Reviewerly/exHarmony huggingface dataset:
Lymphoblast cell morphology identification to detect Acute Lymphoblastic Leukemia (ALL) using various color segmentation

Abstract Acute Lymphoblastic Leukemia (ALL) is a disease that is defined by the uncontrollable growth of abnormal cell of lymphocyte, which is called lymphoblast. ALL patients cannot be left untreated as it can be fatal, and hence, early detection is very crucial for proper treatment suggestion. Conventionally, ALL analysis is manually done, which is time-consuming and highly dependent on the pathologist’s skills. Furthermore, it will be hard to pathologist as the number of sample increases, and also there are other cells inside the blood smear image, which will create confusion to them. Haematology counter is great to assist the process, but unfortunately, the cost is unbearable for some countries. For that reason, in this project, a computer-aided ALL detection with fast response is proposed. The system consists of five main modules which are image acquisition, pre-processing, segmentation, feature extraction and classification. For the process flow, firstly the color space correction based on l*a*b* color space is applied to standardize the color of the input image. Next, WBC segmentation is employed to locate the WBC region and consequently divided it into two parts which are the nucleus and cytoplasm based on a combination of color space analysis and Otsu thresholding. However, the segmented image contains noises and hence, is eliminated by using a combination of morphological filter and Connected Component Labelling (CCL). Then, the feature extraction process is made to study the nature of each individual cell using features derive from color, texture and geometrical properties. Lastly, lymphoblast classification is incurred to categorized the lymphoblast and the non-lymphoblast cell by employing Support Vector Machine (SVM) with a linear kernel function. As mentioned before, WBC segmentation is divided into two parts, and nucleus detection accuracy is higher than cytoplasm detection accuracy, which is 98.87% and 74.12% respectively. The presence of color space correction is analyzed, and the result is better with 96.92% accuracy for the presence of color space correction compared to 93.55% accuracy for without color space correction result. Classification performance is able to achieve 98.72% of accuracy.
A narrative modeling platform: Representing the comprehension of novelty in open-world systems

Reasoning is dynamic. Human understanding evolves across changing and unexpected circumstances, whether escaping a war zone or watching a suspenseful movie. General and past knowledge is often insufficient to make sense of these unfolding situations; it must be adapted to account for unexpected inputs. This chapter describes an early-stage prototype of a modeling platform that tracks how humans adjust old information toward new contexts, revising their understanding of real-world situations. The process is modeled as streams of narrative—as an interplay of semantic networks that represent mental, social, and cultural webs of information. Its taxonomy is newly realized in a Unity 3D environment so that annotations can be spatially captured and anchored in inhabitable spaces. The result is a new method for modeling the emergence of unexpected entities and contexts, including shared contexts. This can endlessly connect artifacts of different media and sources in a unifying immersive space. A long-term plan for these models is to inform artificial intelligence that can reason about situations that unfold past the ontological boundaries of their general reference frameworks.
Research on Computer Network Security Policy of Cloud Computing

Abstract Cloud computing is a special computing model, which provides growth, use, and delivery services for internet data. In the application of cloud computing technology, the security problems of the computer network in some cloud computing environments gradually appear, resulting in the security troubles of computer network applications. This paper attempts to study the computer network security problems in the cloud computing environment, and proposes the corresponding computer network security strategy, to promote the computer network security, reduce the adverse effects of security problems, for the relevant personnel to provide a reference.
Big Data Architecture: Designing the Right Solution for Social Network Analysis

This paper highlights the link between social networks and Big Data, which is generated by its users and how the generated data is used by businesses to improve their products and services as well as to increase their profits. It goes on to give a concise overview of Big Data architecture, the various components involved, challenges faced when developing solutions, the process of retrieving, storing and processing datasets as well as recommendations to follow when designing architectures geared towards social network analysis.
Packet Header Obfuscation Using MIMO

Eavesdroppers can exploit exposed packet headers towards attacks that profile clients and their data flows. In this paper, we propose FOG, a framework for effective full and partial header blinding using MIMO, to thwart eavesdroppers. FOG effectively tracks header bits as they traverse physical (PHY) layer sub-systems that perform functions like scrambling and interleaving. It combines multiple blinding signals for more effective and less predictable obfuscation, as compared to using a fixed blinding signal. We implement FOG on the WARP platform and demonstrate via extensive experiments that it yields better obfuscation than prior schemes that deploy full packet blinding. It causes a bit error rate (BER) of > 40 % at an eavesdropper if two blinding streams are sent during header transmissions. Furthermore, even with full header blinding, FOG incurs a very small throughput hit of ≈5% with one blinding stream (and 9 % with two streams). Full packet blinding incurs much higher throughput hits (25 % with one stream and 50 % with two streams).
Radiator threat assessment based on MEA-BP neural network

Abstract In order to promote the effectiveness of radiation threat assessment, this article builds the mind evolutionary algorithm (MEA) to optimize the BP neural network model of radiation threat assessment. The mind evolutionary algorithm was used to optimize the BP neural network model, through global population convergence and local populations of alienation, the network’s initial weights and threshold and then training the BP neural network, overcoming the random initial value when the disadvantages of neural network fall into the local optimum. The experimental results show that the evaluation results of the BP neural network optimized by the thinking evolution algorithm are significantly smaller than the mean square error of the BP neural network, this algorithm can quickly and effectively implement radiation threat assessment.
Capacity Analysis of Distributed Computing Systems with Multiple Resource Types

In cloud and edge computing systems, computation, communication, and memory resources are distributed across different physical machines and can be used to execute computational tasks requested by different users. It is challenging to characterize the capacity of such a distributed system, because there exist multiple types of resources and the amount of resources required by different tasks is random. In this paper, we define the capacity as the number of tasks that the system can support with a given overload/outage probability. We derive theoretical formulas for the capacity of distributed systems with multiple resource types, where we consider the power of d choices as the task scheduling strategy in the analysis. Our analytical results describe the capacity of distributed computing systems, which can be used for planning purposes or assisting the scheduling and admission decisions of tasks to various resources in the system. Simulation results using both synthetic and real-world data are also presented to validate the capacity bounds.
An Interface for Managing users' Preferences in AmI

One important concept of Ambient Intelligence (AmI) is meeting the user needs unobtrusively. To meet such needs, it is crucial to provide way(s) that will allow users to indicate their preferences to the system and allow users update them easily. As this will be used by the system to reason about their preferences and better align services with users behaviour. The paper aim to identify an effective way to manage, represent and reason with users preferences. A simple interface for managing users' preferences was provided to help manage and resolve some of the complexities in users' preferences. The interface also relied on textual menus for simplicity. Videos demonstration have been provided to show how the interface effectively works with a reasoning system in a smart house, providing different results when there is a change in users' preference(s).
Data Analysis on a Domestic Media Space Connecting Internationally Distributed Families

Families separated by distance face the challenge of limited bonding, affecting their opportunities to connect and sustain their relationships. FamilySong is a domestic Media Space that fosters feelings of togetherness via synchronized music-listening. FamilySong was used in an experiment that ran for six months, with six distributed families. Detailed data collection, e.g. interaction routines and music sharing patterns, was gathered through the system. These data provide interesting insights into the social behavior of families separated by distance and how they connect over those distances. In this work, we built software to analyze the experiment's data. The procedures and insights described in this paper will be useful for other researchers and practitioners involved in Information and Communications Technologies projects, making the FamilySong analysis with this software an exemplar for efficient data analysis.
Hardware Implementation of Automatic Modulation Classification with Deep Learning

Very recently, neural network based deep learning (DL) models are being applied to solve the problems of communication system. In this work, we design a deep neural network (DNN) based automatic modulation classification (AMC) scheme for wireless communication scenario. In particulars, we have implemented a DL network (ModNet) to identify the modulation scheme of received signal in real time using universal software radio peripheral (USRP) hardware. To identify the correct modulation scheme, the constellation diagram of the received complex symbols is plotted in image format. Thereafter, this image is send to pre-trained ModNet classifier for classification. Based on the constellation image and intelligence gained during training, ModNet determines the modulation scheme of the received signal. The proposed ModNet architecture is based on well-known image classification network AlexNet. Simulation result shows, the proposed modulation detection scheme provides higher classification accuracy than other existing modulation classification methods. The same has been verified through hardware implementation.
Effect of Internet Language on Chinese Language Teaching Considering Register Theoretical Analysis

Abstract Register theory is one of the essential theories in Halliday’s systemic functional linguistics (SFL). Specifically, it includes the linguistic field, tenor, and mode. In this paper, the characteristics of computer-mediated communication (CMC) and Internet language are analyzed from the three aspects to identify and verify the practicality and feasibility of the register theory in the new communication mode of Chinese language teaching.
Mind Map and Business Process Model: Specification support by model transformation

One of the main reasons for the software project setback is the poor level of requirements specification. Communication between customer and developer is often a problematic part of the requirements specification phase. This also arises from the fact developers use modeling techniques specific for the software engineering domain to record requirements. However, customers often have difficulty understanding what the models are representing because they do not know the notations used in the domain of software engineering. The solution could be in using simpler, natural and comprehensible modeling techniques to most people. Some models, because of their simplicity, may be less expressive than the more complex models. But they could be able to help customers to understand what the software engineering models represent. The paper deals with the transformation between these two types of models.
Motion Sickness Minimisation in Autonomous Vehicles Using Optimal Control

This paper presents the application of optimal control theory in order to minimise motion sickness in autonomous vehicles. By formulating an optimal control problem, the optimum velocity profile is sought for a predefined road path from a specific starting point to a final one. Specific and given boundaries and constrains are applied in order to minimise the motion sickness without compromising the journey time. For the representation of the motion sickness as a cost function to our optimal control problem, the illness rating is used. Different case studies are investigated by changing the cost functions of our problem for a fixed road trajectory of the vehicle in three road routes by varying the road curvature. According to the results, the road with flexible lateral manoeuvrability is found to provide lower sickness as well as shorter journey time when both motion sickness and journey time are considered in the cost function.
The Development of A Mandarin Learning Mobile Application for Beginners

Abstract In the 21st century, Mandarin is spreading around globally and slowly competing with English. Many Mandarin learning applications, which focus on reading, speaking and listening, but not writing had emerged on market following the trend. Hence, there are Mandarin learners who can speak and read in Mandarin, but not in writing the words according to the correct sequence. This study is to develop a Mandarin learning application, which can increase the Mandarin proficiency of the beginners. This study helps the learners to understand the correct pronunciation and writing steps of Chinese characters. This application is effective in increasing the Mandarin proficiency of non-Mandarin speakers. Further research can be done by adding gaming elements to make the application more appealing and more interesting to use.
Fast image segmentation for pulmonary lesions using hybrid level set model

The lung cancer radiotherapy treatment widely depends on adequate diagnosis. The radiologists intend to reach an image segmentation efficiency in terms of accuracy and low computation cost. However, the pulmonary lesions segmentation is still considered as a challenging task due to the noise and intensity inhomogeneity present in Computed Tomography (CT). In this study, we proposed to accelerate the nonlinear adaptive level set model, using the Bayesian rule, by incorporated the double well potential in the regularization term to get accurate and fast pulmonary lesion segmentation in CT images. We have tested the proposed method on different sized and localized lesions. All the images were taken from the database without any preprocessing. The experimental results show significant speed improvement without losing the precision of segmentation.
Developing a Decision Support App for Computational Agriculture

In the age of climate change, increasing populations and more limited resources, efficient agricultural production is being sought by farmers across the world. In the case of smallholder farms with limited capacity to cope with years of low production, this is even more important. To help to achieve this aim, data analytics and decision support systems are being used to an ever greater extent. For rice/shrimp farmers in the Mekong Delta, Vietnam, trying to tune the conditions so that both crops can be successfully grown simultaneously is an ongoing challenge. In this paper, the design and development of a smartphone app, from a well researched Bayesian Belief Network, is described. This now gives farmers the ability to make better informed planting and harvesting decisions. The app has been initially well received by water management practitioners and farmers alike.
Research Framework of Evaluation Model to Assess the Effectiveness of Coordination Processes in Global Software Development Projects

Abstract Effective coordination is a vital aspect of successful Global Software Development (GSD) projects. Limited studies have examined coordination strategies and their related indicators. Therefore, this study focuses on assessing the coordination processes that require specific strategies and related indicators that can contribute to effective coordination. Thus, the main aim of this study is to present the entire research framework that will be used to formulate the Evaluation Model which consist of coordination strategies and related indicators. Research framework consists of three parts namely research philosophy, research design and research methods. Thus, this study elaborates each of this parts accordingly. The results of this study is the formulation of Evaluation Model to assess the effectiveness of the coordination processes in GSD projects.
Research and Design of Online Learning System Based on Cloud Platform

Abstract The traditional online learning platform has limited educational resources and isolated resources, so it is difficult to support the operation of massive data. This paper designs an online learning system based on cloud computing platform to effectively integrate resources, reduce operation and maintenance costs. The platform uses big data and distributed computing technology to collect and process user access history data and log data, and uses collaborative filtering algorithm to recommend courses, so as to improve the efficiency of learners.
Testing of R-EMS 1.1 Modular Renewable Energy Management System with the Mini SCADA Concept Using IoT

Abstract The 4.0 industrial revolution marked by the existence of the Internet of Things caused the development of technology increasingly rapidly. That makes the need for the use of electrical energy also increases rapidly. Cooperation between the government and the community is needed to increase the role of renewable energy. Thus minimizing the use of electricity from PLN and optimizing electricity from renewable energy. Modular R-EMS is a technological innovation that enables the use of more than one renewable energy that can monitor and control wirelessly using the Internet of Things. Modular R-EMS has an interface to make the user easy to see the system. That Interface displays data in the form of voltage, current, power, percent of battery, monthly usage charge, and load back up system when PLN is not operating. Error generated at AC voltage 0.9% DC voltage 1.89% AC current 0.7% DC current 3%. In the process of sending data in the form of current and voltage from the module to the smartphone has a time lag of 3 seconds. The use of PLN energy at a load of up to 100% at night. The use of renewable energy in the morning 76.58% to 100% in the afternoon 78.95% - 100% in the afternoon 19.64% to 62.85%. The excess power from renewable energy automatically flows to PLN.
Generalized quantum steering ellipsoids for a qubit-field system

Quantum steering in a system consisting of a qubit coupled to a single-mode field are explored when classical-like measurements implemented by heterodyne detection schemes that collapse the state of the field on to a coherent state are considered with the aim of constructing the generalized quantum steering ellipsoid for the qubit. The steering ellipsoid of the qubit enables visualization of the set of states on to which it can be steered using such measurements. In some cases, the steering set does not form an ellipsoid since only heterodyne detection is considered. This is particularly the case when the initial qubit-field state is mixed. The structure of the steering set is explored in detail for this case with representative examples. Evolution of the steering ellipsoid corresponding to joint evolution of the qubit and field under the Jaynes-Cummins hamiltonian is also studied.
Tort Law, Corrective Justice and the Problem of Autonomous-Machine-Caused Harm

Developments in artificial intelligence and robotics promise increased interaction between humans and autonomous machines, presenting novel risks of accidental harm to individuals and property. 1 This essay situates the problem of autonomous-machine-caused harm within the doctrinal and theoretical framework of tort law, conceived of as a practice of corrective justice. The possibility of autonomous-machine-caused harm generates fresh doctrinal and theoretical issues for assigning tort liability. Due to machine-learning capabilities, harmful effects of autonomous machines may be untraceable to tortious actions of designers, manufacturers or users. 2 As a result, traditional tort doctrine—framed by conditions of foreseeability and proximate causation—would not ground liability. 3 Without recourse to compensation, faultless victims bear the accident costs of autonomous machines. This doctrinal outcome reflects possible incompatibility between tort’s theoretical structure of corrective justice and accidents involving autonomous machines. As a practice of corrective justice, tort liability draws a normative link between particular defendants and plaintiffs, as doers and sufferers of the same tortious harm, grounding defendants’ agent-specific obligations to repair the harm. Where accidents are caused by autonomous machines, the argument goes, the essential link between defendants and plaintiffs is severed; since resulting harm is not legally attributable to the human agency of designers, manufacturers or users, victims have no remedy in tort.
Specification testing of agent-based simulation using property-based testing

Abstract The importance of Agent-Based Simulation (ABS) as scientific method to generate data for scientific models in general and for informed policy decisions in particular has been widely recognised. However, the important technique of code testing of implementations like unit testing has not generated much research interested so far. As a possible solution, in previous work we have explored the conceptual use of property-based testing . In this code testing method, model specifications and invariants are expressed directly in code and tested through automated and randomised test data generation. This paper expands on our previous work and explores how to use property-based testing on a technical level to encode and test specifications of ABS. As use case the simple agent-based SIR model is used, where it is shown how to test agent behaviour, transition probabilities and model invariants. The outcome are specifications expressed directly in code, which relate whole classes of random input to expected classes of output. During test execution, random test data is generated automatically, potentially covering the equivalent of thousands of unit tests, run within seconds on modern hardware. This makes property-based testing in the context of ABS strictly more powerful than unit testing, as it is a much more natural fit due to its stochastic nature.
Flex

Most of data centers are significantly underutilized. One of the major reasons is the big gaps between the real usage and the provisioned resources. In this paper, we first conduct an in-depth analysis of a Google cluster trace to unveil the root causes for low utilization and highlight the great potential to improve it. We then developed an online resource manager Flex to maximize the cluster utilization while satisfying the Quality of Service (QoS). Large-scale evaluations show that Flex admits up to 1.74× more requests and 1.6× higher utilization compared to tradition schedulers while maintaining the QoS. The full version of this paper is [4].