Reviewerly/exHarmony huggingface dataset:
Distant Supervised Relation Extraction Based On Recurrent Convolutional Piecewise Neural Network

Distant supervised relation extraction (RE) is currently an effective way to solve the problem of extracting relation from large amounts of unlabeled data.The purpose of distant supervised relation extraction is to identify the relation between the two entities marked in a sentence. However, there are two existing problems.The one is that some methods need to draw on entity description of external knowledge base to enrich entity information, but in reality not every time we have entity description of an external knowledge base. The other one is that the effects of distant supervised relation extraction are not very ideal currently.This paper proposes a novel relation extraction model based on recurrent piecewise convolutional neural network structure to solve the problems above. Firstly, based on the recurrent convolutional neural network structure, the embedding of every word in a sentence is added with context information to enrich the characteristics of the words. And then with piecewise max pooling, it captures the information throughout the entire sentence. Secondly, the semantic information of a sentence can indirectly reflect the relation of the entity.This paper employs sentence vectors to add the semantic information to improve the accuracy of the distant supervised relation extraction. The experimental results are based on real-world dataset. Our model makes full use of the information characteristics of the dataset and has great improvement on the real-world dataset.It proves that our model in this paper exceeds various baselines.
Android-based expert system design for drug selection using certainty factor

Abstract Handling some types of diseases require medical therapy in the form of drug delivery. Appropriate medication should be conducted with assistance from medical experts for doses and types of drugs can be given properly and correctly. This study aims to discuss drug selection system design using certain certainty factor method with android-based. The disease that used as a discussion object is diabetes mellitus disease. System design methodology uses UML (Unified Modelling Language) modelling approach. The design results show that the system can make an early diagnosis of diabetes mellitus type II and drug selection recommendation for diabetes mellitus type II. Since health is essential to the safety of the human psyche, this application system can only be used as the third opinion of the diagnosis of diabetes mellitus type II.
Is it better to choose seen or unseen distracters for graphical passwords

We investigate a form of recognition-based graphical passwords where users choose their pass images from a system provided collection. In these systems the user is presented with a challenge set containing their pass images, together with distracters, when they login. They need to recognise their pass images to gain access.
Programming the Tip of the Iceberg: Software Reuse in the 21st Century

Opportunistic design - an approach in which people develop new software systems by routinely reusing and combining components that were not designed to be used together - has recently become very popular. This emergent pattern places focus on large scale reuse and developer convenience, with the developers "trawling" for most suitable open source components and modules online. The availability of open source assets for almost all imaginable domains has led to software systems in which the visible application code - written by the application developers themselves - forms only the "tip of the iceberg" compared to the reused bulk that remains mostly unknown to the developers. The actual reuse takes place in a rather ad hoc, mix-and-match fashion. In this paper, we take a look at this emerging approach and argue that challenges associated with such development model are quite different from traditional software development.
Automated Voice Recognition of Emotions Through the Use of Neural Networks

The paper describes the development of a software product, based on the architecture of the neural network, which allows to handle record telephone conversations and identify their emotional color. The approaches to the training of the neural network. Based on neural network developed a software product that allows you to assess the emotional state of the negotiators when contacting the emergency services to the quality of more than 60%, which was successfully tested. Carried out the integration of the software with other informational systems specified services.
Necessary Information to Know to Solve Class Imbalance Problem: From a User’s Perspective

In many real-world applications, class imbalance problem is the most attentive (also a major challenging) problem for machine learning (ML). The traditional classification algorithms assume evenly distributed in the underlying training set. In class imbalanced classification, the training set for one class called (majority class) far exceed the training set of the other class called (minority class), in which, the more often interesting class is minority class. We need to increase minority class samples than majority class samples in analysing of a datasets (related to an application). This is a hot problem in the past several decades. This article tries to provide as much as information to know or work about class imbalance problem with a detail description (from a user’s perspective). For this, we include several articles from a reputed publication like IEEE, ACM, Elsevier, Wiley, etc. Hence, this work will help a lot to all the future researchers to find out or a summary (about their interest) with respect to this class imbalance problem (raising in several applications).
Multi-task Network Embedding with Adaptive Loss Weighting

Network embedding is to learn low-dimensional representations of nodes which mostly preserve the network topological structure. In real-world networks, however, nodes are often associated with a rich set of attributes and labels which are potentially valuable in seeking more effective vector representations. To properly utilize this information, we propose a Joint Autoencoders framework for Multi-task network Embedding (JAME), which aims to encode a shared representation of local network structure, node attributes, and available node labels. Jointly embedding via multi-task learning is strongly dependent on the relative weighting between each task's loss function. Tuning these weights by hand is an expensive and difficult process, making multi-task learning prohibitive in practice. Therefore, we define an adaptive loss weighting layer capable of learning an optimal combination of loss weights during model training. This allows to dynamically updates task weights for controlling the influence of individual tasks based on task difficulty. Empirical evaluations on three real-world datasets demonstrate effectiveness of JAME compared to the relevant baseline methods.
Graph-to-Graph Energy Minimization for Video Object Segmentation

We describe a new unsupervised video object segmentation (VOS) method based on the graph-to-graph energy minimization, which focuses on exploiting the mutual bootstrapping information between bottom-up (i.e., using pixel/superpixel attributes) and top-down (i.e., using learned appearance and motion cues) processes in a unified framework. Specifically, we construct a graph-to-graph energy function to encode the spatial similarities among superpixels (superpixel-graph) and temporal consistency among regions (region-graph). An efficient heuristic iterative algorithm is used to minimize the energy function to get the optimal assignment of superpixel and region labels to complete the VOS task. Experiments on two challenging benchmarks (i.e., SegTrack v2 and DAVIS) show that the proposed method achieves favorable performance against the state-of-the-art unsupervised VOS methods and comparable performance with the state-of-the-art semi-supervised methods.
Utilizing mobile phones in mathematics class: teachers’ and preservice teachers’ perceptions

Abstract Mobile phones are one of the technologies that can be used to support mathematics learning, but currently the use of it in the classroom seems to be rarely used by teachers in Indonesia. This study examined a total of 44 mathematics teachers and 62 mathematics preservice teachers’ perception in Indonesia for the use of mobile phones in the classroom. Participants were given a questionnaire to determine their support for mobile phones use. The results showed that majority of the participants supported the use of mobile phones for learning. They agreed that mobile phones are useful to provide learning opportunities anywhere and anytime, increase access to technology in the classroom, and provide opportunities for differentiation in instruction. As for barriers, participants worry about students’ access application that is not in accordance with the teacher’s instructions. They perceived that instructions, control of students and time allocation are the things that teacher need to pay attention before using this media. Although majority teachers reported their support for the use of mobile phone in the classroom, the implementation of this media needs to be improved.
Revisit Lmser from a Deep Learning Perspective

Proposed in 1991, Least Mean Square Error Reconstruction for self-organizing network, shortly Lmser, was a further development of the traditional auto-encoder (AE) by folding the architecture with respect to the central coding layer and thus leading to the features of Duality in Connection Weight (DCW) and Duality in Paired Neurons (DPN), as well as jointly supervised and unsupervised learning which is called Duality in Supervision Paradigm (DSP). However, its advantages were only demonstrated in a one-hidden-layer implementation due to the lack of computing resources and big data at that time. In this paper, we revisit Lmser from the perspective of deep learning, develop Lmser network based on multiple fully-connected layers, and confirm several Lmser functions with experiments on image recognition, reconstruction, association recall, and so on. Experiments demonstrate that Lmser indeed works as indicated in the original paper, and it has promising performance in various applications.
Defining a New Composite Cybersecurity Rating Scheme for SMEs in the U.K.

The 5.7 million small to medium enterprises (SMEs) in the U.K. play a vital role in the national economy, contributing 51% of the private sector. However, the cyber threats for SMEs are increasing with four in ten of businesses experiencing a cyber attack in the last twelve months. One significant treatment of this growing concern is in the implementation of long-established information security standards and best-practices. Yet, most SMEs are not undergoing the certification process, even though the current threats are now widely published by the government. In this paper, we look at the disconnect of cyber threats faced by SMEs considering their current security postures and perceptions. We also identify the influencing factors needed to improve security behaviours and engagements with information security best-practices. We then propose a new foundational composite cybersecurity rating scheme, which is aimed at SMEs in the U.K., but it also has the potential to be scaled internationally. The focus of our scheme is to ascertain and measure the security behaviours, perceptions and risk propensity of each SME, as well as their technical systems. To that end, we define our $$5\times 5$$ matrices based scheme by combining the measurements ascertained from the behavioural as well as technical audits. The preliminary evaluation results demonstrate that this approach provides a higher level of insight, engagement and accuracy as to an SME’s individual security posture.
A Data-driven Clinical Decision Support System for Acute Coronary Syndrome Patient Similarity

Both data-driven clinical decision support system (CDSS) and patient similarity are important topics in healthcare informatics to facilitate precision medicine. However, approaches to combine both are less studied. In this study, we proposed a framework of data-driven CDSS for patient similarity and presented a case study describing a prototype system for acute coronary syndrome patients.
A Content Based e-Commerce Recommendation Approach Under the Veristic Framework

A recommendation system is an automated tool that suggests an ordered list of appropriate items to a user. In this paper, we propose a recommendation algorithm that takes into account the variable and complex semantics of multi-valued properties, and the level of uncertainty or fuzziness inherent in the representations of users and items. In particular, we will rely on the concept of veristic variables, proposed by Yager, and propose algorithms for building user profiles, and for matching product descriptions to those profiles. We tested our algorithms on the Movielens datasets. Our results show statistically significant improvements in the f1-score and the Root Mean Square Error (RMSE) compared to baselines like Bayesian and K-nearest neighbors (KNN) approaches.
A Novel Approach for Efficient Implementation of Nucleus Detection and Segmentation Using Correlated Dual Color Space

This paper presents a novel algorithm that achieves high performance in leukocytes detection and segmentation in microscopic blood sample images. To the best of our knowledge, this algorithm is the first that exploits the correlation between two CMYK and RGB color models. Our proposed algorithm uses the upper and lower limits of the pixel intensity in the Magenta component from the CMYK color space to adjust the image contrast in the Green component of the RGB image to segment the nuclei without any pre-processing steps. The proposed algorithm was tested using a series of 30 blood sample images with different color tones, image resolutions, magnifications, and surrounding conditions. The segmented nuclei are virtually congruent to the nuclei in the original image, in term of shape and area, regardless of the type of nucleus. The overall segmentation accuracy achieved by the proposed algorithm is 98.99%, with a consistent performance for the same image resolution regardless of the number of nucleus in the blood smear image. Our algorithm can be seen as a stepping stone for efficient implementations of nucleus classification to identify and classify the type of leukocyte for accurate medical diagnosis.
Web-data Driven Ontological Approach to Modelling IT Specialists Recruitment Needs

The study describes an approach to creating a web-data driven ontological model of software developer professional skills. The authors show the necessity of such a model for the analysis of IT specialists recruitment needs in country’s economy. Several existing approaches to the ontological modeling of professional competencies are identified with their strengths and weaknesses described. The core algorithm of data extraction from vacancy texts and their structuring using denotation graph is presented. The researchers report a simplified ontological portrait of a software developer. The portrait is created using data from vacancies texts published on job aggregators and domain knowledge extracted from online encyclopedias. The model creates a holistic portrait of an IT specialist based on a set of a few concepts. Concepts for further development of the approach and models are proposed.
Family wearables – what makes them persuasive?

In recent years, digital influence has become visible in many different forms, and consequently, the understanding of what is and what is not persuasive calls for further elaboration. Spring boarding from an analysis of two wearable activity trackers, this paper seeks to contribute to the discussion of characteristics of persuasive design, by arguing that although principles commonly applied in persuasive technologies are present, it may not justify that a technology is defined as persuasive. The rhetorical concept of peithenanke is introduced, in order to explain the subtle nuances of different types of behaviour design, and to support previously made arguments that transparency and ethics are fundamental qualities of persuasion, which should not be overlooked neither in theory nor in practice
Analysis of Psychology of Students by Graphical Representation

Abstract Graph theoretical analysis plays a vital role in various fields. In Graph theory, Graph labeling is the major area of application. Graph labeling is the assignment of labels to the vertices and edges of a graph pertaining to certain conditions. Graph theory finds its applications in Coding theory, Radar, Astronomy, Circuit designs, Communication networks, X-ray crystallography, Data base management and a major role in medical field like Brain network, Forensics and Cephalometric analysis. Here we would like to enhance the graph theory applications in the field of Psychology. This paper gives an overview of graph techniques applied in Sociometry using Sociogram. Sociometry is a quantitative method for measuring social relationships and Sociogram is a tool to find Sociometry of a social space. In this article, an analysis in a classroom climate with of a group of 50 college students was done after they were subjected to answer a series of questions probing for affiliations with other classmates. A graphical representation (sociogram) of the social links of each student was constructed by plotting the structure of interpersonal relations in the group. This graphical representation depicts the social relations, channels of influence, attraction towards others, hatred, isolation and lines of communication of the group.
Order@Cloud: An agnostic meta‐heuristic for VM provisioning, adaptation, and organisation

Summary We propose a flexible meta‐heuristic framework for virtual machine (VM) organisation, provisioning, and adaptation in the cloud domain, based on migration costs and environment constraints. Order@Cloud improves VM placements according to multiple objectives represented by rules, qualifiers, and improvement cost, which can be easily modified and extended. Order@Cloud theoretically guarantees the adoption of a better set of placements, after considering their costs and benefits, by prioritising the worst VM placements. While existing solutions address only specific objectives, our framework is objective‐agnostic and extensible, which enables the adoption and implementation of new policies and priorities. We conduct experiments using a real cloud environment data and discuss the framework's performance, flexibility, and optimality and provide insights on the challenges and benefits of deploying this framework.
Data Augmentation Based on Color Features for Limited Training Texture Classification

Image classification problem requires a large number of labeled training images to obtain a good generalization. However, it is very time consuming and expensive to label data. The color information has been demonstrated that it contains relevant information for characterizing texture image. This paper presents a novel data augmentation method dedicated to color texture classification. We firstly use color features extracted from different color spaces to augment data in case of one sample training per class. The experimental results is very promising since it significantly improves the classification performance on four benchmark color texture datasets.
New Blockchain Standards for Cryptocurrency Exchanges [Standards]

Blockchains, in a nutshell, are mechanisms used for creating artificial scarcity. They are a type of distributed database, or ledger, and a means to keep track of information shared among multiple parties. As you may know, blockchains are one of the IEEE Future Directions initiatives, with a growing number of IEEE Societies creating projects for blockchain-related standards development in the areas of agriculture, digital asset management, energy, finance, health care, the Internet of Things, and so on. In this article, the focus is mainly on cryptocurrency exchanges.
A novel web page recommender using data automatic clustering and Markov process

As the web expands and data grow in the networks, finding useful knowledge from the World Wide Web becomes a major challenge. As intelligent systems, the recommender systems help users to find their favorite resource among a large body of information. This research aimed at providing a new page recommender system, which enhances the accuracy of the suggested pages based on the user interest, Automatic Clustering-based Genetic Algorithm (ACGA), and all modified 3th-order Markov Model. Therefore, the frequency and page visiting time by each user have been extracted from the log file of the web server, while the user interest in the page during a session has been computed by a linear combination. ACGA involved 2 phases of the cluster based on (1) similarity measure and (2) Genetic Algorithm. The novel cluster partitioned the vectorization sessions in separate clusters through a new fitness function. Moreover, all modified 3th-order Markov Model used all orders of Markov Model simultaneously, and predicted the next page in the page recommender system. The research did these experiments on a real CTI dataset. Experimental results and their comparison with other approaches showed superiority in the accuracy of the proposed system.
Towards a Formal Description Language for Digital IT Consulting Products in Decentralized IT Consulting Firms

Today IT consulting services are neither digitalized nor standardized widely. They are conducted with the help of consultants and are fully based on these professionals’ knowledge and experience. Thus, the results of IT consulting initiatives often differ considerably. With the increasing demands of digitalization and globalization, new and relevant challenges arise within the IT consulting sector. Asset- or platform-based approaches can be considered as a possible means to address these challenges in the current consulting research literature. We believe that these approaches require significantly more research related to formal description of IT consulting services. Formally described Digital IT Consulting Products should be the foundation of computer-based interpretation and a future digital IT consulting service system based management and provisioning. This Ph.D. research project addresses this topic first by conducting a structured literature review to understand the state-of-the-art in IT consulting service digitalization and formal service description. This is followed by focus group interviews conducted with experts from the IT consulting sector in order to understand stakeholder requirements and to be able to design a formal description language. The formal description language is validated by implementation and testing of a prototyped digital IT consulting service system and its experimental analysis.
On some methods for generating extremely multistable systems

Abstract A multistable dynamic system can demonstrate solutions with fundamentally different behavior depending on the choice of their initial conditions, which poses a threat to its use in practical engineering applications. On the other hand, the system’s multistability may be its undisputed advantage when it is used, for example, to hide information in communication systems and audio encryption schemes to improve the performance of secure communications, since in this case a special choice of initial conditions can play the role of a “secret key”. In this paper, we propose some approaches to the generation of extremely multistable systems containing an infinite number of attractors using mathematical models of systems in Lurie form.
Steganalysis method of static JPEG images based on artificial immune system

The purpose of this work is to develop the steganalysis method of static JPEG images, based on the usage of artificial immune systems.