Reviewerly/exHarmony huggingface dataset:
Selective Perception as a Mechanism to Adapt Agents to the Environment: An Evolutionary Approach

Rapid advancement of machine learning makes it possible to consider large amounts of data to learn from. Learning agents may get data ranging on real intervals directly from the environment they interact with, in a process usually time expensive. To improve learning and manage these data, approximated models and memory mechanisms are adopted. In most of the implementations of reinforcement learning facing this type of data, approximation is obtained by neural networks and the process of drawing information from data is mediated by a shortterm memory that stores the previous experiences for additional relearning, to speed-up the learning process, mimicking what is done by people. In this paper, we are proposing a novel computational approach able to selectively filter the information, or cognitive load, for the agent's short-term memory, thus emulating the attention mechanism characteristic of human perception. In this work, we use genetic algorithms in order to evolve the most efficient attention filter mechanism that would be able to provide the agent with an optimal perception for a specific environment by discriminating which experiences are valuable for the learning process. This approach can evolve a filter which can able to provide an optimal cognitive load of the experiences entering in the agent's short-term memory of a limited capacity. The evolved sampling dynamics can also lead to the emergence of intrinsically motivated curiosity.
Ideals of Numerical Semigroups and&#x0D; Error-Correcting Codes

Several results relating additive ideals of numerical semigroups and algebraic-geometrycodes are presented. In particular, we deal with the set of non-redundant parity-checks, the codelength, the generalized Hamming weights, and the isometry-dual sequences of algebraic-geometrycodes from the perspective of the related Weierstrass semigroups. These results are related tocryptographic problems such as the wire-tap channel, t-resilient functions, list-decoding, networkcoding, and ramp secret sharing schemes.
Weakly-supervised large-scale image modeling for sport scenes and its applications

Image modeling towards sport scenes plays an important role in sport image classification and analysis. Traditional algorithms for sport image modeling required carefully hand-crafted features, which cannot be popularized in practical application, especially with the emergence of massive-scale data. Weakly-supervised learning algorithms have shown effectiveness in modeling data with image-level labels. Thus, in this paper, we propose a weakly-supervised learning based method for sport image modeling without utilizing bounding box annotations, which can be used for various sport image applications. More specifically, we first collect large-scale sport images from existing datasets and Internet, and we annotate them at image-level labels. Subsequently, we leverage region proposal generation algorithm to select discriminative regions that can effectively represent the category of images. Each region is fed into a pre-trained CNN architecture to extract deep representation. Afterwards, we design an improved multiple discriminant analysis (MDA) algorithm to project these datapoints to a subspace that can more easily to distinguish different sport categories. Comprehensive experiments have shown the effectiveness and robustness of our proposed method.
Robust L1-norm two-dimensional collaborative representation-based projection for dimensionality reduction

Collaborative representation-based projection (CRP) is a well-known dimensionality reduction technique, which has been proved to have better performance than sparse representation-based projection (SRP) in the fields of recognition and computer vision. However, classical CRP is sensitive to noises and outliers since its objective function is based on L2-norm, and it will suffer from the curse of dimensionality as it is used for images processing. In this paper, a novel CRP model, named L1-norm two-dimensional collaborative representation-based projection (L1-2DCRP) and an efficient iterative algorithm to solve it are proposed. Different from conventional CRP, the optimal problem in our proposed model is a L1-norm-based maximization and the vector data is extended to matrix date. The proposed algorithm is theoretically proved to be monotonously convergent, and more robust to noises and outliers since L1-norm is used. Experimental results on CMU Multi-PIE, COIL20, FERET and ORL face databases validate the effectiveness of L1-2DCRP compared with several state-of-the-art approaches.
Performance Analysis of Data Transmission on WebSocket for Real-time Communication

Real-time communication (RTC) is a telecommunications mode of text, image, audio, a video that allows all users to exchange information instantly or with a small latency value. The availability of RTC services is critical because the number of internet users has continued to increase in recent years. WebSocket is a full-duplex data transmission technology that can support RTC. In general, the implementation of WebSocket apply to web-based applications, but in this study applied to mobile applications. The resulting application is a screen sharing application between server and client, but it is not yet known the performance of RTC transmission data on applications that use WebSocket technology. Screen Sharing is a system designed to share the screen display of presentations between presenters and participants without using a projector. There are two main applications on this system, namely the Screen Sharing Server application and the Screen Sharing Client application. The measurement of the transmitted frame image and the size of the data consumed (bytes per second) between the server and client in the screen sharing process is the main focus of this study. The experimental results showed that after the Screen Sharing Server and Screen Sharing Client application was run for 20 seconds, we can find out that *.JPG is an image file type that has the highest average frame value transmitted at ten frames per second (fps), so you can support RTC. Whereas *.WEBP is an image file type that consumes the smallest data that is 44,970.6 (Kb) when the data transmission process is between the server and client.
Propagating trusted execution through mutual attestation

Attestation is used in a trusted computing context to verify the expected good behaviour. It defines a prover and verifier relationship. It requires the presence of an authority if any two devices want to question each other for a sensitive collaborative work. In contrast, the mutual attestation defined in this paper allows the same rank and type devices to directly evaluate each other, and manage their own cooperation. In addition, it provides the attestations with response actions so that a device with an untrusted state can be recovered with the help of others on the network. These features are supported with a Root-of-Trust (RoT) module capable of enforcing actions even on exploited devices. A proof-of-concept FPGA implementation of the proposed RoT module is demonstrated on Zynq SoCs, as an add-on extension to a RISC-V processor for establishing trust between applications of connected devices. Besides, it assists to explore the implementation decisions, abnormal execution situations, resource utilisation, and performance overheads.
Spectrally controlled interferometry for improved radius of curvature measurement

Spectrally controlled interferometry promises to increase accuracy at a reduced cost for radius of curvature measurements for spherical optics by removing the catseye position during measurement and exploiting the inherent ability to measure absolute distances without mechanical compensation.
Unsupervised Transfer Learning Using for Multi-Model Remote Sensing Data Classification

Traditional supervised classification has been successfully applied to remote sensing (RS) classification and thematic mapping. However, labeling data is usually time-consuming and expensive. Transfer learning (TL) is able to reduce the labeling labor by utilizing pre-existing labeled data (source domain). Most of the existing TL methods applied in remote sensing still need a few labeled samples in target domain (where to be classified) or interaction between the classifier and users. By transferring the data distributions between the source and target domain into a joint feature space (where the distribution discrepancy between the source and target domains is smaller than the original space), a novel unsupervised transfer learning framework is proposed in this paper. In our framework, robust remote sensing indices are extracted from Digital Surface Models (DSM) and the associated orthophoto to help us to estimate the conditional probability distribution of the target domain. After that, our proposed Dispersion-optimized Joint Distribution Alignment (DJDA) method is able to generate transformed feature representations of source and target feature sets through simultaneous reduction of the joint distribution discrepancy between two domains and optimizing the dispersion (both intra-class dispersion and inter-class dispersion). In our experiments, the proposed framework is able to improve the transferability between multi-model datasets (Orthophoto + DSM). The comparative experiments demonstrate that the proposed DJDA outperforms the compared state-of-the-art unsupervised domain adaptation methods on RS image classification.
Comparative Studies of Robot Navigation

At present, robot intelligence is an important direction for the development of robots. The intelligent robot is a highly self-planning, self-organizing and adaptive robot suitable for working in complex unstructured environments. Navigation technology is the core of intelligent robot research, and it is also the key technology for robots to achieve intelligence. Intelligent mobile robots can work safely and effectively only by knowing their own position, the position of obstacles in the work space, and the movement of obstacles. Therefore, the problem of navigation and positioning of the robot is particularly important. The main contents of robot navigation technology research include: navigation method, positioning method and multi-sensor information fusion technology research. This paper will discuss the robot navigation technology from these aspects, introduce the development and application of navigation technology, and finally forecast the development trend of robot navigation technology in the future.
Solution of Simple Prey-Predator Model by Runge Kutta Method

The research on simulating logistic equation and prey predator system of equation using Runge Kutta method has been done. In logistic equation, simulation is agreed to some characteristics of equation. The simple form of prey predator system of equation is taken to be simulated. Simulation shows some similar patterns on graph. The changing on constants produces a certain pattern on graph.
Prosodic word boundary detection from Bengali continuous speech

Detection of word boundaries in continuous speech is a tedious process due to the absence of a definite pause or silence in the word boundary position. Thus, continuous speech recognition is a very challenging task. However, the prosodic word boundaries, unlike the written word boundaries, can be predicted using the prosodic parameters of continuous speech. This paper proposes a method for detecting such prosodic word boundaries from Bengali continuous speech. Bengali is a bound-stress language, where stress is observed on the first syllable of a prosodic word. Empirical Mode Decomposition is applied to the logarithm of fundamental frequency (F0) contour of continuous speech to detect prosodic word boundaries. 200 Bengali readout sentences, read by ten speakers, are analyzed for the present work. An overall prosodic boundary detection accuracy of 88.05% is achieved, whereas precision and recall values are 90.73% and 88.31%, respectively, with f-score as 89.5. A prosodic word dictionary comprising 5031 prosodic words has been developed by analyzing 1526 Bengali sentences with the proposed prosodic word boundary detection method.
A pipelined hybrid recommender system for ranking the items on the display

In a session-based recommendation service, currently offered by many online companies including trivago, it is important to effectively incorporate user interactions into recommendations. However, a major challenge lies in the fact that both inter-session and intra-session contexts should be considered at the same time for recommendations to become effective. To address this issue, we propose a pipelined hybrid recommender system that considers the two contexts simultaneously via weighted summation of loss functions designed for the combination of a recurrent neural network (RNN) and a convolutional neural network (CNN). With the hybrid system, our team, OSI LAB, achieved the final score of 0.670167 and reached the 16th place in the RecSys Challenge 2019. Our source code is available from https://github.com/jhoon-oh/recsys2019challenge.
An Improved Facial Recognition Technique Using Scale and Rotation Invariant Statistical Moments

Forensic experts face substantial encumbrance in finding a criminal in electronic databases given a pencil sketch. The problem complexity greatly surmounts when the image database contains thousands or even millions of images. The general approach is to use the sketch images to find the nearest possible match present in the pre-populated database of images consisting of criminals or individuals and groups in their daily routines. The proposed system present an optimal approach to automate the review and search process based on various image analysis techniques. The mathematical computations on images to find the features help us in identifying the characteristics and features of the face. The dataset stored is pre-populated by feature vector corresponding to each image in image database for fast retrieval. Through the experimental run on all the competitive techniques along with the proposed one, it is concluded that the overall accuracy of the systems is 96.2%.
Towards Data-Driven Automatic Video Editing

Automatic video editing involving at least the steps of selecting the most valuable footage from points of view of visual quality and the importance of action filmed; and cutting the footage into a brief and coherent visual story that would be interesting to watch is implemented in a purely data-driven manner. Visual semantic and aesthetic features are extracted by the ImageNet-trained convolutional neural network, and the editing controller is trained by an imitation learning algorithm. As a result, at test time the controller shows the signs of observing basic cinematography editing rules learned from the corpus of motion pictures masterpieces.
Review-Based Sentiment Prediction of Rating Using Natural Language Processing Sentence-Level Sentiment Analysis with Bag-of-Words Approach

User’s opinion plays a vital role in the global world of Internet as they are given freedom to express their feedback. A lot of hidden information and feeling of the user are expressed in the words that he/she used. Extracting this hidden information can help the service industry to better serve the need as per the user acceptance and to outfit the competition in the market. Methodology of this paper is intended to define the rating of the review given by the user. Method of bag-of-words approach taking the sentiment score and magnitude of the sentence using natural language processing is applied. The scale of one to five is considered in this experimental classification on the data set of hotel reviews. The results have shown that around 60% of the ratings can be predicted and 40% unpredicted with the given review. The Data set used in this experimental analysis is the Datafiniti’s hotel reviews.
Management and Ownership

The advent of Industry 4.0 is impacting the current industrial production processes bringing important transformations. Sensing and data collection are increasing quickly, data management and data ownership have become a challenge for technology professionals. For many years, the industry had no concern about data ownership and over time many experts systems have been incorporated to the industrial operations, situation that, sometimes, make data access very difficult due to closed proprietary databases. On the other hand, the historians were always an unanimity choice for collecting, storing and analyzing time series data sources. However, with the growth of data importance and the emergence of big data and cloud computing, the role of historians has started to be questioned. This article presents a data strategy in the scenario of Industry 4.0 and proposes an architecture of coexistence between historians and new technologies related to big data and cloud computing emphasizing the importance of data ownership to the success of industry digital transformation process.
The Expert System “Pharmacy” for Determination of Availability and Conditions of Storage of Medicinal Products

The built-in expert system “Apteka” allows to establish the presence of a drug in the pharmacy, its suitability, and its suitability for the treatment of this type of diseases. This expert system provides for a number of factors (including Factor (`Original Medicines') and the like), the availability of a program to obtain information (instructions for medicines) from the Internet, and the formation of the results of the expert system in the form of a spreadsheet in the format “Microsoft Excel”.
Study on Diagnosis of Automotive Network

Modern Automobiles uses embedded technology wherein the devices like Engine, Ignition Systems, Gauges, Sensors, Actuators etc. are controlled by ECU (Engine Control Unit). As ECUs control multiple devices the network becomes complex. Due to the complexity it is difficult to identify where the fault is occurred. In order to identify the fault and diagnose the same certain diagnostic protocols are made use. This paper enlightens about these protocols and their results that are studied from various researchs.
Population Health Assessment Based on Entropy Modeling of Multidimensional Stochastic Systems

The article attempts to demonstrate the possible application opportunities of multidimensional stochastic systems entropy modeling in medicine on a specific example. An assessment of rural male population health state has been done by the criteria of “healthy”, “practically healthy”, and “diseased”. The entropy levels are determined for the parameters, characterizing the main risk factors for chronic non-communicable diseases, depending on the health group. For each group a single parameter with the largest contribution to the entropy of the population is highlighted, and the dependence degree of this parameter from the others is determined. It was concluded that with the deterioration of health state the population entropy increases. This growth is caused by the increase of randomness entropy. It is shown that with the help of the entropy model it was possible to quite confidently study population health simultaneously by several risk factors. This allows us to count on the successful application of the proposed approach in medicine, as well as in other spheres.
An Environment for Gestural Interaction with 3D Virtual Musical Instruments as an Educational Tool

This paper presents a finalized version of an environment intended for performance and gestural interaction with three-dimensional virtual musical instruments, developed as a part of a larger educational platform, the iMuSciCA workbench. The environment can employ either a Leap Motion or a Kinect sensor, and enables interaction with a variety of virtual musical instruments, namely virtual interpretations of a bichord, a xylophone, a drumming set, a guitar and an upright bass, by means of performing and recognizing hand gestures similar to the ones needed to play their physical counterparts. In order to showcase the usability of the platform in an educational context and measure its effectiveness, we designed a scenario, where the user tries to keep a steady rhythm while drumming. A usability study of the above scenario, involving 22 users, demonstrates that the audiovisual feedback can actually provide assistance to the user.
How Can We Balance Research, Participation and Innovation as HCI Researchers?

This paper reflects upon the growing expectation for HCI research projects to collaborate closely with partners in industry and civil society. Specifically, we suggest that this type of engagement is often prefigured around the agendas, needs and capacity of diverse research partners, which researchers must then carefully negotiate. We explore this by describing a case of a recent large UK research project called Creative Informatics, where our research and co-design activities are heavily influenced by the UK’s Industrial Strategy. As researchers just beginning to work on this project, we call attention to its initial challenges. By doing so, we invite future-oriented discussion about how existing and new research approaches - ranging from participatory design approaches to reflective frameworks - might evolve to meet the challenges of projects where industrial and social impact is equally important to research impact.
Visualizing Convolutional Neural Networks with Virtual Reality

Software systems and components are increasingly based on machine learning methods, such as Convolutional Neural Networks (CNNs). Thus, there is a growing need for common programmers and machine learning newcomers to understand the general functioning of these algorithms. However, as neural networks are complex in nature, novel presentation means are required to enable rapid access to the functionality. For that purpose, this paper examines how CNNs can be visualized in Virtual Reality. A first exploratory study has confirmed that our visualization approach is both intuitive to use and conductive to learning.
Fuzzy number conjoint method to analyse students’ perceptions on the learning of calculus

Abstract The fuzzy conjoint method (FCM) based on fuzzy set has been applied in many areas such as education, management and business. However, the FCM uses the fuzzy set to define the membership function of the linguistic values, which the score of weight and overall attribute for different degrees of confidence that cannot be obtained. Besides, the fuzzy set which is in discrete form does not clearly represent human judgement. Thus, in this study, a fuzzy number conjoint method (FNCM) based on triangular fuzzy number is developed. The fuzzy similarity measure based on distance, height and area was used in the procedure. The FNCM was implemented to analyse students’ perception in the learning of calculus at one selected government institution in Selangor, Malaysia. The study involved a set of 5-options Likert scale questionnaire which is in linguistic values of strongly disagree, disagree, indifferent, agree and strongly agree. All the questions were on various items related to the learning of calculus. The findings showed that overall, students agreed at above 0.85 degree of similarity that they perceived positively in the learning of Calculus. The developed FNCM with a degree of agreement for each linguistic value can successfully evaluate the students’ perceptions in the learning of calculus. This study will be as assistance and guidance to academicians and mathematics educators to enhance students’ achievement in the learning of calculus.