Reviewerly/exHarmony huggingface dataset:
Visualizing and Computing Natural Language Expressions: Through a Typed Lambda Calculus λ

Lambda λ is an abstraction and a method that provides a logical explanation for studying various domains of knowledge cross-disciplines. Semantics, which is a branch of linguistics, has been well known for discussing a typed lambda λ calculus. Coppock and Champollion have elegantly been elaborated a sense of natural language expressions through various moods including a lambda λ calculus. We adopt this proposal here and are trying to understand the computation part in generating algorithms. While on the other hand, we able to find out some predicate-based (complex and compound) cases during the investigation.
An Analysis of Perceptual Confusions on Logatome Utterances for Similar Language

In a polyglot speech synthesis, it is possible to use one language resource for another language. However, if the adaptation is not implemented carefully, the foreignness of the sound will be too noticeable for the listeners. This paper presents the analysis of respondents’ acceptance of a series of listening tests. The research goal was to find out in the absence of phonemes of a particular language, would it be possible for the phonemes to be replaced with another language’s phonemes. This will be especially beneficial for under-resourced language either in the case for 1) the language has not yet well researched into or 2) the language has not well documented in the required media. Preliminary studies were conducted to construct phoneme confusion matrices. The confusion study was observed based on the consonants’ position in syllable structure: onset and coda. These studies were then compared to similar studies to find possible overlap among them. Then, based on the outcome, two perceptual tests have been conducted to observe the applicability of phoneme substitutions. The first test was to observe the effect of phonemes substitution during the intelligibility test for individual words. The second test was to evaluate whether context influenced perception based on whether respondents noticed phoneme substitution on a word in a series of words. From these experiments, it can be concluded that it is possible to do phoneme substitution but with a certain condition. From significance testing, it was found that phoneme substitution may not be suitable to be implemented for onset position but can be applied for coda position provided the context is available.
Genetic Algorithm for Solving Coverage Issues in Undersea Mining

To be cost-effective, robot-based undersea mining must comply several operational constraints. Among the main constraints are the time and energy required to extract the mineral from the seabed. It is also important to reduce the wear of the joints that connect the ship on the surface with the robot crawler that does the mining on the seabed, since this not only reduces operating costs, but also lengthens the useful life of these parts which increases system security. For this reason, the least amount of twisting in these pieces is preferable, so it is advisable to reduce the number of turns or changes of direction in the trajectory of the robot that extracts the mineral. In this article, we present an algorithm to optimize Coverage Path Planning using Genetic Algorithm to produce paths with longer segments, which can be used in underwater mining and reduce the effects the mentioned turning problem. The resulting paths have on average 55&#x0025; less changes of directions in the trajectory than a GA with standard cost function. In addition, in tests made by placing small obstacles in a random way, 76&#x0025; of useful paths were obtained and up to 59&#x0025; of useful path when the obstacles were grouped into a single larger obstacle.
A Model of Human Factors in Cyber Security

Data has become the new trading currency in today's competitive environment. The human factor is still seen as a significant threat through social engineering channels, despite the advanced technological controls implemented by organizations. Companies can implement appropriate technical solutions, but they are still unable to control the human factor. The aim of the chapter is to better understand the role of human factors in information security. For this purpose, it provides a model that focuses on human-security elements including management commitment, skills, experiences, self-efficacy of human resources, and security culture and training as the vulnerable factors in information security issues. Quantitative methodology and statistical technique are used for analysis. Thus, the main contribution of this research is to present a model of human resource factors in information security that can be applied in several case studies, and the results can be compared.
A Survey: Accretion in Linguistic Classification of Indian Languages

The data on the internet is flourishing by leaps and bounds every second in structured and unstructured format. These data might be documents, Tweets, Articles, Images, Blogs, Reviews, Videos or audios in variety of languages and to classify such data or to analyze emotions from this data is a great demand of business nowadays and is a stimulating task. Machine learning and deep learning are spreading its wings in this field for automatic classification of such data and documents. This paper delves into contribution of the researchers in Indian Languages for information retrieval and classification with machine learning.
The first Internet typographers (to 50th anniversary of the oldest electronic library)

In 2021, a number of memorable events are celebrated: 50th anniversary of Project Gutenberg (the first digital library); 10 years since the death of its founder, Michael Hart; 55th birthday of the creator of the famous Russian digital library Maxim Moshkov. This paper is an attempt to show the significance of these events for Internet and its users. Special attention is paid to the issues of copyright duration legislation. This has become one of the main obstacles in the activities of the mentioned digital libraries.
The Roles of Enterprise Resource Planning and Management Control on Knowledge Sharing

This study signifies knowledge as strategic resources in attaining organisational mission and vision. With the increasing trend of employee turnover, the need to retain knowledge is a challenge, and thus, fostering organisational knowledge sharing is an option to uphold the knowledge. The advancement of the technology, together with management control systems have crucial roles in effectively sharing knowledge. This study aims to examine the relationship between Enterprise Resource Planning (ERP), management control (MC) and their complementarity effect towards facilitating organisational knowledge sharing (KS) practice. Questionnaires were administered to top management of randomly selected manufacturer listed in the Federation of Malaysian Manufacturers Directory. 114 usable responses were analysed using partial least square structural equation modelling (PLS-SEM). The results indicate positive and significant relationships between ERP, MC and KS, hence suggesting the knowledge sharing role of ERP and MC as governing mechanism to KS. The complementarity effect between ERP and MC was also found to be positively associated with KS. The results provide evidence on the importance of appropriate governing control mechanism in combination with enhanced technology to foster organizational knowledge-sharing practice. Keywords: Enterprise resource planning; management control; knowledge sharing
Use of Faceted Search: The Effect on Researchers

The extensive amount of results obtained from any Web search operation and loads of related and/or irrelevant hits presented on the user’s screen are still poses challenges in the information retrieval field of study; especially if the user is an academic researcher and is looking for reliable and focused results. Therefore, improving the performance of Web search engines continues to be an active research topic. One of the biggest challenges to search engine optimization is when a user submits incomplete query statements or fragmented keywords. Using broken or fragmented keywords the semantic correlation will fail to result in inconsistent and outsized search results. This oversized (or overloaded) problem can be mitigated by utilizing the Exploratory Search technique with a faceted search refining mechanism. This study’s main goal is to present a short review of the existing Exploratory Search techniques and faceted search implementations and shed light on the main limitations and shortcomings.
Three-dimensional multi-target tracking of point cloud from bird's-eye view

Currently, the most popular approach for multitarget tracking is tracking-by-detection. Existing algorithms cannot balance the quality and speed of three-dimensional (3D) multi-target online point cloud tracking. We propose a low-complexity high-quality 3D multi-target online tracking algorithm to track a 3D point cloud from a bird's-eye view. First, we use a detector to obtain a bird's-eye view of a 3D point cloud and object information. Then, a 3D multi-target online tracking scheme is constructed to obtain the target trajectory. The scheme is mainly divided into state estimation and target association. State estimation uses a 3D Kalman filter algorithm Target association is divided into two steps: cascade matching and intersection over union (IoU) matching. The apparent feature used by the cascade matching algorithm is determined by using the unsupervised algorithm variational autoencoder on the candidate target in a bird's-eye view. The IoU matching algorithm uses the 3D spatial features of the candidate target. We demonstrate the effectiveness of our algorithms on the KITTI dataset. The values of multiple object tracking accuracy (MOTA), multiple object tracking precision (MOTP), and speed are 66.02, 85.81, and 43 frames per second, respectively, indicating the effectiveness of our algorithm in tracking quality and speed.
Comparative Analysis of Supervised Algorithms With Hyperparameter Tuning on Breast Cancer Dataset

In a fast-pacing and the advancing world where setting newer targets each day and working for them has become the next big thing in the scenery, we as humans have taken our health factor on second foot or to be honest, we have now become far more lenient and sooner or later we need to pay the price. We have shed shelter to many new diseases, in turn making us pay the price for it. Amongst those, one which has drawn attention from the past few years is Breast Cancer (BC). As a result, we have seen a major increase in the death rate for women especially. It can be broadly be divided into categories Malignant and Benign. Benign is termed as noncancerous (that means it cannot spread to other parts of the body) but, Malignant is the contrary of benign. If it is spotted at the right time treatment can be done. The data-set we are using here is fetched from Wisconsin Cancer data-set. Through the medium of this paper, we aim at using certain machine learning algorithms and python that are tested for best-case parameters(sequentially), to come up with a conclusion for which machine learning algorithm fits best over here in analysing the given cancer data-set for Benign(B) and Malignant(M).
Predicting Dengue Fever Based on Climate Factor using Modified K-Nearest Neighbor

Dengue fever is a contagious disease caused by mosquito bites. One of the factors that can affect the survival of these mosquitoes is climate. Denpasar is the most densely populated city on the island of Bali, so it is easy for the disease to spread from one resident to another. It was recorded that in 2019, dengue cases in Denpasar City increased drastically, reaching 1,144 people and three of them died. Therefore, it is necessary to immediately deal with the spread of Dengue Fever in Denpasar City. One thing that can be done is to predict the number of cases of dengue fever in the future so that it can be used as material for evaluation and prevention efforts, including the preparation of the number of medical personnel and hospitals in dealing with the disease. The data used in this study are data on the number of dengue cases in Denpasar City and climate data for the City of Denpasar in 2014&#x2013;2019. The method used to predict is Modified K-Nearest Neighbor (MKNN). To implement the visualization of the method, a website-based system will be built that will be able to predict the number of dengue cases per year. Blackbox testing shows that all processes on the website are running as they should. The suggestion that can be proposed is to use humidity and rainfall data not by year but by month so that case prediction can be done per month.
Sentiment Analysis for Video on Demand Application User Satisfaction with Long Short Term Memory Model

Customer reviews are important information that can be used by providers of goods or services to maintain customer loyalty, understand customer feelings and analyze the business competition. Users use customer feedback on social media or e-commerce as material for consideration before using or buying products. This study aims to conduct a sentiment analysis for user satisfaction of the Video on Demand application in Indonesia with the Long Short-Term Memory (LSTM) model. LSTM, which is a deep learning method that is widely implemented in natural language processing research. Sentiment analysis is applied to find out how customers feel about products on the market. The study results indicate that the LSTM model's implementation for sentiment analysis using two positive and negative labels obtained the value of precision 73.81%, recall 73.81%, f1 score 73.81%. In addition, the accuracy value obtained is 73.90% can be used as a consideration for the company in knowing user sentiment to meet the expectations and desires of customers and keep customers using the service.
Conceptual approach to implementation of the threat prevention subsystem of an operational cybersecurity center

One of the most promising trends of modern information security is proactive defense. Proactive defense has a number of significant advantages, which allow to overcome the fundamental limitations which are inherent to traditional reactive methods. It makes relevant the task of obtaining qualitative and quantitative characteristics of a security system, based on proactive information protection technologies. In this regard, the article discusses the issues of analysis and modeling attack prevention subsystems of operational cybersecurity centers. The cyber security operational center is a crucial component of a modern security infrastructure, which, at the same time, demonstrates basic properties of a protected object. Therefore it is selected as an example of a protected object. The research substantiates the effectiveness of proactive defense system and describes its echelons. The structural model is presented for operational cybersecurity center's threat prevention subsystem. The threat prevention subsystem's functional components’ roles are described. On the basis of a model with full overlap and fuzzy indicators, a mathematical model of the threat prevention subsystem of the operational cybersecurity center is presented. The approach proposed is dictated not only by an increase in the functionality of modern technologies, but also by the requirements for the creation of adaptive integrated solutions that are scalable within the architecture to protect against various types of threats.
Multiband Based Joint Sparse Representation for Motor Imagery Classification

The feature extraction method based on multiband has been successfully applied to the classification task of motor imagery (MI). However, it is still a challenge to eliminate redundant information and extract significant common features between multiband. The joint sparse model (JSM) performs joint decoding through joint sparsity among multi-channel signals and has achieved great success in computer vision and pattern recognition. In this work, we employ the Pearson correlation-based channel selection method to establish high-quality spatial distribution. Moreover, a novel multiband based joint sparse representation (MJSR) is proposed to fuse CSP features of multiband and obtain joint coefficient features. The SVM is then applied to classify MI tasks. The experimental results on two BCI competition datasets indicate that the proposed method can effectively improve the classification performance.
A Data Mining Approach to Recommend Idyllic Business Opportunities

Selection of an appropriate sector will be a crucial factor for a success of a business. However, due to a lack of knowledge and guidance in selecting a suitable business idea, the business failure rates have been increased. Within this context, this study aims to develop a computerized model based on a data mining approach to suggest the most suitable business idea to avoid inappropriate business selections. In addition, this solution attempts to determine the likelihood of succeeding in a particular business opportunity that someone is willing to start. During the model development, samples of data were gathered by interviewing experts. Furthermore, a structured questionnaire was prepared by considering the various factors affecting the success of a business and it was used to collect data from businessmen in several sectors. By comparing the accuracies of possible applicable models, the Gradient Boosting model was developed to archive the expected results. To increase the model accuracy, a set of business patterns, which were extracted from data analysis were added to the model.
Use of Pupillometry for Emotion Recognition and Ophthalmology Studies: A Systematic Review

In recent years, the study of emotion has increased due to the interaction of humans with the machine. It is helpful to interpret human actions and to improve the relationship between humans and machines for developing software that can understand the human states and can take action accordingly. The most accurate approach to recognize emotions, pupil diameter (PD) has been discussed in this study. The reason for selecting this approach is that it is directly controlled by the Autonomic Nervous System (ANS) and is easy to detect. The main aim of this paper is to recognize the difference in pupil diameter due to the positive and negative emotional states of a person. Numerous researches have proposed different methods for detecting emotions by using pupillary response and achieved reliable accuracy. The study showed that the pupil diameter is influenced by different video, image, and script content indicating the state of the user. This study provides empirical evidence demonstrating how changes in the state of the user can be unobtrusively identified.
Statistical inference on group Rasch mixture network models

In a two‐mode network, the nodes are divided into two types (primary nodes and secondary nodes), and connections exist only between nodes of different types. In reality, in such a two‐mode network, one‐mode network connections may also exist among primary nodes, and these two kinds of networks are usually not independent and coexistent. In this paper, we first propose a group Rasch mixture network model that focuses on the connections between primary nodes and secondary nodes, while incorporating the group structure and linkage information of primary nodes. We then develop a modified expectation–maximization algorithm to estimate the proposed model with a λ ‐BIC method for selecting the tuning parameter. Additionally, we provide a likelihood‐ratio test statistic to examine whether the two kinds of networks are independent and implement the leave‐one‐out method to construct a network prediction rule. Finally, we establish asymptotic results and demonstrate the numerical performance of the proposed methods using both simulations and the Last.fm dataset.
Atmospheric visibility prediction based on multi-model fusion

In this paper, a combinatorial algorithm-based visibility prediction method is proposed for improving the accuracy of visibility prediction. Firstly, four algorithms, namely support vector machine, kernel extreme learning machine, random forest and RBF neural network, are used as the basis functions for prediction, then the objective function of the combined prediction is constructed, the cuckoo search is used to optimise the calculation of the weighting coefficients of the combined prediction, and finally the combined prediction results are obtained. The experimental results show that the combined prediction algorithm proposed in this paper can effectively improve the accuracy of visibility prediction, and has certain application and research value.
Examining the Factors that Make Co-Watching with Agents Effective

The spread of video streaming services has increased the opportunities for video watching, and research on co-watching with agents is also being conducted. However, few studies have been conducted and not insufficient knowledge has been obtained. In this study, we will conduct an investigation on the relationship among the background of the co-watchers, the video evaluation, and the agent’s impression. In the experiment, we asked the participants to co-watch a basketball game with an agent who responded appropriately then, and after watching, they answered a questionnaire. As a result, there was a moderate correlation between the Likeability and the evaluation of the video watching. There was also a correlation between the evaluation of the video watching and the empathy for the agent.
Compressed Sensing and Shape Extraction with Vortex Singularities

Photonics-based compressed sensing and shape extraction may aid pattern recognition and build intuition for image processing in hybrid vision systems. When an illuminating vortex illuminates an aperture with similar geometry, power flows towards the singularity.
Secure Multi-Party Computation Using Pre-distributed Information from an Initializer

Secure Multi-Party Computation (MPC) is a concept that includes a system of n participants communicating each other securely such that the participants want to compute any given function using their private inputs without giving any information about their inputs. The matter of computing a multiplication gate has raised a security concern. That is, because the multiplication gate rises the degree of the resulted polynomial while there is a limited number of required participants to reconstruct and compute the outcome. In this study, we propose a MPC protocol using a server or a remote computer as an initializer, which has become popular these days to conduct a probabilistic functionality in the circuit. The initializer does not get involved in the actual online computation and it can just share some random pre-computed information at any time prior to it. Our protocol needs only one round of online secret sharing, and the online computation of both the inputs addition and multiplication gates can be executed in parallel. The extension of our protocol can be used for the multiplication gates with different multiplicative depths (intermediate levels). The proposed protocol is information-theoretic secure against a coalition of t passive adversaries with the presence of at least \(n\ge t+1\) participants. The communication complexity of a multiplication gate is linear.