Reviewerly/exHarmony huggingface dataset:
Genetic Algorithm based Approach to Select Suitable Cover Image for Image Steganography

Steganography is used to perform covert communication. The advantage of steganography over other secret communication techniques is its ability to conceal the presence of covert communication. In image steganography, the secret information is concealed in the cover image, in such a way that it produces very negligible change in the cover image. A vast amount of research is performed in image steganography but very limited studies have explored the possibility of choosing a cover image for steganography which provides better compatibility with the secret data. In this paper, we propose a genetic algorithm based technique for selecting a cover image from a database of images. The selected cover image is most compatible with the given secret data. We further explore the possibility of rearranging the secret data to increase the imperceptibility of the stego image.
User Authentication System Based on Speech and Cascade Hybrid Facial Feature

With the increasing demand for security in many fastest growing applications, biometric recognition is the most prominent authentication system. User authentication through speech and face recognition is the important biometric technique to enhance the security. This paper proposes a speech and facial feature-based multi-modal biometric recognition technique to improve the authentication of any system. Mel Frequency Cepstral Coefficients (MFCC) is extracted from audio as speech features. In visual recognition, this paper proposes cascade hybrid facial (visual) feature extraction method based on static, dynamic and key-point salient features of the face and it proves that the proposed feature extraction method is more efficient than the existing method. In this proposed method, Viola–Jones algorithm is used to detect static and dynamic features of eye, nose, lip, Scale Invariant Feature Transform (SIFT) algorithm is used to detect some stable key-point features of face. In this paper, a research on the audio-visual integration method using AND logic is also made. Furthermore, all the experiments are carried out using Artificial Neural Network (ANN) and Support Vector Machine (SVM). An accuracy of 94.90% is achieved using proposed feature extraction method. The main objective of this work is to improve the authenticity of any application using multi-modal biometric features. Adding facial features to the speech recognition improve system security because biometric features are unique and combining evidence from two modalities increases the authenticity as well as integrity of the system.
Work-in-Progress—Role of Innovative Teaching Strategies in Enhancing STEM Education in Malaysia

The Ministry of Education Malaysia has emphasised on the importance of Science, Technology, Engineering and Mathematics (STEM) education in the Malaysia Education Blueprint 2013 - 2025. The inclusion of STEM education in the blueprint is much needed as the number of students enrolling for STEM subjects has seen a decrease over the past few years, especially at the upper secondary level where the students are given the option to choose their subjects of choice from the pool of Science or Arts. This study reviews a collection of literature and aims to further understand the issue causing the downtrend in students opting for STEM subjects in school. The study then reviews literature on using e-learning as a tool to rectify the issue in hand. The study hopes to encourage researchers to look into using e-learning (more specifically, m- learning) as a way to make the learning process of STEM subjects in school more interesting for the students and in turn, will encourage more students to undertake these subjects.
Fault sTypes of Adaptive and Context-Aware Systems and Their Relationship with Fault-based Testing Approaches

Context: Testing adaptive systems (ASs) and context-aware systems (CASs) is usually more challenging than testing conventional systems. This is due to their characteristics, such as the high number of possible configurations and runtime adaptations. Therefore, it is important to know the types of faults that may be incurred by these systems so that such faults can be either avoided during implementation, or detected through tests (e.g. in a fault-based testing approach such as mutation). Objective: Characterising and grouping types of faults for ASs and CASs that have been previously described in the literature, but are disperse in various studies. Moreover, identifying which and how the fault types are addressed by the current research on fault-based testing. Method: we used a Systematic Literature Review that addressed testing for ASs and CASs, whose one of the goals was a characterisation of fault types for ASs and CASs. By dealing with such a goal, we analysed 11 primary studies that address fault types. We also used code snippets to illustrate how the fault types may occur. Finally, we analysed the faults that are addressed in other 7 studies that investigated fault-based testing for ASs and CASs. Results: We present lists of specific fault types (26 in total) and fault type categories (6 in total) for AS and CASs, and discuss the relationship between these fault types with current fault-based testing approaches. Conclusion: Our results are original when compared to the state-of-the-art, given that we proposed the first characterisation of fault types for ASs and CASs. Beyond this, we point out room for improvement of fault-based testing of these types of systems, particularly regarding the types of faults addressed by the current research.
Data Clustering based on Differential Evolution with Modified Mutation Strategy

Clustering is an essential and central task of the data mining process. It aims to find meaningful and valuable groups of unlabeled data. Differential Evolution (DE) algorithm has become a successful alternative for clustering because of their ability to achieve good-quality groups of a given dataset. The adopted mutation and crossover strategies can impact the effectiveness of the DE algorithm. This paper proposes a modified mutation strategy for a differential evolution based data clustering algorithm to enhance the cluster solutions' quality and the convergence speed. The proposed mutation strategy was compared with two normally used mutation strategies such as DE/rand/1 and DE/best/1 and also compared with four nature-inspired metaheuristic algorithms such as Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Particle Swarm Optimization with Age-Group topology (PSOAG) to study the clustering performance. The experimental tests were conducted on five UCI datasets that are frequently used for metaheuristics based clustering. These tests show that the DE based clustering algorithm with the proposed strategy is more efficient and robust than the others.
News Comments Modeling for Opinion Mining: The Case of Senegalese Online Press

Opinion Mining is the processing textual contents from on-line discussions in order to highlight the opinions of net surfers. In the process, the identification of the comments through a model and a formalism which encompass the methods and the data is critical step. Unfortunately, this step is generally neglected in most of the Sentiment analyses studies. The objective of this article is to address this challenging issue of modeling News comments. The relevance of such a study is to enable easy data collection and to avoid the algorithmic complexity of sentiment analysis methods.
A Neural Dynamic Network Drives an Intentional Agent That Autonomously Learns Beliefs in Continuous Time

Autonomous learning is the ability to form knowledge representations solely through one’s own experience. To autonomously learn, an agent must be able to perceive, act, memorize, plan, and desire; it must be able to form intentional states. We build on a neural process account of intentionality, in which intentional states are stabilized by interactions within populations of neurons that represent perceptual features and movement parameters. Instabilities in such neural dynamics induce sequences of intentional behavior. In this article, we examine the neural process organization required to decide and control when learning takes place, to build the representations that can hold learning data, and to organize the selection of neural substrate to learn the novel patterns. We demonstrate how a neural dynamic network may learn new beliefs about the world from single experiences, may activate and use beliefs to satisfy desires, and may deactivate beliefs when their predictions do not match experience. We illustrate the ideas in a simple toy scenario in which a simulated agent autonomously explores an environment, directs action at objects, and forms beliefs about simple contingencies in this environment. The agent utilizes learned beliefs to satisfy its own fixed desires.
Blink Detection Using Image Processing to Predict Eye Fatigue

With the use of information terminals represented by smartphones, our eyes get tired. One index indicating eye fatigue is a change in the number of blinks. In this study, we set the ultimate goal that is to develop eye fatigue prevention system with less time and less physical burden for patient. By doing this, the information terminal performs a flickering of the eyes using a detection camera and a validity check. Previous EAR study with blink detection is confirmed to be effective, and this paper proposes a new formula EARM used EAR. As an evaluation method of blink detection, we used the total time average of the square of the residual value. The results showed that EARM was more accurate than the EAR. Further, it was suggested that the number of blinks during VDT work can be classified into several patterns.
Impact of Gender on Motivation, Engagement and Interaction Behavior in Mobile assisted learning of English

In developing countries like India, proficiency in English is a desired skill since it boosts job prospects and improves economic condition. However, students' proficiency level remains inadequate, even though English is taught in schools. Mobile language learning apps like Hello English addresses this gap by providing learners with interactive activities, collaborative learning opportunities and game-based challenges to develop their English proficiency. In this context, the current paper explores three research questions that examines gender-based variation in motivation, engagement and interaction behavior of school students who use Hello English. Students' motivation level and their subsequent engagement with the app are measured through two separate questionnaires. Gender difference in students' interaction behavior is analyzed using their interaction data with the app. We found that female students are highly motivated, prefers to interact with the app more often and spend more time on app compared to male students.
The Importance of Feedback for Object Hand-Overs Between Human and Robot

Robot systems will soon be able to hand over objects to humans as well as receive objects from humans in a robust way. From an ergonomics point of view, it is required to evaluate those robot systems and their interactions with humans based on appropriate parameters and design them accordingly. Therefore, we conducted an experiment with human-to-human hand-overs. The aim was to analyze different conditions of hand-overs that occur in our daily life such as different spatial direction, cups with varying filling quantities and varying states of perception of the receiving person. It was shown that cups with a higher filling level lead to a significantly higher duration of the interaction phase than cups with a lower filling level. Additionally, perceptual impairment of the receiver and thereby a lack of feedback led to a higher duration of the interaction phase.
Technical Note: Synthesizing of lung tumors in computed tomography images

Purpose When investigating new radiation therapy techniques in the treatment planning stage, it can be extremely time consuming to locate multiple patient scans that match the desired characteristics for the treatment. With the help of machine learning, we propose to bypass the difficulty in finding patient computed tomography (CT) scans that match the treatment requirements. Furthermore, we aim to provide the developed method as a tool that is easily accessible to interested researchers. Methods We propose a generative adversarial network (GAN) to edit individual volumes of interest (VOIs) in pre‐existing CT scans, translating features of the healthy VOIs into features of cancerous volumes. Training and testing was done using VOIs from a dataset of 460 diagnostic and lung cancer screening CT scans. Agreement between real tumors and those produced by the editor was tested by comparing the distributions of several histogram parameters and second‐order statistics as well as using qualitative analysis. Results After training, the network was successfully able to map healthy CT segments to realistic looking cancerous volumes. Based on visual inspection, tumors produced by the editor were found to be both realistic and visually consistent with the surrounding anatomy when placed back into the original CT scan. Furthermore, the network was found to be able to extrapolate well beyond the upper size limit of the training set. Lastly, a graphical user interface (GUI) was developed to easily interact with the resulting network. Conclusion The trained network and associated GUI can serve as a tool to develop an abundance of lung cancer patient data to be used in treatment planning. In addition, this method can be extended to a variety of cancer types if given an appropriate baseline dataset. The GUI and instructions on how to utilize the tool have been made publicly available at https://github.com/teaghan/CT_Editor .
Passive needle guide tracking with radial acquisition and phase‐only cross‐correlation

Purpose Acceleration of a passive tracking sequence based on phase‐only cross‐correlation (POCC) using radial undersampling. Methods The phase‐only cross‐correlation (POCC) algorithm allows passive tracking of interventional instruments in real‐time. In a POCC sequence, two cross‐sectional images of a needle guide with a positive MR contrast are continuously acquired from which the instrument trajectory is calculated. Conventional Cartesian imaging for tracking is very time consuming; here, a higher temporal resolution is achieved using a highly undersampled radial acquisition together with a modified POCC algorithm that incorporates the point‐spread‐function. Targeting and needle insertion is performed in two phantom experiments with 16 fiducial targets, each using 4 and 16 radial projections for passive tracking. Additionally, targeting of eight deep lying basivertebral veins in the lumbar spines is performed for in vivo proof‐of‐application with four radial projections for needle guide tracking. Results The radially undersampled POCC sequence yielded in the phantom experiments a lateral targeting accuracy of 1.1 ± 0.4 mm and 1.0 ± 0.5 mm for 16 and 4 radial projections, respectively, without any statistically significant difference. In the in vivo application, a mean targeting duration of 62 ± 13 s was measured. Conclusion Radial undersampling can drastically reduce the acquisition time for passive tracking in a POCC sequences for MR‐guided needle interventions without compromising the targeting accuracy.
On the Development of a Model to Support the Combined Use of Agile Software Development with User-Centered Design and Lean Startup

Despite the benefits of Agile Software Development, organizations still do not have a clear understanding of the problem to be solved by the software product-to-be. This problem can solved by having a closer engagement with users, and the combined use of Agile, User-Centered Design, and Lean Startup has been pointed out as a strategy to achieve this. However, this novel development approach does not have publicly available tools or instruments that aid organizations in using it in any way. We describe the first steps in building a literature and practice-informed acceleration model to aid in the adaptation to this combined approach. The model is based on a set of principles, activities, and practices derived from Agile, User-Centered Design, and Lean Startup pillars. These will serve as guidance for software teams to determine the competences and skills they need, by accounting for forces that might influence their work processes. We discuss how the acceleration model could help teams adapt to the combined approach and point out our future research plan to define the next steps to fully realize our model.
Graffix: Efficient Graph Processing with a Tinge of GPU-Specific Approximations

Parallelizing graph algorithms on GPUs is challenging due to the irregular memory accesses involved in graph traversals. In particular, three important GPU-specific aspects affect performance: memory coalescing, memory latency, and thread divergence. In this work, we attempt to tame these challenges using approximate computing. We target graph applications on GPUs that can tolerate some degradation in the quality of the output for obtaining the result in short order. We propose three techniques for boosting the performance of graph processing on the GPU by injecting approximations in a controlled manner. The first one creates a graph isomorph that brings relevant nodes nearby in memory and adds controlled replica of nodes to improve coalescing. The second reduces memory latency by facilitating the processing of subgraphs inside shared memory by adding edges among specific nodes and processing well-connected subgraphs iteratively inside shared-memory. The third technique normalizes degrees across nodes assigned to a warp to reduce thread divergence. Each of the techniques offers notable performance benefits, and provides a knob to control the amount of inaccuracy added to an execution. We demonstrate the effectiveness of the proposed techniques using a suite of five large graphs with varied characteristics and five popular graph algorithms.
Improvement of disparity map refinement stage using adaptive least square plane fitting technique

This Letter presents an improvement of disparity map refinement stage using adaptive least square plane fitting technique. This technique is proposed to increase the accuracy on the final stage of stereo matching algorithm. Fundamentally, the accuracy of matching process depends on the robustness of an algorithm on the plain colour region, depth discontinuity and repetitive pattern area. These regions are difficult to be matched and very challenging. Thus, this Letter proposes multiple point selections of disparities on a plane to adaptively group similar disparity values. Then, the distance of each disparity is calculated to ensure the values are accurately grouped. The distance refers to an assigned reference radius disparity location. Based on the experimental results using the KITTI and Middlebury benchmark images, the proposed method is capable of increasing the accuracy on the challenging regions and the quantitative measurement.
Photograph Classification Based on Main Theme and Multiple Values by Deep Neural Networks

Recently, numerous people take photographs easily by smart-phones and post them on social network services. On the other hand, huge knowledge and technique are required in order to become skilled photographers. It is hard to acquire these information especially on real-time filming. Thus, the aim of the research is to create a system to suggest advices for novices of photographers. To construct such a system, the method to evaluate photos precisely like a professional photographer is required. In this paper, we have proposed original dataset of scenery photographs with detailed scores by professional photographers and construct Deep Convolutional Neural Networks to classify two class images with the labels evaluated by the author as a professional photographer.
Adaptation and Validation of the Tamil (Sri Lanka) Version of the Montreal Cognitive Assessment (MOCA)

Kalyani is published by Research and Publications of the University of Kelaniya. It publishes articles, reviews and other communications in Humanities and Social Sciences, Management Sciences, Science and Technology, Medicine, Religions and Ayurveda studies. It is a multidisciplinary journal, hence, the academic and professionals in Sri Lanka and abroad are the target authors.
Adversarial multiple access channels and a new model of multimedia fingerprinting coding

We consider different models of malicious multiple access channels, especially for binary adder channel and for A-channel, and show how they can be used for the reformulation of digital fingerprinting coding problems. In particular, we propose a new model of multimedia fingerprinting coding. In the new model, not only zeroes and plus/minus ones but arbitrary coefficients of linear combinations of noise-like signals for forming watermarks (digital fingerprints) can be used. This modification allows dramatically increase the possible number of users with the property that if t or less malicious users create a forge digital fingerprint then a dealer of the system can find all of them with zero-error probability. We show how arisen problems are related to the compressed sensing problem.
Human Interaction Recognition in Videos with Body Pose Traversal Analysis and Pairwise Interaction Framework

Interaction recognition in videos with body pose is gaining remarkable attention due to its speed and robustness. Recently proposed recurrent neural network (RNN) and deep ConvNets-based methods are showing good performances in learning sequential information. Despite these good performances, RNN lags behind in learning spatial relation between body parts, while deep ConvNets requires huge amount of data for training. We propose a traversal-based three-layer neural network (TNN), followed by pairwise interaction framework (PIF) for interaction recognition. We also propose a novel algorithm for tracking humans in successive frames. The proposed algorithm computes collective traversal of individual body parts across the frames and feeds to TNN to learn effective representation of complex actions. The PIF model combines confidence scores of a pair of action labels corresponding to an interaction for final interaction prediction. We extensively evaluate the approach on two publicly available datasets i.e. UT-Interaction and SBU Kinect Interaction. Results show that our proposed approach outperforms the state-of-the-art methods.
Image Guided Autonomous Grasping and Manipulation for Valve Turning

In this paper, we implement and evaluate image guided visual tracking system for autonomous grasping and manipulation for valve turning applications. The first part of this paper tries to collect the results reached by the researcher and industrial community so far within the field of image guided autonomous manipulation from the technological and control point of view. Then, we present detailed design process and steps of the image guided visual tracking systems. Finally, we present implementation results of the image based visual tracking system for autonomous grasping and manipulation on a 6-DOF UR5 mobile manipulator for valve turning applications.