct a framework called IDWork, which can be used for categorising existing IoT fingerprinting mechanisms in a way that will facilitate a coherent and fair comparison of these mechanisms. We found that the majority of the IoT fingerprinting mechanisms take a passive approach – mainly through network sniffing – instead of being intrusive and interactive with the device of interest. Additionally, a significant number of the surveyed mechanisms employ both static and dynamic approaches, in order to benefit from complementary features that can be more robust against certain attacks such as spoofing and replay attacks.
A Survey of Authorship Identification in Digital Forensics based on Machine Learning Algorithms

Authorship Identification is subfield of authorship analysis deals with finding the plausible author of anonymous messages. The Authorship identification problem of online messages is challenging task because cyber predators make use of obscurity of Cyberspace and conceal the identity. Cybercriminals make misuse of online communication for sending blackmail or a spam email and then attempt to hide their true identities to void detection. Authorship Identification of online messages is the contemporary research issue for identity tracing in cyber forensics. This is highly interdisciplinary area as it takes advantage of machine learning, information retrieval, and natural language processing. By performing the forensic analysis of online messages, empirical evidence can be collected. These evidences can be used to prosecute the cybercriminal in a court and punish the guilty. This way cybercrimes can be minimized up to certain extent by detecting the true identities. Therefore it is required to build up innovative tools & techniques to appropriately analyze large volumes of suspicious online messages. This paper compares the Performance of various classifiers in terms of accuracy for authorship identification task of online messages. Support Vector Machines, KNN, and Naive Bayes classifiers are used for performing experimentation .This paper also investigate the appropriate classifier for solving authorship of anonymous online messages in the context of cyber forensics
The Development of XML Message for Status Tracking the Importing Agrifoods During Transport by UBL

The imported foods, which are imported and sold domestically, are on the rise every year, and the scale is expected to be larger, including processing the imported raw materials. However, the origin of raw materials is indicated when declaring cargo for finished products of agricultural products, but the standardization of inspection information management system for raw materials is insufficient. In addition, there is a growing concern about the presence of residual pesticides or radioactivity in raw materials or products, and customer want to know production history information when purchasing agrifoods. It manages the hazard analysis of imported agricultural products, but most of them are global issues such as microorganisms, residual pesticides, food additives, and allergy components, etc. Therefore, it is necessary to share among the logistics entities in the entire transportation process the related data. Additionally, to do this, it needs to design an architecture and standardize business model. In this paper, it defines the architecture and the work-flow that occurs between the business process for collecting, processing, and processing information for tracking the status of imported agricultural products by steps, and develops XML message with UBL and the extracted conceptual information model. It will be easy to exchange and share information among the logistics entities through the defined standard model and it will be possible to establish visibility, reliability, safety, and freshness system for transportation of agricultural products requiring real-time management.
Dr Wan Isni Sofiah develops algorithm for saving battery usage in sensor nodes

Kuantan, 26 October 2020 - Batteries are an important element for wireless sensor nodes to perform the process of sending and transmitting data.
Research Guides: Electronic Resources at The Evergreen Library: Streaming Media & Art

This guide aims to bring together all the electronic resources (e-books, e-journals, databases, streaming media) and digital content available through The Evergreen State College Library.
Real-Time Pose Estimation of a Mobile Rover Using Kinect Sensor

The critical challenge of vision-based control of the mobile robot is to accurately estimate the position and orientation (pose) in the exploration of unknown terrains. In this paper, first a new approach of determining random er-rors in a pose measurement of Kinect scanner's 3D datasets by frame propagat-ing transformation approach is presented. Next, 3D visual information provided by Kinect scanner is used to track the optimum (minimum error) pose path of the rover through Kalman filter. The experimental results have been performed on real terrains which demonstrate the proposed approach efficiently tracks the poses from noisy 3D datasets.
THE EFFECT OF KNOWLEDGE SHARING ON JOB PERFORMANCE IN JORDANIAN PUBLIC UNIVERSITIES

This study aims to test the impact of knowledge sharing on job performance in Jordanian public universities. To fulfill this aim, a random sample consisted of (350) faculty members who were selected from two Jordanian public universities (Yarmouk University, Jordan University of Science& Technology). Responses were collected through structured questionnaires from (244) faculty members of different disciplines at these two universities, with a response rate of 70%. The analyzed data showed that the level of sharing knowledge (both implicit and explicit), and the level of job performance among faculty members is moderate. It also showed that the knowledge sharing process has an impact on job performance among these faculty members. The study provides some recommendations for the researchers in this subject.
NOVEL CONCEPT SPECIFIC LEARNING MODEL FOR CLASSIFICATION OF DYNAMIC EVOLVING DATA STREAMS TOWARDS ELIMINATING RECURRING CLASSES

Data classification has been the focus on research community due to its wide data oriented applications. A key problem concerns processing a large amount of data, while the underlying dynamic phenomena on the changes in concept of the data possibly evolving over time, its unbounded size and arriving of data at steady fast rate is also considered as major challenging traits. Various techniques have been employed to classify the evolving data streams with emergence of a new class. These techniques fail to detect the recurring classes which occur when a class reappears after a long disappearance from the stream. Recurring classes creates undesirable effects such as false alarm rate and consumes enormous computational resources. In order to handle these challenges, a proposal of concept specific learning model for data classification of dynamic evolving data streams on estimation of the recurring classes on distributed server before new class generation is made. In Training phase, classifier are built for evolving data streams in the ensemble manner with decision boundary for each class as class membership and it is frequently updated using incremental learning principles. Each classifier uses the bag of words and POS tagging to identify the importance features and its evolution. POS tagging is the process of marking up a word in a corpus to a corresponding part of a speech tag, based on its context and definition. In addition, this data classification model is considered as error driven representativeness as it categories accurately the noisy features in the data streams either using dynamic heuristics or probability measures on uncertain data against class membership. Furthermore Concept dript is predicted against the recurring classes using relationship computation on data using wordnet. Experimental results of proposed model on benchmark dataset exhibits high performance in terms of accuracy and resource utilization against state of art approaches.
A Method Based on Floyd’s Algorithm for the Design of the Optimal Workflow of Web Services

There are too many web services worldwide. Because of it, the manual design of an efficient and low-cost sequence of services to do a task (i.e. Optimal Workflow), is hard and complicated. To solve the mentioned problem, software producers decided to use the methods in the design of the optimal workflow of web services. It means that the optimal workflow is automatically designed by using methods. Presentation of a novel method for the automatic design of the optimal workflow is main goal in this paper. The hypothesis is that if floyd’s algorithm is applied to the design of workflow then the workflow would be optimal. During an experiment, the proposed method was compared with an analogous method named multi-criteria search algorithm. The obtained results showed that the presented method correctly recognizes the optimal workflow