Reviewerly/exHarmony huggingface dataset:
Contextual-Cueing beyond the Initial Field of View—A Virtual Reality Experiment

In visual search, participants can incidentally learn spatial target-distractor configurations, leading to shorter search times for repeated compared to novel configurations. Usually, this is tested within the limited visual field provided by a computer monitor. While contextual cueing is typically investigated on two-dimensional screens, we present for the first time an implementation of a classic contextual cueing task (search for a T-shape among L-shapes) in a three-dimensional virtual environment. This enabled us to test if the typical finding of incidental learning of repeated search configurations, manifested by shorter search times, would hold in a three-dimensional virtual reality (VR) environment. One specific aspect that was tested by combining virtual reality and contextual cueing was if contextual cueing would hold for targets outside the initial field of view (FOV), requiring head movements to be found. In keeping with two-dimensional search studies, reduced search times were observed after the first epoch and remained stable in the remaining experiment. Importantly, comparable search time reductions were observed for targets both within and outside of the initial FOV. The results show that a repeated distractors-only configuration in the initial FOV can guide search for target locations requiring a head movement to be seen.
How Blockchain Affects the Technological Strategy of the Financial Industry: An Analysis Based on Knowledge Discovery in Text

Purpose – The main objective of this article is to investigate how blockchain is affecting the technological strategy of the financial industry.Theoretical framework – Our theoretical framework is structured based on four approaches: Blockchain; Blockchain Advantages; Technology Strategy; Blockchain Technology Strategy.Design/methodology/approach – We conducted an exploratory study using knowledge discovery in texts (KDT), with the support of text mining tools. We collected and analyzed contents of a set of news published on the Web about Blockchain. A total of 2,605 blockchain web news items were extracted from the business media worldwide, such as Forbes, Fortune, Financial Post and Business World. We explored the text dataset using sentiment analysis, opinion mining, text association analysis, and correspondence analysis.Findings – The results showed that blockchain technology has the potential to affect the transactions through a common platform. Additionally, the results demonstrated the advantages of blockchain technology for cross-border payment systems.Research, Practical &amp; Social implications – We elaborate four theoretical propositions that may guide research and help practitioners to identify challenges and opportunities for blockchain technology.Originality/value – Our study revealed how blockchain has been affecting the technology strategy of the financial industry and highlighted two starting trajectories: the establishment of private blockchain and its application in the cross-border payment services.
An Enhanced Multimedia Video Surveillance Security Using Wavelet Encryption Framework

Multimedia digital data include medical record and financial documents, which are not guaranteed with security. The concerns for security of multimedia digital data is been a widespread issue in the field of cybernetics. With increasing malwares in video payloads, the proposed study aims to reduce the embedding of malwares using Pseudo Arbitrary Permutation based Cellular Automata Encryption (PAP-CAE) System in video payloads. This method reduces the malware attacks and distortion rate by permuting the secret keys with Pseudo arbitrary permutation. Before the application of PAP-CAE, 2D wavelet transform is applied on the multimedia files that compresses the complex files into different scales and position to be transmitted via a network with reduced size. Simultaneously, it performs the process of decryption and decompression to retrieve the original files. The proposed method is evaluated against existing methods to test its efficacy in terms of detection accuracy, detection time of malwares and false positive rate. The result shows that the proposed method is effective against the detection of malwares in multimedia video files.
Multipedestrian Online Tracking Based on Social Force-Predicted Deformable Key-Points Mapping via Compressive Sensing

Multipedestrian Online Tracking and positioning are the research hotspots and difficulties in the field of computer vision. In this article, we propose a multipedestrian online tracking method based on social force-predicted deformable key-points mapping and compressive sensing. First, part affinity fields are utilized to detect body key-points of pedestrians, and then rectangle boundaries of moving pedestrians can be constrained based on these key-points, the outer rectangle of the key-points replacing the conventional bounding box. Since pedestrian tracking is vulnerable to occlusion and dynamic deformation, a deformable convolution network is applied to extract delicate features. There is one more point, based on the theory of compressive sensing, a very sparse measurement matrix is designed to efficiently compress the features for the pedestrians, which maps features from high-dimensional space to low-dimensional space and preserves the structure of the image feature space of pedestrians. Compressed features are used to measure the similarity between pedestrians in adjacent frames. Furthermore, a pedestrian social force model is adopted, which can predict the trajectory of pedestrians to deal with the problems of partial occlusion and similar appearance, meanwhile making the estimated location more accurately. Finally, extensive experiments on the MOT16 benchmark demonstrate the superior performance of the proposed tracking method. The main tracking performance parameters are significantly improved, with multiobject tracking accuracy and multiobject tracking precision reaching 62.8% and 81.2%, respectively.
The Complexity of Interactively Learning a Stable Matching by Trial and Error

In a stable matching setting, we consider a query model that allows for an interactive learning algorithm to make precisely one type of query: proposing a matching, the response to which is either that the proposed matching is stable, or a blocking pair (chosen adversarially) indicating that this matching is unstable. For one-to-one matching markets, our main result is an essentially tight upper bound of O(n2 log n) on the deterministic query complexity of interactively learning a stable matching in this coarse query model, along with an efficient randomized algorithm that achieves this query complexity with high probability. For many-to-many matching markets in which participants have responsive preferences, we first give an interactive learning algorithm whose query complexity and running time are polynomial in the size of the market if the maximum quota of each agent is bounded; our main result for many-to-many markets is that the deterministic query complexity can be made polynomial (more specifically, O(n3 log n)) in the size of the market even for arbitrary (e.g., linear in the market size) quotas.
Prediction of Agro Products Sales Using Regression Algorithm

This study aimed at developing a system using support vector machine (SVM) that will forecast sales of farm products for an agricultural farm so that managers can take strategic decisions timely to better market the excess farm products which some by nature are perishable.The sales prediction model used SVMs and Fuzzy Theory.The implementation was done using Python Programming Language.The system comprised of three (3) modules: web interface, flask and the SVM Framework.To evaluate the result of the SVM model, the RBF neural network was used as a benchmark.Data of previous sales records from University of Agriculture Makurdi (UAM) farm was used to train and test the system.After training the network with data which covered the time period from 21st January, 2017 to 30th June, 2019, the remaining data which covered from 1st July 2019 up to the 31st December, 2019 was used to test and validate the forecasting performance of the system.The Forecasting Precision (FP) value for the SVM was 96.75% and that of the RBF neural network forecasting value was 90.55%.Analysis from the results shows that the forecasting system with SVM had a greater precision in the sales of agricultural products.
Learning and Learning-to-Learn by Doing: An Experiential Learning Approach for Integrating Human Factors into Maritime Design Education

There is a growing awareness in the marine industry that human factors need to be considered in ship design if seafarers are to operate ships and systems safely and effectively. However, according to literature, the ship design practice today does not show explicit consideration of the end-users. This is mostly due to the ship designers’ lack of awareness and understanding about Maritime Human Factors (HF), Human Centred Design (HCD), and the operational issues that ships’ crew are facing during their sea time. This lack of knowledge can be traced back to the current educational system, which does not provide knowledge on HF, HCD approach, and maritime HF issues.&#x0D; &#x0D; The authors conducted this study aiming to integrate HF and HCD knowledge into maritime design students’ educational platform and to motivate them to utilise this knowledge in their designs. Naval architecture students at the Australian Maritime College (AMC) were the participants of this study. Firstly, a classroom survey was conducted to determine the students current level of awareness and understanding of maritime HF and HCD. Then, an onboard survey was conducted after five HF-related activities during a seven-day voyage onboard MV Bluefin, which is the research vessel of AMC. The onboard activities provided the students with an opportunity for experiencing experiential learning including all its key elements based on Kolb’s experiential learning model.&#x0D; &#x0D; The findings demonstrate how students’ onboard experiences influence their learning process by doing, discovering, reflecting, and applying. Students’ understanding of HF issues and the importance of the HCD approach was influenced during the onboard activities. They learned HF theoretical knowledge as an essential addition to their undergraduate curriculum. The results further highlight the experiential learning as a “paradigm of noteworthy learning” that supports multiple learning objectives for learners, including shaping their knowledge through experience.
SMART CITY SECURITY: FACE-BASED IMAGE RETRIEVAL MODEL USING GRAY LEVEL CO-OCCURRENCE MATRIX

Nowadays, a lot of images and documents are saved on data sets and cloud servers such as certificates, personal images, and passports. These images and documents are utilized in several applications to serve residents living in smart cities. Image similarity is considered as one of the applications of smart cities. The major challenges faced in the field of image management are searching and retrieving images. This is because searching based on image content requires a long time. In this paper, the researchers present a secure scheme to retrieve images in smart cities to identify wanted criminals by using the Gray Level Co-occurrence Matrix. The proposed scheme extracts only five features of the query image which are contrast, homogeneity, entropy, energy, and dissimilarity. This work consists of six phases which are registration, authentication, face detection, features extraction, image similarity, and image retrieval. The current study runs on a database of 810 images which was borrowed from face94 to measure the performance of image retrieval. The results of the experiment showed that the average precision is 97.6 and average recall is 6.3., Results of the current study have been relatively inspiring compared with the results of two previous studies.
Developing an Integrated Model of Speech Entrainment

Entrainment, the phenomenon of conversational partners’ speech becoming more similar to each other, is generally accepted to be an important aspect of human-human and human-machine communication. However, there is a gap between accepted psycholinguistic models of entrainment and the body of empirical findings, which includes a large number of unexplained negative results. Existing research does not provide insights specific enough to guide the implementation of entraining spoken dialogue systems or the interpretation of entrainment as a measure of quality. A more integrated model of entrainment is proposed, which looks for consistent explanations of entrainment behavior on specific features and how they interact with speaker, session, and utterance characteristics.
ICT Facilities and Literacy in Rural Non-Government Secondary School Libraries of Bangladesh

The study explored the ICT facilities and services at rural Non-Government Secondary (NGS) school libraries in Bangladesh. It identified the ICT literacy of NGS school library professionals known as assistant librarians. An exploratory method was used to ensure the best possible outcomes. Based on the interview and literature review in the qualitative phase, a questionnaire was developed for the quantitative phase and collected 86 responses using an online survey. Findings showed, most of the rural NGS school libraries do not have the ICT facilities and of those that do, they are insufficient in number to provide efficient services to library users. There was a lack of ICT skills among assistant librarians and most agreed that ICT literacy would increase the efficiency of their work and regarded it as an essential tool for school libraries. The study provides an analysis of the prevailing situation that helps in planning for policy makers and further in-depth research.
Emotional Intelligence in Teachers’ Activities

Emotional intelligence (EQ) development is becoming a more important issue among such significant factors as competence and efficiency due to the constant and rapid social transformations, new challenges, high social norms, and setting high professional standards.The ability to control emotions and recognize them is especially important for a future teacher who faces two objectives that make a success or failure when establishing communication: developing the personal abilities that provide a basis for emotional intelligence and developing students' emotional intelligence.A future teacher should be involved in searching for new approaches and tools for understanding and controlling emotions, as well as developing the ability to empathize.The aim of this research is to help a teacher acquires effective skills to build optimal relationships with colleagues and students.This search is also due to the need to develop the skills to exercise self-knowledge, understand the motives and goals in a professional teaching environment, to influence the emotions of other people, and to improve the leadership qualities.The article describes the results of an experiment carried out at the Faculty of Pedagogy and Psychology of Naberezhnye Chelny State Pedagogical University and at the Faculty of Psychology and Pedagogy of Nizhny Novgorod State Pedagogical University named after K. Minin in 2019.The experiment was aimed at measuring the development level of students' emotional intelligence that affects the quality of various aspects of professional activity.The authors discuss the main criteria for assessing the level of future teachers' emotional intelligence and give characteristics of each criterion.We have come to the conclusion that developed skills of emotional competence are of great importance to teachers and children, and to the successful social interaction.The findings of the research can be applied when working out the programs to develop emotional intelligence in order to resolve conflicts and forecast their consequences in a professional teaching environment.
Features of software testing in the development of geographic information systems

In this article, the method of selecting a method of testing geographic information systems is proposed. Nowadays, thanks to advances in information technology new methods of data exchange, positioning technologies, improvements in spatial databases, there has been a significant leap in development of geographic information system. When choosing a testing method, it is recommended to take into account not only the financial component, but also the specific characteristics of the tested software system. A list of questions is developed by which qualitative properties of software systems are formalized. The list of questions is based on the ISO/IEC 25010:2011 model. The relevance of this work follows from the fact that new technologies of software development require new approaches to testing software systems. Properly developed testing technique improves the quality of geographic information systems created. Improving quality increases the competitiveness and demand of software products.
NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm (Extended Abstract)

Convolutional neural networks (CNNs) are the backbones of deep learning paradigms for numerous vision tasks. Early advancements in CNN architectures are primarily driven by human expertise and elaborate design. Recently, neural architecture search (NAS) was proposed with the aim of automating the network design process and generating task-dependent architectures. This paper introduces NSGA-Net -- an evolutionary search algorithm that explores a space of potential neural network architectures in three steps, namely, a population initialization step that is based on prior-knowledge from hand-crafted architectures, an exploration step comprising crossover and mutation of architectures, and finally an exploitation step that utilizes the hidden useful knowledge stored in the entire history of evaluated neural architectures in the form of a Bayesian Network. The integration of these components allows an efficient design of architectures that are competitive and in many cases outperform both manually and automatically designed architectures on CIFAR-10 classification task. The flexibility provided from simultaneously obtaining multiple architecture choices for different compute requirements further differentiates our approach from other methods in the literature.
Evaluating and Validating Cluster Results

Clustering is the technique to partition data according to their characteristics.Data that are similar in nature belong to the same cluster [1].There are two types of evaluation methods to evaluate clustering quality.One is an external evaluation where the truth labels in the data sets are known in advance and the other is internal evaluation in which the evaluation is done with data set itself without true labels.In this paper, both external evaluation and internal evaluation are performed on the cluster results of the IRIS dataset.In the case of external evaluation Homogeneity, Correctness and V-measure scores are calculated for the dataset.For internal performance measures, the Silhouette Index and Sum of Square Errors are used.These internal performance measures along with the dendrogram (graphical tool from hierarchical Clustering) are used first to validate the number of clusters.Finally, as a statistical tool, we used the frequency distribution method to compare and provide a visual representation of the distribution of observations within a clustering result and the original data.
A Neuron Noise-Injection Technique for Privacy Preserving Deep Neural Networks

Abstract Data is the key to information mining that unveils hidden knowledge. The ability to revealed knowledge relies on the extractable features of a dataset and likewise the depth of the mining model. Conversely, several of these datasets embed sensitive information that can engender privacy violation and are subsequently used to build deep neural network (DNN) models. Recent approaches to enact privacy and protect data sensitivity in DNN models does decline accuracy, thus, giving rise to significant accuracy disparity between a non-private DNN and a privacy preserving DNN model. This accuracy gap is due to the enormous uncalculated noise flooding and the inability to quantify the right level of noise required to perturb distinct neurons in the DNN model, hence, a dent in accuracy. Consequently, this has hindered the use of privacy protected DNN models in real life applications. In this paper, we present a neuron noise-injection technique based on layer-wise buffered contribution ratio forwarding and ϵ-differential privacy technique to preserve privacy in a DNN model. We adapt a layer-wise relevance propagation technique to compute contribution ratio for each neuron in our network at the pre-training phase. Based on the proportion of each neuron’s contribution ratio, we generate a noise-tuple via the Laplace mechanism, and this helps to eliminate unwanted noise flooding. The noise-tuple is subsequently injected into the training network through its neurons to preserve privacy of the training dataset in a differentially private manner. Hence, each neuron receives right proportion of noise as estimated via contribution ratio, and as a result, unquantifiable noise that drops accuracy of privacy preserving DNN models is avoided. Extensive experiments were conducted based on three real-world datasets and their results show that our approach was able to narrow down the existing accuracy gap to a close proximity, as well outperforms the state-of-the-art approaches in this context.
An Efficient Hybrid Load Balancing Algorithm for Heterogeneous Data Centers in Cloud Computing

Cloud computing has recently grown into a major global trend of computing.Using the Internet and Wide Area Network (WAN) to make services remotely is a modern design.This is a new solution and technique to achieve high availability, versatility, cost savings and demand-scalability.However, cloud computing faces many problems, such as wasteful resource use, which has a major effect on the performance of cloud computers.These issues have to be managed from time to time to avoid the utilization factors of the various attributes that are used while implementing the process.Because of the enormous amount of knowledge these issues emerged and are unaddressed for so many years even though they were just adjusted in between to carry out the normal activities.Therefore, one of the most critical issues in this area in improving cloud computing performance is the need for robust and efficient load balancing algorithms for cloud computing.Many researchers have proposed different load balancing and job scheduling algorithms in cloud computing, but system efficiency is still very unstable and load still unbalance.This has subsequently delayed the process of executing the algorithms within the required timelines.Hence, in this research, we propose a load balancing algorithm to improve performance and efficiency in a heterogeneous cloud computing environment.We propose a hybrid algorithm that utilizes both random and greedy algorithms.The algorithm takes into account the current resource data and the CPU capacity factor to attain the objectives.The hybrid algorithm was tested using Cloud Analyst simulator, and compared with other algorithms.This comparison has been carried out both in a subjective way and also objective way to establish the proposed method.The experimental investigations showed improvements in average response time and processing time by taking current resource information and the CPU capacity factor compared to other algorithms into consideration.