Reviewerly/exHarmony huggingface dataset:
PaloAnalytics: project concept, scope and early results from the system implementation

This paper describes the national funded project entitled PaloAnalytics, which develops an innovative platform that enables organizations, which operate in many countries, to monitor and analyze, in depth, the markets' interest to their products and successfully manage their marketing and communication plans, with data and insights collected from the local media. The project scope is to explore, design and develop a range of algorithms and tools in order to collect and manage big data from heterogeneous online news sources, social networks and open sources; to extract knowledge from textual references in a multilingual environment, and to interconnect information together by making user-friendly visualisations. In this paper, we specifically focus on the architecture of the system that performs trending topics extraction from the Twitter platform, presenting some early results from its implementation.
A novel hierarchical clustering approach based on data gravitation model

Abstract The purpose of clustering is to group the data points into several clusters according to their similarity. Inspired by law of gravitation, each data point considered as an object is exposed in data space and influenced by the data gravitation of the other data points. In this paper, a novel hierarchical clustering approach (NGHC) is proposed based on local data gravitation. Firstly, the dataset is partitioned into some clusters as intermediate result by a simple gravity-based clustering approach. Then the intermediate clusters are merged by a new linkage measure which combines the data gravitations between two clusters until the satisfactory result is obtained. Two real-world datasets are used to validate the clustering performance of NGHC compared with five representative clustering algorithms. The results exhibit that the NGHC algorithm obtains the best performance on low- and high-dimensional datasets.
Integrating Deep Learning and Bayesian Reasoning

Deep learning (DL) is an excellent function estimator which has amazing result on perception tasks such as visualization recognition and text recognition. But, its inner architecture acts as a black box, because the users cannot understand why such decisions are made. Bayesian reasoning (BR) provides explanation facility and causal reasoning in terms of uncertainty which is able to overcome demerit of DL. This paper is to propose a framework for the integration of DL and BR by leveraging their complementary merits based on their inherent internal architecture. The migration from deep neural network (DNN) to Bayesian network (BN) involves extracting rules from DNN and constructing an efficient BN based on the rules generated, to provide intelligent decision support with accurate recommendations and logical explanations to the users.
Students Learning Results Prediction with Usage of Mixed Diagnostic Tests and 2-Simplex Prism

Students learning results prediction is one of the “hottest” problems in modern learning process. We describe mathematical framework of students learning results assessment on the basis of mixed diagnostic tests (MDT). This framework includes cognitive graphic tools 2-simplex and 2-simplex prism, being the powerful means of data visualization for learning outcomes evaluation and efficient goal-setting. A new approach to the prediction of students’ learning results based on MDT and cognitive graphic tools is proposed. Students test results for the e-learning course “Selected Chapters of Electronics” and examples of their learning outcomes cognitive visualization are given. The proposed approach to prediction and cognitive visualization is discussed. Cross-platform software implementation specificity of cognitive graphic tools invariant to problem areas is described.
Criterion of spatial resolution of imaging system

The paper proposes the new criterion of spatial resolution of an imaging system. This criterion considers shape and dimensions of a central peak and side lobes of a point spread function, standard deviation of noise. As a result, it helps to reach the optimal balance between the characteristics of a central peak, side lobes and noise. It differs from the widely known full width at half maximum and Sparrow criterion that mainly consider only characteristics of the central peak. There are discussed the digital filter optimal according to the proposed criterion and the limitations in maximization of spatial resolution of imaging systems.
Review of the Sedimentological and Geochemical Approaches for Environmental Assessment of River Sadong, Samarahan-Asajaya District Sarawak, Malaysia

This paper presents an overview of the implication of geomorphology and sedimentology on the chemical characteristics of sediments of River Sadong, Malaysia. There is limited published work on the effect of topography, climate, soil and geology on the sediments lying in the River Sadong. Studies have been undertaken around Kota Samarahan and Asajaya areas whereby some heavy metal constituents and their environmental effects were determined. The study entails the description of thedepositional processes alonggside with physical and geochemical changes, without neglecting naturaland anthropogenic effects.
Semantic Annotation of Scientific Publications Based on Integration of Concept Knowledge

Discovery of knowledge plays a crucial role in large volumes of data for extracting the valuable knowledge units. The indexing activity with the meaning of contents instead of character strings has become the motivation of searching documents in the information retrieval field. The process of finding and selecting the relevant concepts are the main objectives for the semantic indexing activity. This paper proposes a semantic annotation strategy to support the semantic indexing activity of academic community. The proposed activity extracts the corresponding concepts of a specific document from the semantic network. The annotation activity is based on the semantic degree value of each concept. The knowledge-based approach is used to calculate the degree value of concepts and this approach only rely on the concepts structure of knowledge graph. The proposed annotation activity can be applied as part of the semantic web application and semantic search engines for analyzing and characterizing the meanings of contents.
Influence of physical and network security on company efficiency using SEM

Abstract A computer network is a system consisting of two or more computers that are interconnected and designed to share resources, communicate, and access information. Nowadays, IT security plays an important role in a company. The purpose of this study is to get an overview of the performance of IT security and network security to improve the efficiency of company. Implementing IT security and network security, however, poses major challenges to many organizations. To ensure IT and network security, the article applies SEM method to analyze its influence in performance efficiency. The research results show that physical security is more influential on enterprise efficiency than network security. The outcome of the article is expected to deliver reference for companies to apply appropriate physical and network security.
Comparison of Machine Learning Based Methods Used in Bengali Question Classification

QA classification system maps questions asked by humans to an appropriate answer category. A sound question classification (QC) system model is the pre-requisite of a sound QA system. This work demonstrates phases of assembling a QA type classification model. We present a comprehensive comparison (performance and computational complexity) among some machine learning based approaches used in QC for Bengali language.
Making activities visible: a way to patient empowerment

Patient empowerment is a concept that has found an extensive discussion in health care. It is of significant interest, especially now when the number of people living with chronic diseases has increased. However, there is a stigma associated with being a patient, and contributing to empowerment becomes more challenging. In this paper, we present an empirical study of the cognitive rehabilitation process. We root the case analysis in Computer Supported Cooperative Work (CSCW) research field and further reflect on patient empowerment concept. Through our reflections, we contribute to the discussion of patient empowerment by highlighting some of the work structures that need to be in place in healthcare to make possible patient empowerment. Moreover, we contribute back to CSCW through a situated discourse of concepts in the context of rehabilitation care model. Rendering publicly visible the activities and facilitating the overseeing of events and activities, both in the level of the overall treatment as well as the micro-interactions among health care professionals and patients, are found relevant in building awareness among parts and leading to empowerment.
Engaged Reading Strategy in Literature Appreciation Learning and Its Implications on Character Education

This current study aims at describing (1) engaged-reading strategy in the literature appreciation learning, (2) methods of engaged-reading strategy, and (3) the implementation of engaged-reading strategy to the character education.The employed method is literary review from some relevant references related to the focus of this study.There are some revealed findings listed as below.(1) The engaged-reading strategy in literature appreciation learning is deemed suitable because this strategy elaborates the mind, heart, and flow in the process of reading and thus it trains students to become skilled readers.(2) The implementation of engaged-reading strategy provides cognitive, affective, and psychological skills in the appreciation activities for creative thinking with visualization, prediction, inference, understanding, and challenges in real life.(3) The implications of the engaged-reading strategy to the character education are curiosity, hard work, cooperation, and self-esteem for resolving problems based on the literature appreciation and for adequately developing social competence.
Interactive gigapixel video streaming via multiscale acceleration

Immersive video applications grow faster for users to freely navigate within a virtualized 3D environment for entertainment, productivity, training, etc. Fundamentally, such system can be facilitated by an interactive Gigapixel Video Streaming (iGVS) platform from array camera capturing to end user interaction. This interactive system demands a large amount of network bandwidth to sustain the reliable service provisioning, hindering its massive market adoption. Thus, we propose to segment the gigapixel scene into non-overlapped spatial tiles. Each tile only covers a sub-region of the entire scene. One or more tiles will be used to represent an instantaneous viewport interested by a specific user. Tiles are then encoded at a variety of quality scales using various combinations of spatial, temporal and amplitude resolutions (STAR), which are typically encapsulated into temporally-aligned tile video chunks (or simply chunks). Chunks at different quality level can be processed in parallel for real-time purpose. With such setup, diverse chunk combinations can be simultaneously accessed by heterogeneous user per its request, and viewport-adaptation based content navigation in an immersive space can be also realized by adapting multiscale chunks properly, under the bandwidth constraints. A serial computational vision models measuring the perceptual quality of viewport video in terms of its quality scales, adaptation factors, as well as the peripheral vision thresholds, are devised to prepare and guide the chunk adaptation for the best perceptual quality index. Furthermore, in response to the time-varying network, a deep reinforcement learning (DRL) based adaptive real-time streaming (ARS) scheme is developed, by learning the future decision from the historical network states, to maximize the overall quality of experience (QoE) in a practical Internet-based streaming scenario. Our experiments have revealed that averaged QoE can be improved by about 60%, and its standard deviation can be also reduced by &asymp; 30%, in comparison to the popular Google congestion control algorithm widely adopted in existing system for adaptive streaming, demonstrating the efficiency of our multiscale accelerated iGVS for immersive video application.
Semantic Vehicle Segmentation in Very High Resolution Multispectral Aerial Images Using Deep Neural Networks

The fusion of complementary information from co-registered multi-modal image data enables a more detailed and more robust understanding of an image scene or specific objects, and is important for several applications in the field of remote sensing. In this paper, the benefits of combining RGB, near infrared (NIR) and thermal infrared (TIR) aerial images for the task of semantic vehicle segmentation through deep neural networks are investigated. Therefore, RGB, NIR and TIR image triplets acquired by the Modular Aerial Camera System (MACS) are precisely co-registered through the application of a virtual camera system and subsequently used for the training of different neural network architectures. Various experiments were conducted to investigate the influence of the different sensor characteristics and an early or late fusion within the network on the quality of the segmentation results.
Ovoids and primitive normal bases for quartic extensions of Galois fields

We determine lower bounds for the number of primitive normal elements in a four-dimensional extension E over a Galois field $$F={\mathrm{GF}}(q)$$ . Our approach is based on viewing E as the three-dimensional projective space $$\Gamma =\mathrm{PG}(3,q)$$ . In any of the three cases, whether q is even, or $$q\equiv 3 { \text{ mod } }4$$ , or $$q\equiv 1 { \text{ mod } }4$$ , we use a decomposition of the multiplicative group of E in order to determine a (canonical) partition of the point set of $$\Gamma $$ into $$q+1$$ ovoids. The points of $$\Gamma $$ are distinguished into primitive and non-primitive ones, and an ovoid is called primitive if it contains at least one primitive point. The bounds are derived by studying the intersections of the primitive ovoids with the configuration of those points of $$\Gamma $$ which do not give rise to normal elements of E / F. Given that $$q^2+1$$ is a prime number when q is even, or that $$\frac{1}{2}(q^2+1)$$ is a prime number when q is odd, we actually achieve the exact number of all primitive normal elements for the quartic extension over F.
Adaptive oriented filtration of digital images in the spatial domain

This article describes the developed method of adaptive-oriented filtration of digital images in the spatial domain, which is designed to remove noise and enhance the visual quality of images. as the kernel of filter an oriented two-dimensional gaussian distribution is used. as a result of this filtration, the contours of the images are blurred slightly, since the filtration is mainly carried out along the contour direction, that is, the filter parameters are adapted to each image region. the proposed method of image filtering is implemented in Matlab.
Utilizing full neuronal states for adversarial robustness

Small, imperceptible perturbations of the input data can lead to DNNs making egregious errors during inference, such as misclassifying an image of a dog as a cat with high probability. Thus, defending against adversarial examples for deep neural networks (DNNs) is of great interest to sensing technologies and the machine learning community to ensure the security of practical systems where DNNs are used. Whereas many approaches have been explored for defending against adversarial attacks, few have made use of the full state of the entire network, opting instead to only consider the output layer and gradient information. We develop several motivated techniques that make use of the full network state, improving adversarial robustness. We provide principled motivation of our techniques via analysis of attractor dynamics, shown to occur in the highly recurrent human brain, and validate our improvements via empirical results on standard datasets and white-box attacks.
Password Strength Estimators Trained on the Leaked Password Lists

Passwords currently are and will be used as the main authentication mechanism across online applications for the foreseeable future. Estimating the strength of a user’s password gives the user a valuable insight into the strength or weakness of their chosen passwords. Current password strength estimators, when giving an estimate on a password’s strength, often fail to consider the plethora of leaked lists at an attacker’s disposal. This research investigates the effect of training a password strength estimator on a leaked list of 14.3 million passwords, all of which are commonly used in the password cracking world and then observing the effect that it has on the estimation of a password’s strength. Through modifying the trained dictionary lists that the zxcvbn classifier is fed, an estimate that accounts for the leaked list was achieved. Our empirical results show that there is a clear need to include leaked passwords in the password strength estimation process and that the accuracy of the estimator should not be sacrificed in order to provide a faster service.
The improvement students’ reproductive health knowledge using BASR BPP KRR learning material

Abstract Based on studies reported the higher cases and improper sexual behaviour emerged due to the lower of health reproductive knowledge among teenagers and our representative survey that described a need for improving students health sexual knowledge in Kota Padang, we integrated promotion and prevention knowledge into learning material for students of senior high school (SMA). Furthermore, an experimental study has been conducted to measure the influence of learning material, called BASR BPP KRR toward adolescence’ health reproductive knowledge. Population consists of SMA students in Kota Padang. By using purposive sampling, around 179 SMA students are involved as the sample which represent three different categories of school academic ability level. Experimentation applies pre-test – post-test group only design, consist of two variable, learning material (independent variable) and reproductive health knowledge (dependent variable) which measured using multiple choice test. Then, the learning result (gain) analysed statistically by using t-test. The result showed the highest knowledge improvement on average were 28.7 by students’ of higher category school, 17.7 by middle category school, and 1.04by lower category school. The result shows BASR BPP KRR learning material significantly contributes to the improvement of higher academic level students. The increasing gain score also found toward middle academic level students, yet it is not statistically significant. Toward students’ of a lower academic level, BASR BPP KRR learning material does not increase the knowledge.
Aadhar Based Electronic Voting Machine

Abstract The “AADHAR BASED ELECTRONIC VOTING MACHINE” describes an easy and secure electoral voting system for Indian election. Here two levels of authentication is done. Using RFID based aadhar card and by taking fingerprints because both the levels are unique for every user. User id and fingerprint are to be matched in given database or else he/she cannot eligible to cast the vote. The process of this system is done in a simple way, first user RFID card is scanned for user details then fingerprint is taken to verify and using keypad can press the number of his/her favourite candidate to cast the vote after that lcd will displays a confirmation message. This will be the high secured system than our present voting system because authentication identity is verified by two levels before casting a vote. Directly votes will be counted so that the time taken by election commission to announce results will be fast and in short period.
Clarity under cognitive constraint: Can a simple directive encourage busy speakers to avoid ambiguity?

abstract Failing to communicate a message in everyday settings can be a frustrating experience. However, miscommunication can lead to disaster in high-stakes situations. Yet in these contexts, under pressure to perform efficiently, speakers may also find themselves with limited resources to devote to message clarity. To understand how cognitive constraint affects communication and explore a possible low-cost solution, we investigated a method for moderating ambiguity production in the face of competing attentional demands: taking the perspective of the listener. Over two experiments, speakers labeled images (Experiment 1) or provided instructions (Experiment 2) to listeners in a non-interactive communication task. In both experiments, speakers were randomly assigned to cognitive constraint and perspective-taking conditions, such that some speakers were under higher cognitive constraint and some speakers received a simple perspective-taking directive. We replicated previous findings that additional cognitive constraint impairs speakers’ ability to avoid ambiguity. Additionally, we found that a simple directive can promote speaker clarity when labeling images, but not when providing instructions. These results suggest that a simple directive is likely insufficient to ensure speaker clarity in all cases.
RETRACTED: CO-EXERCISE OF DT AND KNN CLASSIFIERS FOR FAULT DETECTION AND CLASSIFICATION OF SEMI SUPERVISED MACHINE LEARNING

Abstract In this paper a new comprehensive scheme for fault finding and logging for a compound progression is presented. This paper concentrates on the progression facts and skilful data, a trivial amount of labelled data and a large amount of unlabelled progression data are used for constructing the neighbourhood weighted graph. The optimal prediction label matrix of unlabelled data and the optimal regression function are obtained by solving the optimal problem. Then the monitoring data is projected to the low dimensional with the regression function, and the state label matrix of monitoring data can be acquired. This scheme can guarantee intrinsic structural of data by constructing an undirected weighted graph, and is suitable for the complex progression with nonlinear trait.
Accommodating Secret Sharing Technique for Personal Remembrance via Steganography

The secret sharing scheme is a data security tool that provide the reliability and robustness for multi-user authentication systems. This work focus on improving counting-based secret sharing technique for better shares security as well as simple and fast computation. The research considers resolving some originally published defects in the shares reconstruction phase proposing new distribution model authenticated to be optimized practical and efficient. The shares reconstruction model also reflected increasing the security of the system by authenticity of the shares via steganography. We have employed image-based steganography methods to store the shares presenting a comparison proofed remark. The improved system has been analyzed according to distortion security and capacity parameters showing attractive contributions.