Reviewerly/exHarmony huggingface dataset:
Human Tumor Detection Using Active Contour and Region Growing Segmentation

Segmentation and detection of borders are one of the most important task in image processing. The segmented images are divided into a different range of areas. In many image processing programs, this is an intermediate step, such as content-based image and video recovery, object detection and tracking, biomedical engineering, and many others. The segmentation of human brain tumors from data sets of magnetic resonance imaging (MRI) is of first-rate importance for advanced analysis, prediction of rate increases and remedies. However, automating this method is hard because of the existence of extreme partial quantity effect and high-quality variability in tumor structures. In this work, we introduced the area of developing segmentation and active contour model for the automatic segmentation of the human brain tumor from multimodal volumetric MRI images. Using active contour and region growing segmentation, we progressed the accuracy and robustness to detect more than one brain tumor. With the accurate place and shape approximation within the initial degree, the tumor is effectively detected with the aid of using active contour. The correctness and robustness of the proposed algorithm are verified on extracting the tumor areas of MRI. Compared with the tumor outlines, using the outlines based on the proposed set of rules can achieve sensitivity values of up to 98.98%. The proposed algorithm presents an easy and accurate approach for tumor segmentation in MRI images.
Forward Privacy Analysis of a Dynamic Searchable Encryption Scheme

Dynamic searchable encryption is one branch of searchable encryption. Compared with the static searchable encryption, a dynamic searchable encryption scheme can support update (add or delete) of outsourced data. However, this kind of operation may cause data leakage. Forward privacy is an important character for dynamic scheme to limit leakage of inserted document. It requests that a previous search token cannot be linked to later inserted document. In this paper, we demonstrate that a dynamic searchable symmetric encryption scheme proposed recently does not satisfy the forward privacy. It means that the cloud server can realize whether or not a newly added document contains any of the keywords used in previous searches.
A novel utility metric to measure information loss for generalization and suppression techniques in Privacy Preserving Data publishing

Privacy has become a prime importance in this digital era. Personally Identifiable Information (PII) gets collected in various firms such as educational institutions, government organizations, hospitals etc‥ The collected data is often published and utilized for the purpose of analysis, research, decision making, advertisement or for the purpose of business. It is the duty of the data curator to store and to publish the data safely. A person who is having accessibility to the data that is published must not be able to identify or learn any new personal or sensitive information of an individual. Statistical Disclosure Control(SDC) is a suite of anonymization techniques. The aim of SDC is post processing the data containing sensitive or personal information and effectively protect the privacy of the participating data subject. However these techniques leads to “information loss”, i.e., any analysis or research carried on such a data might not give a appropriate result. This paper aims to discuss various metrics available to assess the information loss in the processed data and an attempt has been made to propose our technique for measuring the loss.
Of Seamlessness and Frictions: Transborder Data Flows of European and US Social Science Data

Open science initiatives are predicated upon managing research data to overcome “data frictions,” or the points of resistance in the movement of data time [12]. This paper explores organizational creation of data frictions to manage the flow of data from one data organization to another. We describe the creation and modification of data frictions between European data organizations and between data organizations in Europe and the USA. We analyze historical documentary data from CESSDA, an umbrella organization representing European data organizations that has served as a platform for development of international data sharing arrangements from the 1960s through today.
Multidisciplinary Development Process of a Story-based Mobile Augmented Reality Game for Learning Math

Despite the high number of educational games released, only a few games have a strong story that is more than an excuse for players’ actions. Furthermore, even fewer story-based games utilise the affordances of augmented reality (AR) to concretise abstract concepts while engaging players.Based on our literature review, we were inspired to merge AR into a story-based educational mobile game for teaching fractions to elementary school students. The game Tales & Fractions was created through a two-phase multidisciplinary development process. In order to successfully integrate AR into a story-based educational game, we employed an adapted version of the Scrum agile software development method implemented by a multidisciplinary team of experts from computer science, pedagogy, design and arts. During the development process, we faced many issues that other story-based AR game developers could meet. We summarised the encountered issues with our solutions which could be useful for developers to avoid common pitfalls and to enrich the user engagement.
Architecture and Conceptual Formalization of Cyber-Mission Operations

Nowadays, military cyber-missions are increasingly complex, demanding specific and critical socio-technical operational resources. We propose a conceptual proposal of a system architecture aimed at supporting complex operations of cyber-missions. The architecture allows the systematic selecting and prioritizing socio-technical resources for cyber-missions in a systematic manner. The foundations of the proposed architecture are based on a mission operations ontology; it was developed for formalizing the main concepts. The main contributions of the work are: (i) Conceptual Architecture to supporting complex cyber-mission operations; (ii) Lightweight ontology to make explicit its main concepts; and, (iii) an Application Scenario of the system architecture. Our proposal is meant to be useful for researchers, developers and actuators who deal with socio-technical systems in cyber-mission operations.
EM-based Extended Target Tracking with Automotive Radar using Learned Spatial Distribution Models

This paper presents a novel interpretation of data driven extended target tracking with applications to the automotive sector. Specifically, learning the spatial distribution of measurements from a vehicle in the form of a Variational Gaussian Mixture (VGM) model is examined. This distribution yields an interpretation applicable for the Expectation Maximization (EM) algorithm such that a closed-form measurement update for tracking an extended target can be derived. The approach is in particular designed for sparse and noisy measurements and is applied to Radio Detection and Ranging (RADAR) point information. Furthermore, an evaluation based on data from the recent nuScenes data set is performed.
Mongodb on Cloud for Weather Data (Temperature and Humidity) in Sittway

The environmental conditions play the major effects on human beings and the weather parameters are very important roles in our daily life. Many research efforts have paid to solve the environmental problems. So the collecting of data about different parameters of the weather is necessary for planning in home and environment, and the database of weather parameters become more important for living things. In this work, two weather parameters: temperature and relative humidity in Sittway township, Rakhine state have been measured by Raspberry Pi 4 with DHT22 sensor for solving the environmental problems. The collected data from the system have been stored and transmitted to the cloud by MONGODB with Java Script programming language and then the comparison of the data collected from the sensor and the Mongo database on cloud has been made for the accuracy of the project.
User research enabled by makerspaces: bringing functionality to classical experience prototypes

Abstract This paper sheds light on the new possibilities for user research activities facilitated by access to makerspaces. We present four case studies of user research conducted in two university-based makerspaces as examples of makerspace-driven user research. Further, by comparing the cases to three classical user research activities, namely observation, prototyping, and user journey mapping, we highlight the main aspects of this new context of user research. We find that accessibility to makerspaces enables user researchers to build low-fidelity yet high-functionality prototypes for exploring users’ preferences and motivations in controlled and repeatable ways. These prototypes fall into the category of experience prototypes, but they have greater functionality than the prototypes previously used in this field. Thus, a user researcher can explore a topic more systematically and in a more hypothesis-driven manner. In summary, this study encourages stakeholders in the early stages of product development to consider a makerspace as a resource for user-related requirement elicitation rather than for only specific product iteration.
Privacy-preserving SVM on Outsourced Genomic Data via Secure Multi-party Computation

Machine learning methods are employed in many areas, such as medical data research, for their efficient and powerful data mining ability. However, submitting unprotected data to a third party, which attempts to train a machine learning model, may suffer from data leakage and privacy violation when the third party is compromised by an adversary. Hence, designing a protocol to execute encrypted computation is inevitably indispensable. In order to address this problem, we propose protocols based on secure multi-party computation to train a support vector machine model privately. Utilizing the semi-honest adversary model and oblivious transfer, the proposed protocols enable the training of a non-linear support vector machine on the combined data from various sources without sacrificing the privacy of individuals. The protocols are applied to train a support vector machine model with the radial basis function kernel on HIV sequence data to predict the efficacy of a certain antiviral drug, which only works if the viruses can only use the human CCR5 coreceptor for cell entry. Benchmarked on synthesized data with 10 data sources that consist of randomly generated integers, containing 100 labeled samples each, the protocol has consumed online time 2991.386/166.912 ms on average in arithmetic/boolean circuits, respectively. The cross-validation has reached 0.5819 F1-score on average on training data with the optimized parameters, which have reached 0.7058 F1-score afterwards on testing data set, which consists of protein sequence of CCR5 and its subtypes. The complete training and testing process on the real data, which contains in total 766 samples having 924 features after encoding, has consumed 43.75/15.84 seconds on average using arithmetic/boolean circuits, respectively, which shows the effectiveness and efficiency of our protocols compared to some of the existing studies in the literature.
Detection and Tracking of Fingertips for Geometric Transformation of Objects in Virtual Environment

This paper presents an approach of two-stage convolutional neural network (CNN) for detection of fingertips so that an interaction of the fingertips with a 3D object in the virtual environment (VR) can be established. The first-stage CNN is assigned to detect and locate the hand. Subsequently, the detected hand is cropped, resized, and fed to the second stage CNN for predicting the coordinates of fingertips. Next, a tracker is employed to track the hand continuously so that the system becomes reliable in real-time performance. The VR environments are designed to demonstrate the performance of the fingertip-based interaction system. The proposed method focuses on the geometric transformation of a virtual 3D object by using the gesture of the thumb and index finger. In particular, the distance of the thumb and index fingertips is employed to scale a 3D object in virtual environment. To realize the system, a dataset of 1000 images, named, Thumb Index 1000 (TI1K) dataset, is developed including those variations that are commonly-seen in real-life thumb and index fingers. The system is evaluated with the aid of a number of participants and virtual objects that are distinctive in nature. The proposed approach attains the desired goal and performs in real-time seamlessly to facilitate the human-computer interaction in the VR environment.
Uncalibrated Non-Rigid Factorisation by Independent Subspace Analysis

We propose a general, prior-free approach for the uncalibrated non-rigid structure-from-motion problem for modelling and analysis of non-rigid objects such as human faces. We recover the non-rigid affine structure and motion from 2D point correspondences by assuming that (1) the non-rigid shapes are generated by a linear combination of rigid 3D basis shapes, (2) that the non-rigid shapes are affine in nature, i.e., they can be modelled as deviations from the mean, rigid shape, (3) and that the basis shapes are statistically independent. In contrast to the majority of existing works, no statistical prior is assumed for the structure and motion apart from the assumption that underlying basis shapes are statistically independent. The independent 3D shape bases are recovered by independent subspace analysis (ISA). Likewise, in contrast to the most previous approaches, no calibration information is assumed for affine cameras; the reconstruction is solved up to a global affine ambiguity that makes our approach simple and efficient. In the experiments, we evaluated the method with several standard data sets including a real face expression data set of 7200 faces with 2D point correspondences and unknown 3D structure and motion for which we obtained promising results.
A Master-Slave Chain Architecture Model for Cross-Domain Trusted and Authentication of Power Services

With the gradual complexity of China's electricity consumption information, the current power business is diversified, and multi-service integration has increasingly become the direction of power business development. However, the problem of converged business trust and mutual trust has not been effectively solved, and it will bring huge economic losses to the power grid. Therefore, while effectively isolating multiple services, how to ensure multi-service integration and credibility is an urgent security issue. As a decentralized distributed storage peer-to-peer trusted network, blockchain is highly transparent, decentralized, trusted, unchangeable, and anonymous. This paper introduces a master-slave chain architecture based on blockchain for cross-domain trusted authentication of power services. It uses the slave chain to isolate multiple services. The backbone ensures the trust of the business and minimizes the security risk of untrustworthiness.
The new teaching method using virtual reality technology in building technology subject

Abstract One of the challenges for educators in the future is that educators are faced with students with high technological mastery abilities. Advances in the business and entertainment sectors drive the evolution of computer equipment with increasingly sophisticated technology that can make things easier. The development of equipment that supports virtual reality technology is also increasingly modern and realistic, so that the use and the utilization of virtual reality technology can also be widened in various disciplines. Researchers and educators also start competing to explore their potential, develop pedagogical strategies, create teaching materials, and build technology-based and educational infrastructure to find learning systems that are more likely by students and can present high-quality, easy and fun education. In this study, it will be reviewed how the role of Virtual reality technology can help students in understanding the science of building construction. Through combination the use of tools such as VR Gear, 360 cameras and digital computer design, it is expected that various methods that are appropriate, convenient and easy for students.
ECAD Prototype Design for Engineering Students Training

This paper analyzes Computer-Aided Design systems used by students for studying circuit design in technical universities. The analysis identifies and describes the main requirements for the software enabling students to solve the tasks set before them. The main pros and cons of the existing circuit design software solutions are presented. A prototype of a web application DreamSim was developed. The features of the DreamSim allow creating and simulating circuits of complexity and size encountered in the circuit design courses in curriculums of technical universities.
A Modified Method for Brain MRI Segmentation Using Dempster-Shafer Theory

In any kind of medical imaging, segmentation plays a crucial part. To detect brain tissues or tumors, brain MRI segmentation is the safest way. But brain MRI segmentation is not an easy task because of various kinds of artifacts such as intensity non-uniformity, partial volume effects, and noise. Brain MRI artifacts lead to uncertainty in pixel values of the different parts of MRI modalities, which include T1- and T2-weighted, Proton density (PD) and Flair images. In this paper, the proposed method has followed two main steps. Firstly, Brain MRI is divided into some clusters using Fuzzy c-mean (FCM) then Fuzzy clustering and specific mapping were used to form the Dempster-Shafer belief structure. The purpose of this research is to combine Dempster- Shafer theory and fuzzy clustering which gives a technique based on data fusion of different modalities by considering spatial neighborhood information. The results of the proposed method have been evaluated by using Dice and Jaccard coefficients which prove its superiority over other benchmark methods.
Personal Authentication with Eye Movement Features During PIN Input

In recent years, personal authentication is used in various situations. Among them, the personal authentication using biometric information is becoming widespread. Conventional biometrics have a spoofing problem. Personal authentication using eye movement is considered to be more difficult to spoof. Biometrics research using eye movement has been conducted in the case where the eye movement range is relatively wide. In this study, we aim at development of the personal authentication method using eye movement in the case of narrow range of eye movement such as smartphone and ATM operation. In this paper, we investigated effective features of eye movement in the case where the range of eye movement is narrow. The authentication accuracy was evaluated using equal error rate (EER). In addition, we examined whether the authentication accuracy could be improved by score level fusion. From the experimental results, it was found that complex eye movement (CEM) had the best authentication accuracy when the range of eye movement was narrow, and the authentication accuracy was improved by score level fusion.
Handwritten Text Segmentation in Scribbled Document via Unsupervised Domain Adaptation

Supervised learning methods have shown promising results for the handwritten text segmentation in scribbled documents. However, many previous methods have handled the problem as a connected component analysis due to the extreme difficulty of pixel-level annotations. Although there is an approach to solve this problem by using synthetically generated data, the resultant model does not generalize well to real scribbled documents due to the domain gap between the real and synthetic dataset. To alleviate the problems, we propose an unsupervised domain adaptation strategy for the pixel-level handwritten text segmentation. This is accomplished by employing an adversarial discriminative model to align the source and target distribution in the feature space, incorporating entropy minimization loss to make the model discriminative even for the unlabeled target data. Experimental results show that the proposed method outperforms the baseline network both quantitatively and qualitatively. Specifically, the proposed adaptation strategy mitigates the domain shift problem very well, showing the improvement of baseline performance (IoU) from 64.617% to 85.642%.
Challenges Posed by Voice Interface to Child- Agent Collaborative Storytelling

Child-agent collaborative storytelling can be facilitated through text and voice interfaces. Voice interfaces are more intuitive and closely resemble the way people usually relate to one another. This may be attributed to the colloquial characteristics of everyday conversations that do away with rigid linguistic structures typically present in text interfaces, such as observing the use of correct grammar and spelling. However, the capabilities of voice-based interfaces currently available in virtual assistants can lead to failure in communication due to user frustration and confusion when the agent is not providing the needed support, possibly caused by the latter's misinterpretation of the user's input. In such situations, text-based interfaces from messaging applications may be used as an alternative communication channel. In this paper, we provide a comparative analysis of the performance of our collaborative storytelling agent in processing user input by analyzing conversation logs from voice-based interface using Google Assistant, and text-based interface using Google Firebase. To do this, we give a brief overview of the different dialogue strategies employed by our agent, and how these are manifested through the interfaces. We also identify the obstacles posed by incorrect input processing to the collaborative tasks, and offer suggestions on how these challenges can be addressed.
Task recognition based on task history considering estimation error of localization in care support with autonomous mobile wheelchairs

We propose a task recognition method for recognizing task performed by caregivers using autonomous mobile wheelchairs equipped with automatic stop and movement functions. Task locations and times are defined as a task history, and task information is based on this task history, referring to the “Spatial Memory” of Niitsuma et al. We propose a task recognition method that considers estimation error of localization at actual sites, and evaluated this method using data with simulated error added to worker's position data.
Q-Learning with Side Information in Multi-Agent Finite Games

In this paper, we propose a discrete-time Nash equilibrium-seeking reinforcement learning algorithm for n-player finite games which can be adapted to leverage side information when it is available. Under standard assumptions used in stochastic approximation theory, the induced strategies of the discrete-time process converge to a perturbed Nash equilibrium for a class of games characterized by negative monotonicity properties of its utility, which encompasses 2-player zero-sum games and concave potential games. Our convergence results are still valid when no side information is exploited. Through numerical simulations of certain representative games, we show that a faster rate of convergence to a Nash equilibrium can be achieved when more side information is available and exploited.