Reviewerly/exHarmony huggingface dataset:
Responding to uncertainty in emotion recognition

Purpose Uncertainty is an under-respected issue when it comes to automatic assessment of human emotion by machines. The purpose of this paper is to highlight the existent approaches towards such measurement of uncertainty, and identify further research need. Design/methodology/approach The discussion is based on a literature review. Findings Technical solutions towards measurement of uncertainty in automatic emotion recognition (AER) exist but need to be extended to respect a range of so far underrepresented sources of uncertainty. These then need to be integrated into systems available to general users. Research limitations/implications Not all sources of uncertainty in automatic emotion recognition (AER) including emotion representation and annotation can be touched upon in this communication. Practical implications AER systems shall be enhanced by more meaningful and complete information provision on the uncertainty underlying their estimates. Limitations of their applicability should be communicated to users. Social implications Users of automatic emotion recognition technology will become aware of their limitations, potentially leading to a fairer usage in crucial application context. Originality/value There is no previous discussion including the technical view point on extended uncertainty measurement in automatic emotion recognition.
The role of readers’ literary preferences in predicting success in fiction search

Purpose The purpose of this paper is to study what extent readers’ socio-demographic characteristics, literary preferences and search behavior predict success in fiction search in library catalogs. Design/methodology/approach In total, 80 readers searched for interesting novels in four differing search tasks. Their search actions were recorded with a Morae Recorder. Pre- and post-questionnaires elicited information about their background, literary preferences and search experience. Readers’ literary preferences were grouped into four orientations by a factor analysis. Linear regression analysis was applied for predicting search success as measured by books’ interest scores. Findings Most literary orientations contributed to search success, but in differing search tasks. The role of result examination was greater compared to querying in contributing search success almost in each task. The proportion of variance explained in books’ interest scores varied between 5 (open-ended browsing) and 50 percent (analogy search). Research limitations/implications The distribution of participants was biased toward females, and the results are aggregated within search session, both reducing the variation of the phenomenon observed. Originality/value This study is one of the first to explore how readers’ literary preferences and searching are associated with finding interesting novels, i.e. search success, in library catalogs. The results expand and support the findings in Mikkonen and Vakkari (2017) concerning associations between reader characteristics and fiction search success.
Network Transfer Learning via Adversarial Domain Adaptation with Graph Convolution.

This paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in a partially labeled source network to assist node classification in a completely unlabeled or partially labeled target network. Existing methods for single network learning cannot solve this problem due to the domain shift across networks. Some multi-network learning methods heavily rely on the existence of cross-network connections, thus are inapplicable for this problem. To tackle this problem, we propose a novel network transfer learning framework AdaGCN by leveraging the techniques of adversarial domain adaptation and graph convolution. It consists of two components: a semi-supervised learning component and an adversarial domain adaptation component. The former aims to learn class discriminative node representations with given label information of the source and target networks, while the latter contributes to mitigating the distribution divergence between the source and target domains to facilitate knowledge transfer. Extensive empirical evaluations on real-world datasets show that AdaGCN can successfully transfer class information with a low label rate on the source network and a substantial divergence between the source and target domains. Codes will be released upon acceptance.
Extended Study of Undergraduate Students’ Usage of Mobile Application for Individual Differentiation Learning Support of Lecture-based General Education Subjects

This is an investigation in the development and assessment of a mobile application that was developed for differentiated (Individualized) learning in support of lecture-based general education subjects. The mobile phone application was developed as a learning aid as a substantial number of students were experiencing difficulty in successfully completing these courses. The researcher developed the mobile phone application that contains uploaded course content from each course. This was used to create a summary of the lecture content from each course, in the form of infographics and linked ‘PDF files’ or soft copies of lecture materials. The researcher developed exercises that students could complete which later contributed to self-created content based on their understanding, skills, and interests. With use of the mobile application, 100% of the students successfully completed these courses. Moreover, the level of satisfaction in education among students was high with an average of 4.75. The standard deviation of 0.43 indicated that, concerning contemporary education, and the active learning atmosphere, effective learning can be achieved due to the development of learning innovation in the use of electronic devices, which give opportunities to students to use their creativity in problem solving, as based on their preferences, whilst demonstrating their understanding about lesson content.
A Method for Track Association Using Dynamic Programming

Track association is one of the key problems in modern distributed multi-sensor information fusion systems design, which paves the way for the subsequent combination of state estimates. A distributed data processing structure is preferred in such a distributed system, which results in a lot of advantages over centralized architecture. Track association generally contains two steps: establishing an association metric to evaluate each track-to-track association hypothesis, and the selection of the best assignment between sets of tracks based on association metric. Feature assisted tracking often can't be realized in lots of situations, especially in tracking to opponent target, in that the features can't be acquired effectively. A track association method is presented based on exploiting the inherent nature of the targets' tracks using only kinematic data, which was implemented by dynamic programming. The accumulated sequential statistics' taking account of the cross-covariance were adopted in formulating the association metric, providing robust similarity description to track-to-track association hypothesis in complex target environment. Then the best assignment between sets of tracks was achieved by solving an optimization problem using dynamic programming. Numerical simulations are conducted to show the performance of the presented method.
Analysis Of c4.5 and ID3 Methods in Determining Student Graduation

Abstract From the performance measurement of the two algorithms that have been done based on the amount of data, it can be concluded that ID3 algorithm has better performance (precision, recall, and accuracy) than C4.5 algorithm. The performance measurement of the two algorithms based on the number of attributes indicates that the ID3 algorithm has better performance (precision, recall, and accuracy) than the C4.5 algorithm. Overall, From the experiments that have been carried out it can be concluded that ID3 has a better performance than C4.5 algorithm. Performance measurement of a data mining algorithm can be done based on several criteria such as accuracy of prediction, speed / efficiency, reliability, scalability and interpretation. In this study the authors suggest using the ID3 algorithm method.
Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)

Although Seq2Seq models for table-to-text generation have achieved remarkable progress, modeling table representation in one dimension is inadequate. This is because (1) the table consists of multiple rows and columns, which means that encoding a table should not depend only on one dimensional sequence or set of records and (2) most of the tables are time series data (e.g. NBA game data, stock market data), which means that the description of the current table may be affected by its historical data. To address aforementioned problems, not only do we model each table cell considering other records in the same row, we also enrich table's representation by modeling each table cell in context of other cells in the same column or with historical (time dimension) data respectively. In addition, we develop a table cell fusion gate to combine representations from row, column and time dimension into one dense vector according to the saliency of each dimension's representation. We evaluated our methods on ROTOWIRE, a benchmark dataset of NBA basketball games. Both automatic and human evaluation results demonstrate the effectiveness of our model with improvement of 2.66 in BLEU over the strong baseline and outperformance of state-of-the-art model.
Duet: An Expressive Higher-order Language and Linear Type System for Statically Enforcing Differential Privacy

During the past decade, differential privacy has become the gold standard for protecting the privacy of individuals. However, verifying that a particular program provides differential privacy often remains a manual task to be completed by an expert in the field. Language-based techniques have been proposed for fully automating proofs of differential privacy via type system design, however these results have lagged behind advances in differentially-private algorithms, leaving a noticeable gap in programs which can be automatically verified while also providing state-of-the-art bounds on privacy. We propose Duet, an expressive higher-order language, linear type system and tool for automatically verifying differential privacy of general-purpose higher-order programs. In addition to general purpose programming, Duet supports encoding machine learning algorithms such as stochastic gradient descent, as well as common auxiliary data analysis tasks such as clipping, normalization and hyperparameter tuning - each of which are particularly challenging to encode in a statically verified differential privacy framework. We present a core design of the Duet language and linear type system, and complete key proofs about privacy for well-typed programs. We then show how to extend Duet to support realistic machine learning applications and recent variants of differential privacy which result in improved accuracy for many practical differentially private algorithms. Finally, we implement several differentially private machine learning algorithms in Duet which have never before been automatically verified by a language-based tool, and we present experimental results which demonstrate the benefits of Duet's language design in terms of accuracy of trained machine learning models.
UAV Detection Employing Sensor Data Fusion and Artificial Intelligence

The purpose of this paper is to present a multi-sensorial detection method for discovering and obtaining characteristics of flying Unmanned Aerial Vehicles (UAVs) in restricted areas. Different solutions may be applied for this purpose: radio signals analysis, acoustic patterns analysis, video processing, IR imaging, RADAR, LIDAR etc. The new Concurrent Neural Networks (CNN) classification has been introduced as a collection of low-volume neural networks that perform parallel classification. In the present paper the identification and classification of drones is analyzed employing two CNNs, a multilayer perceptron (MLP) for acoustic pattern recognition and a self – organizing map (SOM) to recognize an object from a video stream.
Machine Learning with R, Third Edition (Book Review)

This book is highly recommended for anyone with previous programing experience who seeks a solid, grounded introduction to basic machine learning using the R statistical software. With nearly 100 a...
An Improvement of Gravitational Search Algorithm

The gravitational search algorithm (GSA) has the advantages of strong exploitation performance and fast convergence speed. However, the GSA is easy to appear premature phenomenon and get into the local optimum during the search process because the particle diversity declines during the optimization process and the particle swarm optimization information is not shared. Therefore, an improved GSA (IGSA) is proposed, which keeps the particle diversity by adjusting the gravitational constant and enhances particle swarm information sharing ability by imposing global optimal information into the search position of each particle. The proposed IGSA has been evaluated on 9 nonlinear benchmark functions and compared with standard GSA and particle swarm optimization (PSO). The obtained results confirm that the convergence accuracy of IGSA is several orders of magnitude higher than that of GSA, and the search speed is also increased by more than 2 times. In addition, a case study of optimizing generator operating costs of IEEE9 is carried, and the IGSA algorithm achieves lower operating costs and less network loss than that of GSA.
Within-Network Ensemble for Face Attributes Classification

Face attributes classification is drawing attention as a research topic with applications in multiple domains, such as video surveillance and social media analysis. In this work, we propose to train attributes in groups based on their localization (head, eyes, nose, cheek, mouth, shoulder, and general areas) in an end-to-end framework considering the correlations between the different attributes. Furthermore, a novel ensemble learning technique is introduced within the network itself that reduces the time of training compared to ensemble of several models. Our approach outperforms the state-of-the-art of the attributes with an average improvement of almost 0.60% and 0.48% points, on the public CELEBA and LFWA datasets, respectively.
Low-Complexity Scene Understanding Network

Multi-task networks often rely on complex architectures in order to perceive and understand the driving scene. Computationally intensive networks achieve state of the art results at the cost of real-time inference on embedded devices. Our proposed unified solution obtains competitive results on multiple tasks, while targeting an embedded platform. We build upon our previous work of performing low-complexity object detection and bottom point prediction and add semantic and instance segmentation tasks while maintaining 19 FPS on the NVIDIA Jetson TX2 embedded platform. We find that sharing layers between task sub-networks is essential for achieving real-time inference. Due to the task similarity and correlation between object detection, bottom point prediction, semantic segmentation and instance segmentation we find that the individual task performance is not greatly impacted by the reduced computational capacity resulted from sharing layers amongst the task sub-networks.
A Logging-in Scheme in Virtual Keyboard Protection from Shoulder Surfing and Screenshot Capturing

Passwords are widely used as authentication in a variety of applications in the light of cost, immediacy and convenience, even though numerous securer alternative authenticators come out. Through restricting the form of how they stored on local host, how they transmitted over network, and the number or the frequency of password guesses, the risks while using passwords can be mitigated. However, most existing measures are still vulnerable to physical security issues, such as shoulder surfing and keyloggers. This paper proposes a new protection scheme from deterring the threats to achieve the security in efficiency.
Transferable Neural Processes for Hyperparameter Optimization

Automated machine learning aims to automate the whole process of machine learning, including model configuration. In this paper, we focus on automated hyperparameter optimization (HPO) based on sequential model-based optimization (SMBO). Though conventional SMBO algorithms work well when abundant HPO trials are available, they are far from satisfactory in practical applications where a trial on a huge dataset may be so costly that an optimal hyperparameter configuration is expected to return in as few trials as possible. Observing that human experts draw on their expertise in a machine learning model by trying configurations that once performed well on other datasets, we are inspired to speed up HPO by transferring knowledge from historical HPO trials on other datasets. We propose an end-to-end and efficient HPO algorithm named as Transfer Neural Processes (TNP), which achieves transfer learning by incorporating trials on other datasets, initializing the model with well-generalized parameters, and learning an initial set of hyperparameters to evaluate. Experiments on extensive OpenML datasets and three computer vision datasets show that the proposed model can achieve state-of-the-art performance in at least one order of magnitude less trials.
Epistemic Multi-agent Planning Using Monte-Carlo Tree Search

Coordination in multi-agent systems with partial and non-uniform observability is a practically challenging problem. We use Monte-Carlo tree search as the basis of an implicitly coordinated epistemic planning algorithm which is capable of using the knowledge distributed among the agents to find solutions in problems even with a large branching factor. We use Dynamic Epistemic Logic to represent the knowledge and the actual situation as a state of the Monte-Carlo tree search, and epistemic planning to formalize the goals and actions of a problem. Further, we describe the required modifications of the Monte-Carlo tree search when searching over epistemic states, and make use of the cooperative card game Hanabi to test our planner on larger problems. We find that the approach scales to games with up to eight cards while maintaining high playing strength.
Particle Filtering for Tracking in 360 Degrees Videos Using Virtual PTZ Cameras

360 degrees cameras are devices able to record spherical images of the environment. Such images can be used to generate views of the scene by projecting the spherical surface onto planes tangent to the sphere. Each of these views can be considered as the output of a virtual PTZ (vPTZ) camera with specific pan, tilt and zoom parameters. This paper proposes to formulate the visual tracking problem as the one of selecting, at each time, the vPTZ camera to foveate on the target from the unlimited set of simultaneously generated vPTZ camera views. Assuming that the selected vPTZ camera is a stochastic variable, the paper proposes to model the posterior distribution of the underlying stochastic process by means of a set of particles each representing a vPTZ camera view. Experiments on a publicly available dataset show that the proposed tracking strategy is viable and achieves state-of-the-art performance.
Evolutionary Strategies of Intelligent Agent Training

Groups of interacting agents are able to solve complex tasks in a dynamic environment. Robots in a group can have a simpler device than single stand-alone robots. Each agent in the group has the ability to accumulate interaction experience with the environment and share it with other members of the group. In many cases, the group behavior is not deduced from any properties of its parts. The paper proposes an approach to modeling the mobile agent group behavior that is busy with a common goal. The main purpose of the agents is to study the greatest territory at minimal time. The agents interact with the environment. A control of each agent is carried out by a modified neural network with restrictions imposed on it. Weights of the neural network are chosen by a genetic evolution method. The agents compete among themselves for obtaining the greatest reward from the environment. An efficiency of the proposed model is confirmed by some convergence speed tests by computer simulation. The proposed model can be applied to a group of robots that perform search tasks in a real physical space.
Analysis of recognition of elementary and middle school teachers' digital literacy education

The purpose of this study is to examine how digital literacy is accepted in subject-matter curriculum, to identify how elementary and middle school teachers perceive and practice digital literacy education and to suggest implications for improvement of digital literacy education in subject-matter education. As a result of reviewing the subject-matter curriculum, it appeared that digital literacy contents have been accepted in them along with the trend of the time, but they have not been systematic. Teacher survey results showed that elementary and middle school teachers recognized the need for digital literacy education and relatively understood the changes in the 2015 revised curriculum, while its practice in class was not the equivalent level as the recognition and understanding. The middle school teachers were more passive than the elementary school teachers in terms of recognition and practice as well as the aspects of each subject were shown different. Thus, it is necessary to clarify what basis the implementation of the digital literacy education in the subject-matter curriculum has, to design learner-centered classroom instruction, to build the digital resource management and support system, or to coordinate the interdisciplinary system among teachers or different subject-matters.
Sonic characteristics of robots in films

Robots are increasingly becoming an integral part of our everyday life. Expectations on robots could be influenced by how robots are represented in science fiction films. We hypothesize that sonic ...
Hand Detection Using Zoomed Neural Networks

The object detection problem has been widely focused due to the development of personal cameras allowing the general population to have access to high end cameras. This has resulted in cameras with various perspectives, one of which is the Egocentric Perspective, like the GoPro cameras. This new perspective opens the possibility of having hand detection as a special problem, due to the hands containing enough information to be detected and even for hand recognition and users’s activity recognition. However, due to the perspective being new the databases are scarce, and most of them focus on generic object detection rather than hand detection. In this paper we address hand detection and hand disambiguation which focuses on detecting left and right hands as different objects. This paper addresses these challenges by using the information of a left hand being the mirror image of the right hand for the hand disambiguation, and we also train a Neural Network to focus on the hand over all the image and another Neural Network to focus on the bottom area of the image, increasing the resolution as the hands go out of image, which is a characteristic of the hands in the Egocentric Perspective. In addition, we propose three Neural Network architectures using the hand and increase resolution bottom image information, and we compare them with current object/hand detection approaches.