Reviewerly/exHarmony huggingface dataset:
RMID: A Novel and Efficient Image Descriptor for Mammogram Mass Classification

For mammogram image analysis, feature extraction is the most crucial step when machine learning techniques are applied. In this paper, we propose RMID (Radon-based Multi-resolution Image Descriptor), a novel image descriptor for mammogram mass classification, which perform efficiently without any clinical information. For the present experimental framework, we found that, in terms of area under the ROC curve (AUC), the proposed RMID outperforms, upto some extent, previous reported experiments using histogram based hand-crafted methods, namely Histogram of Oriented Gradient (HOG) and Histogram of Gradient Divergence (HGD) and also Convolution Neural Network (CNN). We also found that the highest AUC value (0.986) is obtained when using only the carniocaudal (CC) view compared to when using only the mediolateral oblique (MLO) (0.738) or combining both views (0.838). These results thus proves the effectiveness of CC view over MLO for better mammogram mass classification.
Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings

Daisuke Oba, Naoki Yoshinaga, Shoetsu Sato, Satoshi Akasaki, Masashi Toyoda. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Development of Flexible Autonomous Car System Using Machine Learning and Blockchain

Autonomous Driving car is an upcoming technology. In our project, we are taking a step towards this vision by developing a system using Raspberry Pi, image processing and machine learning and connect the system to any electric car. The proposed system provides an autonomous car feature to any existing electric car on the road that doesn’t have autonomous driving feature inbuilt within it. Most existing electric cars that are on roads don’t have this technology and this is mostly found in new and expensive cars. An alarming fact about autonomous cars is that many of them are being frequently hacked, indicating a problem related to security. The application of blockchain network, which seems to provide security and transparency in the usage of the network is employed to transfer data. Using the proposed system, such autonomous car feature can be installed separately at a cheaper expense in all existing electric cars. We aim to achieve the above by using image processing which is trained by using neural networks to create a model through which autonomous cars are achieved. With the usage of blockchain network, security and transparency of data transfer can be achieved. The hardware components used in this project are Raspberry PI 3 B microcomputer and camera module. This Raspberry Pi and camera unit forms a separate system which, when connected to the electronic control unit, helps the car to drive automatically.
Authorship Attribution of The Golden Lotus Based on Text Classification Methods

In this paper, we explore the authorship attribution of The Golden Lotus using the traditional machine learning method of text classification. There are four candidate authors: Shizhen Wang, Wei Xu, Kaixian Li and Zhideng Wang. We choose The Golden Lotus's poems and four candidate authors' poems as data set. According to the characteristics of Chinese ancient poem, we choose Chinese character, rhyme, genre and overlapped word as features. We use six supervised machine learning algorithms, including Logistic Regression, Random Forests, Decision Tree and Naive Bayes, SVM and KNN classifiers respectively for text binary classification and multi-classification. According to two experiments results, the style of writing of Wei Xu's poems is the most similar to that of The Golden Lotus. It is proved that among four authors, Wei Xu most likely be the author of The Golden Lotus.
Improving the Otsu method for MRA image vessel extraction via resampling and ensemble learning

Accurate extraction of vessels plays an important role in assisting diagnosis, treatment, and surgical planning. The Otsu method has been used for extracting vessels in medical images. However, blood vessels in magnetic resonance angiography (MRA) image are considered as a sparse distribution. Pixels on vessels in MRA image are considered as an imbalanced data in classification of vessels and non-vessel tissues. To extract vessels accurately, a novel method using resampling technique and ensemble learning is proposed for solving the imbalanced classification problem. Each pixel is sampled multiple times through multiple local patches within the image. Then, vessel or non-vessel tissue is determined by the ensemble voting mechanism via a p-tile algorithm. Experimental results show that the proposed method is able to outperform the traditional Otsu method by extracting vessels in MRA images more accurately.
Heuristic D* Algorithm Based on Particle Swarm Optimization for Path Planning of Two-Link Robot Arm in Dynamic Environment

Finding a path solution in a dynamic environment represents a challenge for the robotics researchers, furthermore, it is the main issue for autonomous robots and manipulators since nowadays the world is looking forward to this challenge. The collision free path for robot in an environment with moving obstacles such as different objects, humans, animals or other robots is considered as an actual problem that needs to be solved. In addition, the local minima and sharp edges are the most common problems in all path planning algorithms. The main objective of this work is to overcome these problems by demonstrating the robot path planning and obstacle avoidance using D star (D*) algorithm based on Particle Swarm Optimization (PSO) technique. Moreover, this work focuses on computational part of motion planning in completely changing dynamic environment at every motion sample domains. Since the environment type that discussed here is a known dynamic environment, the solution approach can be off-line. The main advantage of the off-line planning is that a global optimal path solution is always obtained, which is able to overcome all the difficulties caused by the dynamic behavior of the obstacles. A mixing approach of robot path planning using the heuristic method D* algorithm based on optimization technique is used. The heuristic D* method is chosen for finding the shortest path. Furthermore, to insure the path length optimality and for enhancing the final path, PSO technique has been utilized. The robot type has been used here is the two-link robot arm which represents a more difficult case than the mobile robot. Simulation results are given to show the effectiveness of the proposed method which clearly shows a completely safe and short path.
The educational impact of using mobile technology in a database course in college

Purpose Nowadays, when mobile devices are actively evolving and penetrating the various spheres of human activity they have a potential to modernize and facilitate the sphere of IT education as well. This paper aims to test the hypothesis stating that introduction of mobile technology to the college-level database course for future IT specialists has a positive academic impact on students. Design/methodology/approach The main theoretical premises for the present study were the general findings of the surveys by Hinze et al. (2017) and Bowen and Pistilli (2012) indicating that: using mobile tools to work on classroom projects improves student’s academic success students, especially those from computing specialties, actively turn to mobile tools in their academic activity students favor the Android and iPhone platforms and prefer native mobile apps to Web-based mobile apps The key idea of the present study was to validate these general premises in the specific case of the college-level database class. Namely, the authors formulated a hypothesis that Android-based mobile devices have a positive impact on the IT students’ academic performance and interest in the subject at the college-level introductory database lessons. Throughout the study, the authors examined both quantitative (students’ scores and attendance rate) and qualitative aspects (students’ and teachers’ opinions) of the hypothesis. Findings Based on the experiment’s positive outcome resulting in students’ increased academic performance and interest in the subject, the authors conclude that in the context of the college-level database course mobile devices can successfully substitute traditional desktop computers and positively affect students’ academic activity. Originality/value Based on the experiment’s positive outcome resulting in students’ increased academic performance and interest in the subject, the authors conclude that in the context of the college-level database course mobile devices can successfully substitute traditional desktop computers and positively affect students’ academic activity.
An Algorithm for Swarm Robot to Avoid Multiple Dynamic Obstacles and to Catch the Moving Target

This paper presents a method for swarm robot to catch the moving target and to avoid multiple dynamic obstacles in the unknown environment. An imaginary map is built, including the highest mountain, some small hills, and a lowest lying land, respectively corresponding to the starting position of the robot, the detected obstacles, and the target. The robot is considered as a flow of water flowing from high to low. The flow of water is the robot trajectory that is divided into a set of points created by an algorithm called Self-organizing migrating algorithm. Simulation results are also presented to show that the obstacle avoidance and catching target task can be reached using this method.
Algebraic and Topological Semantics for Inquisitive Logic via Choice-Free Duality

We introduce new algebraic and topological semantics for inquisitive logic. The algebraic semantics is based on special Heyting algebras, which we call inquisitive algebras, with propositional valuations ranging over only the $$\lnot \lnot $$-fixpoints of the algebra. We show how inquisitive algebras arise from Boolean algebras: for a given Boolean algebra B, we define its inquisitive extension H(B) and prove that H(B) is the unique inquisitive algebra having B as its algebra of $$\lnot \lnot $$-fixpoints. We also show that inquisitive algebras determine Medvedev’s logic of finite problems. In addition to the algebraic characterization of H(B), we give a topological characterization of H(B) in terms of the recently introduced choice-free duality for Boolean algebras using so-called upper Vietoris spaces (UV-spaces) [2]. In particular, while a Boolean algebra B is realized as the Boolean algebra of compact regular open elements of a UV-space dual to B, we show that H(B) is realized as the algebra of compact open elements of this space. This connection yields a new topological semantics for inquisitive logic.
Web 2.0 use in academic libraries of top ranked Asian universities

Purpose This paper aims to explore recent trends of how Web 2.0 applications were used in 75 academic libraries in Asia through their library websites. Design/methodology/approach The Times Higher Education Asia University Rankings 2016 was considered for this study and out of 200 top universities ranked, 75 universities were selected for data collection. Using a multi-method approach, this study evaluated key design elements, library service platforms and website content of each academic library website, examining their site features, Web 2.0 types and applications. The criteria for selecting the websites were first the website was in English and second had Web 2.0 applications integrated into the main website. For the ranking of websites, a library web service index was developed, benchmarking from these groups – resource discovery tools, Web 2.0 applications, e-resources, mobile applications, library guides, digital reference services and digital inclusion – as indicators. Findings The authors found that over two-thirds of Asian university libraries have deployed one or more Web 2.0 applications, though their popularity and implementation vary greatly. Most widely used Web 2.0 applications are Facebook (61.3 per cent), RSS (53.3 per cent), Twitter (46.7 per cent) and YouTube (37.3 per cent). Instant messaging (5.3 per cent) and podcasting (4 per cent) were least applied. With an average of 44 per cent, the diffusion rate of Web information is moderately high among the majority of the Asian university libraries. Originality/value Many studies explored Web 2.0 applications from developed countries. However, this study attempts evaluating the use of Web 2.0 applications through content, sites and features of academic libraries in Asia, from developing countries perspective.
Forensic analysis of Sync.com and FlipDrive cloud applications on Android platform

The complex architecture and legal restrictions associated with cloud services make the acquisition of data from servers almost impossible in digital investigations involving cloud services. However, smartphones used to access these cloud services can serve as potential sources. In this paper, we investigate Sync.com and FlipDrive cloud client applications on Android platform for artefacts left behind by user activities. Our experiments demonstrate that rich information arising from user activities is left behind by these applications. This information includes installation details, login credentials, names and timestamps of files uploaded, downloaded, deleted and shared. The study also identifies mechanisms for extracting these artefacts from the devices. These findings assist forensic investigators in performing complete, credible and conclusive digital investigation by allowing them to create complete file management history of these applications. Finally, based on these findings, we make many recommendations relevant to the digital investigation involving these applications.
A Graphene-Based Sleep Mask for Comfortable Wearable Eye Tracking

We present a new wearable electrooculogram (EOG) monitor for measuring eye movements. We fabricated conductive and flexible graphene-based textiles from nylon to use as a sensing electrode, which we then integrated into a commercially available eye mask held in place only with the standard elastic strap. We tested this mask on 4 participants to quantify the noise floor and show that we can detect eye blinks to a high SNR of over 16 dB. We also identify that the material can detect other eye movements in cases when the noise floor is low. As our system is held in place with only an elastic strap it offers the same level of comfort as when wearing a normal eye mask. Our sensors offer an increased level of comfort over conventional gelled electrodes traditionally used in EOG monitoring and may be of use for comfortable eye movement experiments. This is particularly important during sleep studies where the EOG is routinely monitored, but using bulky instrumentation.
ECM-IBS: A Chebyshev Map-Based Broadcast Authentication for Wireless Sensor Networks

As a basic and crucial security requirement for Wireless Sensor Networks (WSNs), authentication is generally used to prevent various communication attacks such as Denial-of-Service (DoS) attack. A novel broadcast authentication framework is proposed in this paper, where an Identity-Based Signature scheme by using the Extended Chaotic Maps (ECM-IBS) is designed to authenticate all broadcast messages and specifically, a chaos-based hash function is used for message authentication in the WSNs. It is implemented using a WSN hardware device of CC2530 and its performance is analyzed under various methods. Performance analysis and experimental results show that the proposed ECM-IBS scheme has a quick signature generation speed, low energy consumption and short verification delay, and can be applied to WSN applications.
Hennessy-Milner Properties for (Modal) Bi-intuitionistic Logic

Bi-intuitionistic logic is an extension of intuitionistic propositional logic with a binary operator that is residuated with respect to disjunction. Our main result is a Hennessy-Milner property for bi-intuitionistic logic interpreted over certain classes of Kripke models. We generalise this to obtain a corresponding result for modal bi-intuitionistic logic. Our main technical tools are a categorical duality between (modal) descriptive Kripke frames and (modal) bi-Heyting algebras, and the use of behavioural equivalence.
Immersive Learning in the Wild: A Progress Report

Immersive technologies have entered the mainstream. To establish them firmly in educational curricula requires both practical and empirical assessments that ultimately lead to best practice and design recommendations. We report on a study that contributes to both. To enrich geoscience education, we developed an immersive virtual field trip (iVFT) that we evaluated in previous small-scale studies. In order to make it accessible to larger audiences we (a) developed a version of the iVFT for mobile devices (Oculus Go); and (b) used an evolving public VR infrastructure at The Pennsylvania State University. The results of an empirical evaluation are insightful in that they show that system characteristics are only partially predicting learning experiences and that required mainstream adoption, that is, making immersive experiences mandatory for all students in a class, still has its challenges. We discuss the results and future developments.
Models of decision support in socio-cyber-physical systems

Introduction: Socio-cyber-physical systems are complex non-linear systems. Such systems display emergent properties. Involvement of humans, as a part of these systems, in the decision-making process contributes to overcoming the consequences of the emergent system behavior, since people can use their experience and intuition, not just the programmed rules and procedures. Purpose: Development of models for decision support in socio-cyber-physical systems. Results: A scheme of decision making in socio-cyber-physical systems, a conceptual framework of decision support in these systems, and stepwise decision support models have been developed. The decision-making scheme is that cybernetic components make their decisions first, and if they cannot do this, they ask humans for help. The stepwise models support the decisions made by components of socio-cyber-physical systems at the conventional stages of the decision-making process: situation awareness, problem identification, development of alternatives, choice of a preferred alternative, and decision implementation. The application of the developed models is illustrated through a scenario for planning the execution of a common task for robots. Practical relevance: The developed models enable you to design plans on solving tasks common for system components or on achievement of common goals, and to implement these plans. The models contribute to overcoming the consequences of the emergent behavior of socio-cyber-physical systems, and to the research on machine learning and mobile robot control.
An Exhaustive Review on Security Issues in Cloud Computing

The Cloud Computing is growing rapidly in the current IT industry.Cloud computing has become a buzzword in relation to Grid & Utility computing.It provides on demand services to customers and customers will pay for what they get.Various "Cloud Service Provider" such as Microsoft Azure, Google Web Services etc. enables the users to access the cloud in cost effective manner.However, security, privacy and integrity of data is a major concern.In this paper various security challenges have been identified and the survey briefs the comprehensive overview of various security issues in cloud computing.The classification of security issues in cloud computing have been studied.In this paper we have discussed security challenges in cloud computing and also list recommended methods available for addressing them in the literature.
Product line architecture recovery with outlier filtering in software families: the Apo-Games case study

Software product line (SPL) approach has been widely adopted to achieve systematic reuse in families of software products. Despite its benefits, developing an SPL from scratch requires high up-front investment. Because of that, organizations commonly create product variants with opportunistic reuse approaches (e.g., copy-and-paste or clone-and-own). However, maintenance and evolution of a large number of product variants is a challenging task. In this context, a family of products developed opportunistically is a good starting point to adopt SPLs, known as extractive approach for SPL adoption. One of the initial phases of the extractive approach is the recovery and definition of a product line architecture (PLA) based on existing software variants, to support variant derivation and also to allow the customization according to customers’ needs. The problem of defining a PLA from existing system variants is that some variants can become highly unrelated to their predecessors, known as outlier variants. The inclusion of outlier variants in the PLA recovery leads to additional effort and noise in the common structure and complicates architectural decisions. In this work, we present an automatic approach to identify and filter outlier variants during the recovery and definition of PLAs. Our approach identifies the minimum subset of cross-product architectural information for an effective PLA recovery. To evaluate our approach, we focus on real-world variants of the Apo-Games family. We recover a PLA taking as input 34 Apo-Game variants developed by using opportunistic reuse. The results provided evidence that our automatic approach is able to identify and filter outlier variants, allowing to eliminate exclusive packages and classes without removing the whole variant. We consider that the recovered PLA can help domain experts to take informed decisions to support SPL adoption.
A Categorization of Interoperability Issues in Networks of Transformations.

Bidirectional transformations (BX) are a common approach for keeping two types of models consistent, but consistency preservation between more than two types of models is not researched well.One solution is the composition of BX to networks of transformations.Nevertheless, such networks are prone to failures due to interoperability issues between the individual BX, which are independently developed by various experts.We therefore systematically identify and categorize such issues.First, we structure the process of consistency specification into different conceptual levels.Then we develop a catalog of potential mistakes, which we derive from those levels, and consequential failure types.Finally, we discuss strategies to avoid mistakes at the different levels.This catalog is beneficial for transformation developers and transformation language developers.It improves awareness in developers of potential mistakes and consequential failures, enables the development of techniques to avoid specific mistakes by construction, and eases the identification of reasons for failures.