Reviewerly/exHarmony huggingface dataset:
Artificial Subjectivity: Personal Semantic Memory Model for Cognitive Agents

Personal semantic memory is a way of inducing subjectivity in intelligent agents. Personal semantic memory has knowledge related to personal beliefs, self-knowledge, preferences, and perspectives in humans. Modeling this cognitive feature in the intelligent agent can help them in perception, learning, reasoning, and judgments. This paper presents a methodology for the development of personal semantic memory in response to external information. The main contribution of the work is to propose and implement the computational version of personal semantic memory. The proposed model has modules for perception, learning, sentiment analysis, knowledge representation, and personal semantic construction. These modules work in synergy for personal semantic knowledge formulation, learning, and storage. Personal semantics are added to the existing body of knowledge qualitatively and quantitatively. We performed multiple experiments where the agent had conversations with the humans. Results show an increase in personal semantic knowledge in the agent’s memory during conversations with an F1 score of 0.86. These personal semantics evolved qualitatively and quantitatively with time during experiments. Results demonstrated that agents with the given personal semantics architecture possessed personal semantics that can help the agent to produce some sort of subjectivity in the future.
Solving narrow Konane boards

In this paper we investigate the game of Konane, using Combinatorial Game Theory and game-specific solving strategies. We focus on narrow rectangular boards ( m × n boards with m ⩽ 4). These are dubbed as Linear Konane, Double Konane, Triple Konane, and Quadruple Konane, respectively. The initial board contains black and white stones in a checkered pattern, with a gap of two adjacent empty squares to enable moving. Only capture moves are possible. Depending on the exact location of the initial gap (the setup) we have four classes of initial Konane boards (two for Linear Konane), namely all combinations of a horizontal or vertical setup in the middle of the board or at a corner. For solving narrow Konane boards two notions proved very useful. First, we define moves that cannot be prevented by the opponent as safe moves of a player. Second, two fragments are independent if there is no way they can ever interact. Using these two notions Linear and Double Konane have been completely solved. Triple Konane was solved except for the horizontal corner setup. For Quadruple Konane only the vertical setup in the middle of the board was solved.
A Multidimensional Data Mining Approach for Video Analysis and Ranking System

YouTube is the most popular comprehensive online video source where continuous real-time video uploading is done by users throughout the world at a tremendous rate. It has become the most popular streaming media where users’ interaction is observed in terms of sharing, commenting, and rating the videos. Generally, these ratings represent the relevancy, validity, quality, and popularity of the videos. The comments made by the viewers are also a deciding factor for the new viewers to have an idea about the relevance and the quality of the videos. Hence the proper analysis of the sentiments exhibited in the comments can indicate the nature of the videos. Sometimes in the user’s search result, a low quality, irrelevant, and inappropriate video content is often ranked higher due to the large number of views that seem untenable. In this work, a Natural Language Processing (NLP) framework based on sentiment analysis of the comments is presented to minimize this issue. Based on the positive and negative sentiments presented in the user comments videos are given a measure which is termed as polarity factor. Finally, the video impact measure is calculated based on the Polarity factor together with like-dislike and view counts. This study assists in mining the most appropriate and popular videos on YouTube based on the search key. A data-driven experiment has been done here to prove the effectiveness and accuracy of the proposed system.
Privacy-Preserving kNN Spatial Query Using Voronoi Diagram

With the increased demand for outsourcing databases, there is a demand to enable secure and efficient communications. The concern regarding outsourcing data is mainly providing confidentiality and integrity to the data. This paper proposes a novel solution to answering kNN queries at the cloud server over encrypted data. Data owners transform their data from a native domain to a new domain to assist in nearest neighbors’ classification. The transformation is achieved by the Voronoi diagram, which transforms the data space into numerous small regions, simplifying the nearest neighbor search. However, because the regions that make up a Voronoi diagram are irregularly shaped, the search through the network becomes hard to accomplish. Thus, the solution includes a Grid-based indexing approach for the Voronoi diagram to expedite the kNN search. Additionally, a strong encryption algorithm, like AES, is used to encrypt the data objects being sent from the data owner to the cloud. The cloud service provider utilizes the proposed indexing scheme to identify a superset of the nearest neighboring objects to be sent back to the user. An authorized user wants to send encrypted kNN queries to the cloud where the query is processed over encrypted data. However, the user should not have a copy of the encryption key. Therefore, Trusted Party or a Trusted Machine (Query Proxy) was added that handles the encryption and decryption of the communication between the user and the service provider.
Review of Extrinsic Plagiarism Detection Techniques and Their Efficiency Comparison

Plagiarism can be defined as stealing or copying the work of other researchers/authors and presenting it as your own without referencing or acknowledging their work. In this paper various types of extrinsic plagiarism detection techniques have been discussed which are based on Linguistic features, Semantic role labeling, Context matching and Word embeddings, Multi- agents indexing system, Syntactic and Semantic similarity. We have also explored Deep Learning and Machine Learning based plagiarism detection techniques which are based on SVM, Genetic algorithm, CNN & RNN, Logical regression model. These techniques have been implemented on different datasets PAN-PC-(2009–2016), AraPlagDet, Plagiarized Short Answers, SemEval 2015 Twitter and Microsoft Paraphrase corpus. In this paper we have also compared these techniques on the standard measures that are Precision, Recall, F1 Score, PlagDet and Granularity and we have also discussed the pros and cons of these plagiarism detection techniques.
The Determination of Key Competencies for Managers in Russian High-Tech Companies in the Context of the Transition to a New Technological Paradigm

A competency-based approach application in management is determined by the acceleration of innovative processes, the complexity of the technologies used, flexibility in a dynamic external environment. In this context the availability of resources and the possibility of their usage are of high importance in order to ensure the organisation sustainability and competitiveness. The innovative potential of high-tech enterprises is based on the most complex equipment, high technologies and high- qualified human resources. Therefore, the task of personnel competencies formation and development is urgent at the enterprises. In the study problems were identified in the personnel training and professional development of companies-technological leaders. For this purpose, a managerial personnel survey was conducted in 60 leading Russian technology companies. The core digital, professional, personal and supra-professional competences were also identified, which are of paramount importance in enterprise management in the context of the transition to a new technological order. Based on the study results, a competency model and fundamentally new approaches to assessing the formation of the enterprise competence model will be developed. Also, the results obtained can be used in the development of the concept of “lifelong learning”, namely, when working out the organizational aspects of lifelong learning elements application at the enterprise level.
Quantifying Dominance Data

We have discussed many problems related to quantification of contingency tables and multiple-choice data. The main theme was quantification with optimality and symmetry. In this context, we noted a recommendation for changing the domain of quantification from total space to symmetric space, called dual space. As we will find out, dominance data are mostly row-conditional. Therefore, symmetric quantification space will be outside the purview of dominance data. Yet, we will still see mathematical symmetry in the quantified dominance data. As mentioned earlier, dominance data are characterized by ordinal information of such responses as greater than, equal to, less than. This aspect of the data complicates the procedure of quantification—what should we do about ordinal information? This was the reason why we designated dominance data as “quasi-domain” for quantification. We will look at this unique characteristic of data as reflected on quantification of the data.
Mixed-dual-head Meets Box Priors: A Robust Framework for Semi-supervised Segmentation

As it is costly to densely annotate large scale datasets for supervised semantic segmentation, extensive semi-supervised methods have been proposed. However, the accuracy, stability and flexibility of existing methods are still far from satisfactory. In this paper, we propose an effective and flexible framework for semi-supervised semantic segmentation using a small set of fully labeled images and a set of weakly labeled images with bounding box labels. In our framework, position and class priors are designed to guide the annotation network to predict accurate pseudo masks for weakly labeled images, which are used to train the segmentation network. We also propose a mixed-dual-head training method to reduce the interference of label noise while enabling the training process more stable. Experiments on PASCAL VOC 2012 show that our method achieves state-of-the-art performance and can achieve competitive results even with very few fully labeled images. Furthermore, the performance can be further boosted with extra weakly labeled images from COCO dataset.
Quantum Attention Based Language Model for Answer Selection

Attention mechanism originally introduced for machine translation has a wide application in NLP tasks. By attending more important data with higher wight, the mechanism has the potential to improve neural networks’ performance. Meanwhile, along with the pursuit of higher performance is the challenge of neural networks’ interpretability. Based on quantum probability theory, quantum language model is just such an attempt and has drawn increasing attention. In this paper, we intend to investigate a balance between model’s performance and interpretability, and propose a quantum attention based language model. Density matrix which carries the appearance probability of any word is used to construct quantum attention. Applied in a typical Question Answering task—Answer Selection, our model shows an effective performance on TREC-QA and WIKI-QA datasets.
Learning at home for Grade 1 learners in disadvantaged communities: Insights from the Sandbox@Home COVID-19-response intervention

Background: The spread of the coronavirus disease-2019 (COVID-19) has escalated the need for studying the home learning environment (HLE). With learners spending more time at home, understanding about learning at home, especially in disadvantaged communities, is paramount.Aim: The aim of this research study was to explore the HLE for foundation phase learners by analysing data from an intervention that aspired to support organised learning at home during the COVID-19 pandemic.Setting: This research study was conducted telephonically with families from 11 schools (10 in the Waterberg district, Limpopo and one in Soweto, Gauteng).Methods: Qualitative data (recorded phone interviews) were collected from caregivers and learners from 17 households. The interview recordings were transcribed and translated into English. The constant comparative method of data analysis was used to analyse interview transcripts.Results: Three themes emerged from the analysis: (1) challenges of learning at home, (2) perceived benefits of learning at home and (3) the desire to continue with the programme even after schools re-opened. The data showed that having access to learning material facilitated learning at home. Additionally, caregivers noted benefits to learners from engaging in learning activities. However, there were also challenges in facilitating learning at home. These include caregivers not following the learning guidelines provided.Conclusion: Caregivers appreciated having access to learning material and were willing to facilitate learning at home. However, the challenges they faced resulted in them not executing the programme effectively. Future studies and interventions should explore how to provide adequate and accessible support to caregivers.
Image Enhancement Algorithm for Remote Surveillance Using Low Cost Cameras in Unevenly Illuminated Environments

The surveillance domain is becoming increasingly popular due to the demand for security and safety. In terms of the data being used, the cameras linked to them play an essential role. However, in poorly lit environments, the acquired image may appear to be underexposed when the camera adjusts itself to the light available in the scene, resulting in a loss of visual information in poorly lit regions. This could result in poor functionality in image based processing, as well as a negative impact on remote surveillance monitoring activities. This study offers a novel algorithm for improving the illumination of scenes that are unevenly lit using a selective pixels processing method to enhance the illumination of the environment. Rather than processing grayscale versions of red,green and blue channels to enhance the image, this approach uses value (v) channel of an HSV image.
Statistical Movement Classification based on Gram-Schmidt Transform

In this work, a technique for automatic classification of finger motion signals was introduced by using Gram-Schmidt orthonormalization. Because the motion signal has nonlinear and non-stationary processing characteristics, the Gram-Schmidt orthonormalization method is used to analyze the motion signal. By batch processing the high correlation between variables, the classification results are obtained. At the same time, combined with the idea of partial least squares, a new identity measure is proposed to select the best parameters. Finally, SVM is used to classify the movement types of the research objects. According to the test sample records of two finger motion signals (thumb and pinky), the average accuracy is 96.65&#x0025;. The average accuracy of data activation (thumb and pinky) was 100&#x0025;. Experiments show that the operation speed of Gram-Schmidt classification method is significantly improved, and it has better interpretability and prediction ability.
Using Q-Learning to Personalize Pedagogical Policies for Addition Problems

The prevalence of COVID-19 has illuminated the need for practical digital education tools over the past year. With students studying from home, teachers have struggled to provide their students with adequately challenging coursework. Our project aims to solve this issue in the context of math. More specifically, our goal is to encourage thoughtful learning by supplying students with personalized two-number addition problems that take time to solve but expect the student to answer correctly still. Our solution is to model the process of selecting a math problem to give a student as a Markov Decision Process (MDP) and then use Q-learning to determine the best policy for arriving at the most optimally challenging two-number addition problem for that student. The project creates three student simulators based on group member data. We show that it took student one: $(162 \pm 134)$ iterations to give appropriate level problems where the first entry is mean and the second is the standard deviation. Student two took $(230 \pm 205)$ iterations, and student three took $(247 \pm 236)$ iterations. Lastly, we demonstrate that pre-training our model on students two and three and testing on student one showed a significant improvement from $(162 \pm 134)$ iterations to $(35 \pm 44)$ iterations.
Analyzing Digital Advertisement Effectiveness using Image Processing Model with Person’s Attentiveness

In recent days, the revenue generated by different advertising mediums is massive, in particular the revenue generated through digital advertisement is approximately 200 billion dollars worldwide in 2020. The revenue generated from these ads often depends on the watch time of the ad and/or the user involvement in the ad. Number of viewers clicking the skip button is an important statistic that helps to determine the quality of the ad. If the ad is appealing to the viewers, it grabs the user’s interest and attention in the first few seconds to increase the watch time, making the user watch the ad completely. A person is more likely to buy the product or service being advertised if he finds the advertisement interesting. An advertisement should provide an insight into the key aspects of the product which will enable the target customer to decide whether he wants to use the product or service. More viewers are taking control by skipping advertisements they don’t want to see. Hence companies must take actions to ensure that theirs reach the target audience, with the right blend of reach and relevance. Ad effectiveness offers a solution to the companies looking to understand the impact of their ads on the audiences they want to influence. Hence, the effectiveness of the advertisement is very important to sell to the target audience. To aid companies in measuring the effectiveness, a tool/application which detects and analyses people’s attentiveness to the advertisement would be of great help in making advertisements that are engaging and appealing to the general public. In this paper, an image processing model is proposed to implement eye gaze and head pose tracking system, to analyze the person’s attentiveness towards the ad. This proposed approach can be used and analyzed by the advertising industry to determine the effectiveness of their advertisement in selling their product or service to the target audience.
A New Fast Intersection Algorithm for Sorted Lists on GPU

Set intersection algorithms between sorted lists are important in triangles counting, community detection in graph analysis and in search engines where the intersection is computed between queries and inverted indexes. Many researches use GPU techniques for solving this intersection problem. The majority of these techniques focus on improving the level of parallelism by reducing redundant comparisons and distributing the workload among GPU threads. In this paper, we propose the GPU Test with Jumps (GTWJ) algorithm to compute the intersection between sorted lists using a new data structure. The idea of GTWJ is to group the data, of each sorted list, into a set of sequences. A sequence is identified by a key and is handled by a thread. Intersection is computed between sequences with the same key. This key allows skipping data packets in parallel if the keys do not match. A counter is used to avoid useless tests between cells of sequences with different lengths. Experiments on the data used in this filed show that GTWJ is better in terms of execution time and number of tests.
Using artificial intelligence search in solving the camera placement problem

Due to the impact of optimal camera placement on the efficiency and the cost of surveillance systems as well as the rapid development in sensor technologies and the pressing security needs, the last two decades witnessed an increasing interest in developing and introducing efficient methods for solving what is known as the camera placement problem. Given some monitoring quality measures coupled with the specifications of the visual sensors in hand, the goal of the camera placement framework is to capitalize the area seen by a set of visual sensors. This problem is considered a discrete optimization problem and is known to have an NP-hard problem complexity. In order to solve the camera placement problem, a crucial fundamental step is modeling the coverage of the cameras in use. Following the coverage modeling, an optimization method needs to be used to locate the optimal poses and/or camera positions. In general, artificial intelligence search strategies are extensively used in solving discrete optimization problems. In particular, this chapter discusses formulating the camera placement problem in order to be solved by artificial intelligence search. Moreover, the chapter applies selective artificial intelligence search strategies to solve the camera placement problem. Most of these search formulations investigate the problem from a greedy-based perspective. Thus the target is to maximize the primary coverage of the camera network. Additionally, the initiation of the camera model and the subsequent coverage table are counted as key steps prior to applying the optimization method. Thus all instances of the camera coverage over the potential locations must be computed and stored in tabular form, usually known as a coverage table. The computation of the coverage table offers essential data to formulate the search space. Furthermore, in order to locate the solution to the problem, each search strategy defines a unique path throughout the search space. However, regardless of the selection of the search technique, the solutions are usually attained by utilizing randomization restart settings. The chapter also carries out an analytical review of three main searching algorithms namely, generate and test, uninformed search, and hill climbing search algorithms. Two case studies are used to evaluate those algorithms, and the camera placement problem is formulated as a coverage maximization problem. The various searching algorithms are implemented to seek the maximum coverage of the camera array. The placement results obtained based on those algorithms are critically compared in terms of the algorithms’ efficiency and performance. Finally, the chapter highlights the strengths and weaknesses of each approach.
A Phishing Detection Method Based on Data Mining

Data mining technology is a very important technology in the current era of data explosion. With the informationization of society and the transparency and openness of information, network security issues have become the focus of concern of people all over the world. This paper wants to compare the accuracy of multiple machine learning methods and two deep learning frameworks when using lexical features to detect and classify malicious URLs. As a result, this paper shows that the Random Forest, which is an ensemble learning method for classification, is superior to 8 other machine learning methods in this paper. Furthermore, the Random Forest is even superior to some popular deep neural network models produced by famous frameworks such as TensorFlow and PyTorch when using lexical features to detect and classify malicious URLs.