Reviewerly/exHarmony huggingface dataset:
Free Registration Based Shape Prior for Active Contours

A novel method of active contours with shape prior knowledge is presented in this research in order to improve its robustness for partially occluded objects. Our prior is based on a free-registration shape template estimation by using a complete and stable set of invariant descriptors. The prior template is then incorporated into the level set model, using a stopping function that updates the evolving curve only in the region of variability between the active contour and the researched template so that the computation time is reduced considerably. The proposed framework is demonstrated using both simulated and real data involving the segmentation of occluded and noisy images. Results show better robustness and stability compared to the well-known methods using statistics.
Augmented Reality Mobile Application: A Feasibility Study in a Local National Museum

Abstract In an age of thriving science and technology, the use of advanced technologies in the museum exhibition of museums in Malaysia is still limited, especially in the context of Augmented Reality (AR). Most of the exhibitions in Malaysia’s national museum are still using the passive and conventional information display methods. Hence, this study is intending to promote the usage of AR technology in the national museum exhibition by investigating the feasibility of AR mobile application usage in a local museum. A qualitative approach was used in this study including the interview with domain experts and template analysis. From the template analysis in this study, it shows positive feedback from the respondents. Positive results were shown where AR is considered as an innovative way to preserve and conserve history, enable to the capability to enhance the visitor experience, attract more potential visitors and elevate visitors learning level.
Multiparty Zero-Knowledge Protocol for User Authentication on Android

Nowadays, mobile Internet has been growing flourish and made transactions over the Web become more susceptible to eavesdropping. Moreover large number of user migrating to mobile web applications, hence remembering many user-id/password pairs becomes a tedious task. Therefore it is a necessary to build a robust and efficient authentication system. Zero-knowledge protocol is a cryptographic method that does not expose any secrecy in the communication channel. The solution proposed is to implement an interactive password-less authentication. Here two identification schemes based on the zero-knowledge protocol are implemented on Android smartphone, first a modified parallelism on the Feige-Fiat-Shamir scheme and second, the difficulty of prime number factoring on Guillo-Quisquater scheme. This paper presents running-time and security analysis benchmarking of both schemes. Experiment results show that the modified Feige-Fiat-Shamir scheme with quadratic running-time is faster than exponential running-time on the Guillo-Quisquater scheme, at the same length of public key size.
Human Attention Span Modeling using 2D Visualization Plots for Gaze Progression and Gaze Sustenance

This paper presents a novel perspective on human attention span modeling based on gaze estimation from head pose data extracted from videos. This is achieved by devising specialized 2D visualization plots that capture gaze progression and gaze sustenance over time. In doing so, a low-resolution analysis is assumed, as is the case with most crowd surveillance videos wherein the retinal analysis and iris pattern extraction of individual subjects is made impossible. The information is useful for studies involving the random gaze behavior pattern of humans in a crowded place, or in a controlled environment in seminars or office meetings. The extraction of useful information regarding the attention span of the individual from the spatial and temporal analysis of gaze points is the subject of study in this paper. Different solutions ranging from plotting temporal gaze plots to sustained attention span graphs are investigated, and the results are compared with the existing techniques of attention span modeling and visualization.
Task-adaptive incremental learning for intelligent edge devices

Convolutional Neural Networks (CNNs) are used for a wide range of image-related tasks such as image classification and object detection. However, a large pre-trained CNN model contains a lot of redundancy considering the task-specific edge applications. Also, the statically pre-trained model could not efficiently handle the dynamic data in the real-world application. The CNN training data and their label are collected in an incremental manner. To tackle the above two challenges, we proposed TeAM - a task-adaptive incremental learning framework for CNNs in intelligent edge devices. Given a pre-trained large model, TeAM can configure it into any specialized model for dedicated edge applications. The specialized model can be quickly fine-tuned with local data to achieve very high accuracy. Also, with our global aggregation and incremental learning scheme, the specialized CNN models can be collaboratively aggregated to an enhanced global model with new training data.
A Graph Representation Learning Algorithm Based on Attention Mechanism and Node Similarity

Recently graph representation learning has attracted much attention of researchers, aiming to capture and preserve the graph structure by encoding it into low-dimensional vectors. Attention mechanism is a recent research hotspot in learning the representation of graph. In this paper, a graph representation learning algorithm based on Attention Mechanism and Node Similarity (AMNS for short) is proposed. Firstly, the similarity neighborhood is generated for each node in graph. Secondly, attention mechanism is used to learn weight coefficients for each node and its similarity neighborhood. Thirdly, the node vectors are generated by aggregating its similarity neighborhood with weight coefficients. Finally, node vectors are applied to many tasks, e.g., node classification and clustering. The experiments on real-world network datasets prove that the AMNS algorithm achieves excellent results.
Mobile Web-Based Information System for thesis’s Administration in University PGRI Yogyakarta

Abstract This research aims to develop an Information System thesis’s administration that efficient and effective supported by computerized administration system or on-line system. The subject of this research is Universitas PGRI Yogyakarta which have 13 study programs. Mobile web can use in the thesis’s administration system. It can facilitate the management of required letters and essay recap. This system can also browse the titles of the thesis that has been created so as to reduce duplicate titles of the thesis. This study concludes that the use of thesis administrative systems can facilitate and accelerate the management of the required letters. It also can sorting titles of the essay that has been taken so as to avoid duplication. Based on black box testing, it is found that the whole function has been run well, the functions tested include: (1) User Verification (2) Data Management (3) Information and reporting. 3) Based on the alpha test it can be seen that the system developed in usability is considered good, the assessment aspect includes: ease of use, ease of navigation, access speed, completeness of features and attractive appearance.
Emotion-Based Story Event Clustering

In this paper we explore how events can be represented and extracted from text stories, and describe the results from our simple experiment on extracting and clustering events. We applied k-means clustering algorithm and NLTK-VADER sentiment analyzer based on Plutchik’s 8 basic emotion model. When compared with human raters, some emotions show low accuracy while other emotion types, such as joy and sadness, show relatively high accuracy using our method.
Research on Cluster Mining Algorithms for Personal Privacy Protection in the Background of Big Data

Abstract With the rapid development of cloud computing and mobile Internet technology, huge amounts of data are generated on the network at every moment. In the era of big data, most of the data are designed in distributed storage, stored on many independent sites. The typical data mining algorithm can not meet the requirement of privacy data security in the big data background. In the big data background, distributed data mining is faced with the need to solve data security, privacy disclosure and other technical problems. Therefore, this paper applies homomorphic encryption technology to typical K-means clustering mining algorithm, and designs a distributed privacy protection PP-kmeans algorithm based on big data. Experiments show that this algorithm can realize data privacy security protection. And more accurate big data clustering mining effect is achieved.
The Future of Cyber Analytics: Identity Classification for Systematic and Predictive Insight

The Internet's constant-changing infrastructure and technology calls for broader, more robust methods that can adapt and aid in establishing/strengthening linkages between a physical person and an online persona based on representative use cases with time, human, and cyber-physical system dimensions. This research introduces a novel identity classifier system comprised of a defined scheme, salient features, and mathematical axiom-based model logic of mapped type patterns utilizing contextual reasoning over time for consistent, long-term analysis. This holistic multi-discipline approach was designed with real-world case simulated evidence data to determine a mapped identity type. Ph.D. experimentation has proven effective design aspects for demographic profiles and more targeted identities. The bio-inspired, system-based design was also intended to support emerging and/or learned features, and help determine human characteristics or a person's unique signature. Experiment trials are promising and have yielded insightful visualizations and feedback for improved Situational Awareness. This paper focuses on evaluation of holistic identity classification system prototyped design with enhanced aspects of communication and automated control systems in both machines and living things which has revealed interesting types of human vs. non-humans. We leverage prior effective design features for contextual salience, and assess innovative living and non-living cybernetic identity classification features for systematic and predictive insight.
A Memory Acquisition Method for Android Application Forensics

Abstract With the rapid development of social economy, the number of Android mobile devices has increased rapidly in recent years. At present, digital forensics experts mainly extract digital evidence from non-volatile physical memory and file system of mobile devices. However, non-volatile storage based method has little effect for some high secure Apps forensics, such as the encrypted data or deleted information, e.g. the private conversation records or payment records, in chat Apps or payment Apps. Accordingly, data residential in memory is an important source of evidence. On the other hand, relying solely on digital evidence in non-volatile storage is incomplete and unreliable as well. So, the demand for memory forensic on the Android platform has increased. In this paper, based on ptrace a scheme for Android memory extraction at the process level is proposed. For this scheme, a complete memory mirror of a specific process can be achieved, including memory space shared anonymously by the process and other processes, the memory space of the process, and the memory space of each thread in process. This method is of great importance to high secure Apps forensics, such as Chat Apps or Bitcoin Payment Apps.
An improved dark channel prior based defogging algorithm for video sequences

AbstractAbstractBad climatic situations comprising of haze, dimness, and residue frequently decrease the general execution of outside cameras. Fog carries an issue to PC vision/photographs applications as it drops deceivability of the scene. The murkiness is framed because of principal marvels, which may be a constriction and air-light. Weakening diminishes the differentiation and air-light will expand the whiteness inside the scene. Cloudiness disposal systems improve the shading and correlation of the stage. To enhance the adequacy of surveillance and in-vehicle cameras underneath such conditions, a method dependent on the dull channel earlier for snappy defogging of pictures is proposed. On this work to defog an image, dark channel prior based algorithm is carried out the use of atmospheric scattering version. The proposed algorithm estimates the air light-based totally on the input image, calculating the preliminary transmission rate map, refining it and then finally restoring the fog image and it is applied for a real-time video sequence, and the video frames are defogged.Subject Classification: 65D1868U10Keywords: Modified Dark Channel PriorContrast limited Adaptive HistogramEqualizationDiscrete cosine transformHazeReal-time video sequence
Arena Mapping Rover

Objectives:The study aims at designing an Arena Mapping Rover that has adequate functions to optimally map and analyze the shape of an enclosed space, which cannot be satisfactorily done by the human being or which may be too hazardous for the human being to explore.Methods: The rover is capable of achieving this by moving around the different parts inside the arena.It continuously retrieves data related to the surrounding of enclosure and simultaneously sends the data to the computer.Findings: The computer processes the acquired data and presents a two dimensional reconstruction of the arena environment.Applications: The autonomous rover consists of a firm metal structure, which makes use of two stepper motor for precise movement inside the arena.Three infrared proximity sensors, facing three different directions detect the boundary and obstacles in vicinity.
Structure and Delivery for Mobile Learning Experiences

The purpose of this article was to identify, implement, and evaluate the effectiveness of best practices from the mobile learning literature for the structure and delivery of mobile learning. Mobile learning activities were deployed in a videoconference equipment training course which was accessed by physicians, nurses, and healthcare professionals at medical organizations across Ontario. With regards to mobile learning delivery, user flexibility and control were identified as critical when utilizing a mobile learning experience to apply knowledge in a specific learning context. Avatar hosts were also identified as effective feedback and guidance mechanisms. The informal structure of mobile learning proved to be ideal for contextual, hands-on learning of specific workplace skills, supported by the baseline and summative knowledge provided by the online learning course. This study found that the structure and delivery of mobile learning must be considered during the instructional design stage in order to provide practical learning experiences and reliable learning outcomes.
2D/3D Mixed Interface for Furniture Placement in Smartphone-based Mobile Augmented Reality

In this work, we propose to use an approximate 2D map of the environment generated from the latest environment modeling technology and enhance the object manipulation performance for the touch based mobile augmented reality. We validated the advantage of the proposed interface through a pilot experiment and confirmed that the use of the 2D map helps reduce the task completion time almost 2 times and improve the usability as well.
Analysis of cryptosystem recognition scheme based on Euclidean distance feature extraction in three machine learning classifiers

Abstract In reality, many cryptographic analysis techniques are based on a specific cryptographic system or a large number of encrypted ciphertext. The identification and detection of cryptographic system is of great significance for evaluating the security of the algorithm and guiding the design and improvement of the algorithm. In this paper, we transcode each character in ciphertext into a decimal number, construct these numbers into one-dimensional arrays, and obtain the Euclidean distance between these one-dimensional arrays. Then we use these distances as features and input them into three machine learning classifiers: random forest, logistic regression and support vector machine to recognize cryptosystem and compare their recognition accuracy. The subjects include 8 common block ciphers (DES, 3DES, AES-128, AES-256, IDEA, SMS4, Blowfish, Camellia-128). The experimental results show that using the feature extraction scheme not only shortens the experimental time, reduces the computational cost, but also improves the recognition accuracy of eight typical block cipher algorithms. The classification accuracy of the ECB mode in the random forest classifier is 75%, which is higher than the existing published literature experimental results. The classification accuracy rate of CBC mode is higher than 13.5%, which is higher than the accuracy of random classification.
Visually Exploring Software Maintenance Activities

Lehman's Laws teach us that a software system will become progressively less satisfying to its users over time, unless it is continually adapted to meet new needs. A line of previous works sought to better understand software maintenance by studying how commits can be classified into three main software maintenance activities. Corrective: fault fixing; Perfective: system improvements; Adaptive: new feature introduction. In this work we suggest visualizations for exploring software maintenance activities in both project and individual developer scopes. We demonstrate our approach using a prototype we have built using the Shiny R framework. In addition, we have also published our prototype as an online demo. This demo allows users to explore the maintenance activities of a number of popular open source projects. We believe that the visualizations we provide can assist practitioners in monitoring and maintaining the health of software projects. In particular, they can be useful for identifying general imbalances, peaks, deeps and other anomalies in projects' and developers' maintenance activities.
Changing roles of State Geological Surveys in the USA: experiences from Illinois

Abstract State Geological Surveys (SGSs) in the USA play vitally important roles, providing sound, unbiased scientific information to each state and the nation. Although implementation of each survey's scientific programme has evolved differently, these organizations are often the principal drivers of economic development, and they consult on policies for protecting land and water, mitigating geologic hazards and promoting sustainable development. The SGSs are represented by the American Association of State Geologists. For more than 110 years, they have partnered with the federal government on important geoscience issues concerning topographic and geologic mapping, water, mineral and energy resources, and geologic hazards. These collaborations continue to develop and expand across multiple specialties, providing critical support to the SGSs. The future role of SGSs will depend on legislative decisions, contributions to research and development and scientific advancement, and ability to leverage support from existing and new collaborations with academia and with federal, state, county and municipal agencies. The Illinois State Geological Survey, one of the largest SGSs, has continually pursued relationships across geoscience sectors to develop a strong multidisciplinary scientific programme. Going forward, all SGSs will be challenged to develop an effective data stewardship programme to communicate with a diverse clientele.
ReTreeve

For the past 30 years, there has been a steady onslaught of the Digital Era. An exponential increase in the quantity of information and not a proportional increase in our capacity to accommodate it has resulted in information overload. In an era where everything we use is designed to fight for our attention, attention management is one of the biggest challenges we face. Through interviews, observational sessions, and focus groups, insights were gained, which informed the design of the concept. The concept proposed in this paper is a system which makes the user better at prioritizing what is important to pay attention to, using machine intelligence. We achieved this by augmenting the functioning of the human mind by learning from a few biological traits of a tree. What we learned was then used to create a biological metaphor-driven framework. Along with managing attention using the framework, an additional visualization aims to amplify an otherwise abstract cognitive representation of what is purposeful and meaningful in life.
Decision Fusion for Partially Occluded Face Recognition Using Common Vector Approach

Partial occlusions in the face image negatively affect the performance of a face recognition system. Modular versions of some methods are used to overcome this problem. Modular Common Vector Approach (MCVA) was successfully applied partial occlusion problem. In this work, we apply some well-known decision fusion methods (product rule, borda count, and majority voting) to the decision stage of MCVA approach to increase the classification performance. A well-known appearance based feature descriptor so called Local Binary Patterns (LBP) is used to extract the facial features. The performance comparisons are conducted on AR face database with several experiments. It is observed that combining the classifier outputs using decision fusion methods increase the classification performance of MCVA.
Domain-specific requirements analysis framework: ontology-driven approach

Requirement engineering (RE) is the first track in the course of software development that represents and analyses the problem domain for suitable solutions. This improves the quality of the software development and decreases the risk of budget overrun, delays and project failure. However, traditional methods are inadequate to address different problems in RE. Prime challenges among those are lack of interoperability between heterogeneous requirements models, less focus on validation of requirements, and inability to reuse similar requirement specification in different domains. To solve these challenges, semantic approaches are needed to represent commonalities and variability in requirements across different applications and domains. This paper is aimed to address this issue. A requirement analysis framework named as Ontology-driven requirement analysis (ODRA) is proposed in this paper. Proposed ontology-driven specification provides common semantics towards different requirements. Proposed framework also comprises Formal Scenario descriptions and cause-effect-dependency graphs to conduct an early and a detailed analysis of requirements. It is capable to represent 5W1H characteristics (What, Why, Who, When, Where and How) of requirements. Further, it devises a set of key validation rules and a customized rule-based reasoner to validate consistency, unambiguity, and traceability in requirement specifications.
Big Data and Clustering Techniques

Big data has become a chief strength of innovation across academics, governments, and corporates. Big data comprises massive sensor data, raw and semi-structured log data of IT industries, and the exploded quantity of data from social media. Big data needs big storage, and this volume makes operations such as analytical operations, process operations, retrieval operations very difficult and time consuming. One way to overcome these difficult problems is to have big data clustered in a compact format. Thus, this chapter discusses the background of big data and clustering. It also discusses the various application of big data in detail. The various related work, research challenges of big data, and the future direction are addressed in this chapter.