Reviewerly/exHarmony huggingface dataset:
A Framework for Monitoring Clustering Stability Over Time

Mining data streams and arriving at intelligent decisions is becoming more and more important nowadays as a lot of applications produce large volume data streams. Data stream clustering has been considered to be very useful for online analysis of streams. Monitoring the cluster transitions over time provide good insight into the evolving nature of the data stream. This paper introduces a framework for monitoring the stability of individual clusters and clusterings over time, along with the progress of the stream. Tracking the historical evolution of clustering structures is the main focus of this framework. Two real-world datasets have been used for conducting the experiments. The results point up the fact that monitoring the stability of clustering structures will help to get an important hint of the physical events happening in the environment. This information can be used to predict the future clustering structure changes and in turn the upcoming events.
Power-Efficient Approach to Optimize SHA-256 Bit Using Reversible Logic

With the growing network to connect people all over the world, network security has received major attention in this field. Network security provides a secured way of communication eliminating unauthorized access and misuse of important data. Cryptography is the major entity of network security and involves conversion of readable data to apparent nonsense. Secured hash algorithm (SHA) is the family of cryptographic hash functions which are one-way faster with good security and consumes low power but they have attacks like brute-force attack, side-channel attack(particularly differential power analysis) and the main reason behind these power attack is power consumption. This paper is intended to implement SHA-2 design using reversible logic. Reversible logic has received great attention in low-power VLSI design, nanotechnology and quantum computing, etc. SHA-2 contains different length output hash functions which are 224, 256, 384 and 512 bits and are used in significant applications like digital signatures, authentication and message integrity checks. A novel approach in design of SHA-2 to achieve more security, less hardware and power consumption is minimized by using reversible logic. Proposed SHA-2 algorithm of 256 bits has been simulated using Xilinx through reversible logic. Proposed SHA-2 is power efficient and having less hardware complexity in comparison with existing work. Performance analysis of reversible implementation of SHA-2 with respect to gates used, garbage and quantum cost is done. Synthesis analysis of our work shows that total time delay is 626 ns and the final result shows 12% reduction in hardware requirements.
Watermarking Technique for Multimedia Documents in the Frequency Domain

In order to secure and maintain the authenticity and integrity of multimedia documents, we use digital watermarking. This discipline can be applied to images, audios, and videos. For this reason, and to be independent of the nature of the signal composing the document to be watermarked, we will propose in this chapter two watermarking techniques, one for the audio and another for the image to watermark a video containing the two components audio and image. MDCT is combined with Watson model and a motion detection algorithm in the image watermarking technique and is combined with a psychoacoustic model to elaborate the audio watermarking technique. For the two techniques, the bits of the mark will be duplicated to increase the capacity of insertion and then inserted into the least significant bit (LSB). We will use an error correction code (Hamming) on the mark for more reliability in the detection phase. To highlight our experimental results point of view robustness and imperceptibility, we will compare the proposed techniques with some other existing techniques.
Developing a mental model for use in the context of computer security

A mental model is a useful tool for describing user's general mental processes that go into certain actions. In this paper, we investigate how to enhance the usability of security applications by considering human factors. Specifically, we study how to better understand and develop the user's mental model in the context of computer security through the use of the reasoned action approach (RAA). RAA explains that a user's behavior is determined by her intention to perform the behavior and the intention is, in turn, a function of attitudes towards the behavior, perceived norms (or social pressure), and perceived behavior control (capacity and relevant skills/abilities). A user study was conducted to test the validity of each of the main components of the model. Our user study concluded that alterations to a computer security application improved by the analysis through the mental model created improved user behavior.
Weights-Based Image Demosaicking Using Posteriori Gradients and the Correlation of R–B Channels in High Frequency

In this paper, we propose a weights-based image demosaicking algorithm which is based on the Bayer pattern color filter array (CFA). When reconstructing the missing G components, the proposed algorithm uses weights based on posteriori gradients to mitigate color artifacts and distortions. Furthermore, the proposed algorithm makes full use of the correlation of R–B channels in high frequency when interpolating R/B values at B/R positions. Experimental results show that the proposed algorithm is superior to previous similar algorithms in composite peak signal-to-noise ratio (CPSNR) and subjective visual effect. The biggest advantage of the proposed algorithm is the use of posteriori gradients and the correlation of R–B channels in high frequency.
Exploring Future IoT for Families through End User Development

Industry and research increasingly explore opportunities to make our homes smart, e.g. through the Internet of Things (IoT). Technological developments nurture this rise of smart products, seemingly corresponding to households' needs. Yet, these domestic environments remain a complex domain to study or design for. This work explores the understudied complexity of families' needs and values in relation to connected and smart technology, in particular as a multi-user group. By leveraging participatory and do-it-yourself practices, I aim to engage families in discussion - and empower them to externalize and reflect upon their views. As such, I can study their reflective practices to reveal (tacit) understandings and (latent) needs which informs future developments in smart home technologies.
HaptDoll

With the advance of display technology, the virtual reality technique has become more valuable than ever, which then promotes the development of VR/AR interactive contents. VR has the potential to provide experiences and deliver results that cannot be otherwise achieved. However, interacting with immersive applications is not always straightforward and it is not just about an interface for the user to reach their goals. It is also about users working in an intuitive attitude that is a comfortable experience and devoid of frustration. This work provides solutions to the issues of being too hard for the user to have intuitive experiences in the virtual scenario. For the intuitive experience, we developed a wireless wearable device which is set on the user's upper-body to detect the activities then import them into the virtual environment. For interactive and immersive VR contents, we developed a companion doll that provides haptic feedback in the real-world and six emotional expressions in the virtual world.
Non‐linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation

In this work, the authors analyse the estimation of the generalised autoregressive conditional heteroscedastic (GARCH) process conditional variance based on three non-linear filtering approaches: extended Kalman filter (EKF), unscented Kalman filter and cubature Kalman filter. The authors present a state model for a GARCH process and derive an EKF including second-order non-linear terms for simultaneous estimation of state and parameters. Using synthetic data, the authors evaluate the consistency and the correlation of the innovations for the three filters, by means of numerical simulations. The authors also study the performance of smoothed versions of the non-linear Kalman filters using real clutter data in comparison with a conventional quasi-maximum likelihood estimation method for the GARCH process coefficients. The authors show that with all methods the process coefficients estimates are of the same order and the resulting conditional variances are commensurable. However, the non-linear Kalman filters greatly reduce the computational load. These kind of filters could be used for the radar detector based on a GARCH clutter model that uses an adaptive threshold that demands the conditional variance at each decision instant.
Cyclic Scheduling Algorithm

CPU Scheduling has played a critical role in making efficient systems, It is the mechanism which allows one process to use the CPU for execution while other processes are put on hold because of unavailability of any required resource, with the aim of maximizing the CPU utilization and reducing the waiting time and the turnaround time. This paper presents a new Scheduling Algorithm, which supports preemption, reduces the turnaround time and the waiting time. To show its effectiveness, its comparison is done with other traditional scheduling algorithms including First Come First Serve, Shortest Job First, and Round Robin Scheduling Algorithm, and as a result it was found that the proposed algorithm provides a new and effective scheduling approach which reduces the average waiting time and average turnaround time in a much better way than the traditional approaches.
Statistical Analysis for Twitter Spam Detection

The spreading and learning of new discoveries and information is made available using current online social networks. In Recent days, the solutions may be irrelevant to the actual content; also termed as attacks in the layman’s term such attacks are been performed on Twitter as well and called as Twitter spammers. The quality of data is being compromised by addition of malicious and harmful information using URL, bio, emoticons, audio, images/videos &amp; hash-tags through different accounts by exchanging tweets, personal messages (Direct Message’s) &amp; re-tweets. Misleading sites may be linked with the malicious links which may affect adverse effects on the user and also interfere in their decision making processes. To improve user-experience from the spammers attacks, the training twitter dataset are applied and then by extracting and using the 12 lightweight features like user’s age, number of followers, count of tweets and re-tweets, etc. are used to distinguish the spam from non-spam. For enhancing the performance, the discretization of the function is important for transmission of spam detection between tweets. Our system creates classification model for Spam detection which includes binary classification and automatic learning algorithms viz. Naïve Bayes classifier or Support Vector Machine classifier which understands the behaviour of the model. The system will categorize the tweets from datasets into Spam and Non-spam classes and provide the user’s feed with only the relevant information. The system will report the impact of data-related factors such as relationship between spam and non-spam tweets, size of training dataset, data sampling and detection performance. The proposed system’s function is detection and analysis of the simple and variable twitter spam over time. The spam detection is a major challenge for the system and shortens the gap between performance appraisals and focuses primarily on data, features and patterns to identify real user and informing it about the spam tweets along with the performance statistics. The work is to detect spammed tweets in real time, since the new tweets may show patterns and this will help for training and updating dataset and in knowledge base.
A Case of Development of Contents for Marine Fishery Education Using Storyline

The purpose of this study is to improve the learning effect of water and marine high school students by using game and iterative learning to systematically memorize `swimming animal` part, which is a subcategory of `
Artificial Intelligence Methods in E-Learning

Gathering and extracting knowledge from the large amount of data available today is becoming more and more important in our information society, and similarly, learning is an essential important part of our everyday lives. The new requirements of the competing world and the development of more advanced technologies have also changed traditional educational systems, which now employ better and more effective teaching and learning methods. In this regard, the integration of artificial intelligence (AI) technologies in the field of education offers both great challenges and opportunities in building e-learning systems. E-learning systems allow learners to access the educational materials ubiquitously from anywhere at any time. Therefore, these systems have to become adaptive to the needs and preferences of each individual learner. This chapter presents a review of the important concepts and background for research to include introduction and examination of e-learning systems and intelligent tutoring systems (ITSs), available today.
Socializing via a Scarf

Smart textiles, a subset of wearable technologies, inspire novel human-interactions that leverage people's physical and cognitive actions. However, people with intellectual and developmental disabilities (IDD) have different communication, social, and sensory experiences than non-disabled people. We empirically investigate how adults with IDD determine the functionality of smart textiles and which smart textile qualities impact comfort. We demonstrate a research design method that elicits social interaction among adults with IDD. We found that smart textiles facilitate embodied and social interactions among adults with IDD. We also present an emerging design space based on smart textile capabilities and user needs.
Development and Aplication of Nutriton Education Program for the Elderly in Low Income

The aim of this study was to develop a nutrition education program ‘Nutritional Management for Healthy Aging’ for elderly. The program was developed and implemented for low-income elderly people using welfare facility located in Daejeon for 18 weeks. Various experiential activities were included to enhance the effectiveness of the education. After, the interest and performance level of the participants was investigated. The media material showed the highest interest, and the game materials drew the highest performance level among the materials.
NFL – Free Library for Fuzzy and Neuro-Fuzzy Systems

The paper presents «Neuro-Fuzzy Library» (NFL) – a free library for fuzzy and neuro-fuzzy systems. The library written in C++ is available from the GitHub repository. The library implements data modifiers (for complete and incomplete data), clustering algorithms, fuzzy systems (descriptors, t-norms, premises, consequences, rules, and implications), neuro-fuzzy systems (precomposed MA, TSK, ANNBFIS, and subspace ANNBFIS for both classification and regression tasks). The paper is accompanied by numerical examples.
A Principled Two-Step Method for Example-Dependent Cost Binary Classification

This paper presents a principled two-step method for example-dependent cost binary classification problems. The first step obtains a consistent estimate of the posterior probabilities by training a Multi-Layer Perceptron with a Bregman surrogate cost. The second step uses the provided estimates in a Bayesian decision rule. When working with imbalanced datasets, neutral re-balancing allows getting better estimates of the posterior probabilities. Experiments with real datasets show the good performance of the proposed method in comparison with other procedures.
Feature redundancy approach to efficient face recognition in still images

This paper proposes the use of redundant features for efficient recognition of faces in still images using a novel system framework that offers a detailed systematic workflow for solving the facial recognition problem. It accepts still frontal face images, processes and represents salient facial features (face, eyes, nose, and mouth) using facial detection and extraction techniques. The extracted features are then modeled by an ensemble of self-organizing maps. The ensemble outputs are later reassembled into a single dataset consisting of a normalized image Euclidean distance matrix to enhance the search space for optimal convergence and classification. The feasibility of the framework is tested using an experiment facial database captured during the study, and three benchmark facial expression databases, namely the Extended Cohn–Kanade (CK+) database, the Japanese Female Facial Expressions database, and the MMI Facial Expression database. The results suggest that feature redundancy is indeed useful for efficient facial recognition, as the support vector machine classification recorded high accuracies across the various databases, with the normalized image Euclidean distance dataset producing the highest performance, when compared with the localized principal component analysis and unnormalized image Euclidean distance datasets. Furthermore, overall classification accuracy of above 99% was achieved for the experiment (nonexpressive still face) database, compared with the benchmark facial expression databases, which yielded slightly lower results. A future direction of this work is further improvement of the framework to robustly handle severe facial variations.
Super-Resolution Restoration for Sonar Images: Comprehensive Comparison

This paper investigates the performance of advanced super-resolution restoration algorithms on sonar images. Because of the ability of being able to shoot images in relatively dim light underwater, sonar has been more and more widely used during recent years. However, images taken by sonar usually have low resolution due to the positive correlation between sonar image resolution and frequency of sonar sensors, as well as the negative relationship between working range and frequency of sonar sensors. For more accurate observation, resolution of sonar image should be increased. Since few super-resolution restoration algorithm has been designed for sonar images, we selected eight existing state-of-the-art or classical super-resolution restoration algorithms designed for optical images. Then we tested their performances on sonar images using classical and state-of-the-art image quality assessment (IQA) methods. Finally we analyze the relationship between their performances and the content of sonar images by comparing the visual qualities of reconstructed magnified super-resolution sonar images.
A hand-drawn map-based navigation method for mobile robots using objectness measure

Correct cognition of the environment is the premise of mobile robots to realize autonomous navigation control tasks. The inconsistency caused by time-varying environmental information is a bottleneck for the development and application of cognitive environment technologies. In this article, we propose an environmental cognition method that uses a hand-drawn map. Firstly, we use the single skeleton refinement and fuzzy c-means algorithms to segment the image. Then, we select candidate regions combining the saliency map. At the same time, we use the superpixels straddling method to filter the windows. The final candidate object regions are obtained based on a fusion of saliency segmentation and superpixels clustering. Based on the above objectness estimation results, we use a human–computer interaction method to construct an inaccurate hand-drawn environment map for navigation. The experimental results from PASCAL VOC2007 validate the efficacy of the proposed objectness measure method, where our result of 41.2% on mean average precision is the best of the tested methods. Furthermore, the experimental results of robot navigation in the actual scene also verified the effectiveness of the proposed approach.
Computing the Missing Lexicon in Students Using Bayesian Networks

The available lexicon for a person usually increases according to their needs through their live evolution. It is especially important during the early stages in students formation; in every class one of the objectives is to get students capable of using an extensive vocabulary according to different topics in which they are involved. We use an online platform, Lexmath, which contains data (latent lexicon) of a significant number of students in a specific geographic region in Chile. This work introduces a software application which uses data from Lexmath to determine the missing lexicon in students, by using Bayesian networks. The goal of this development is to make available to teachers the lexical weaknesses of students, to generate recommendations to improve the available lexicon.
A State-of-Art Review on Automatic Video Annotation Techniques

Video annotation has gained attention because of the rapid development of video information and wide usage of video analysis in all directions. With the capacity of depicting video at the semantic level, video annotation has numerous applications in video analysis. Due to the shortcomings present in manual video annotation, Automatic Video Annotation was introduced. In this paper, distinctive methodologies of automatic video annotation are discussed. These models are classified into five classes namely, (1) Generative models, (2) Distance-based similarity model, (3) Discriminative model, (4) Ontology-based models, (5) Deep Learning-based models. The key theoretical contributions in the current decade in support of video annotation strategies are discussed. Additionally, the future directions concerning the research aspect of video annotation strategies are discussed.
Memory based computation core for nonlinear neural operations

In this paper we introduce a memory based processor that can produce linear and nonlinear operations. We fabricated an array of 9 cores occupying 1.5mm × 1.5mm in a 130nm technology. Every core has 32 8-bit inputs. The integrated circuit (IC) runs at 85MHz with a power supply of 1.1V and consumes 2.1mW. At this operating point, the IC produces 100 8-bit MOPS and exhibits an efficiency of 21.9 pJ/OP.
Research on urinary micro-particles image feature extraction and analysis method

In order to obtain the feature of urinary micro-particles image, a method was proposed from shape and structure and region aspects in this paper. The shape and structure features were extracted according to the chain code method. Moreover, the region features were extracted by the invariant moments. Based on the binary segmentation of microscopy image, the red cell (RC), white cell (WC), cast, epithelial cell (EC) and crystal feature calculation of urinary micro-particles image are tested. The data tables are provided in detail to verify the accuracy and feasibility of the proposed method. The data mining of the tested micro-particles feature would be important to identify and classify the type of urinary micro-particles.