Reviewerly/exHarmony huggingface dataset:
Towards Enhancement of Privacy-Preserving Data Mining Model for Predicting Students Learning Outcomes Performance

There is massive data collection such as the student's information which is included their profile background and learning outcome performance that had been collected by the academic institution. With those of the data collection to identify the potential students who can contribute towards the development of the academic institution. An academic institution that provides open and distant learning programs can advantage from big data on their students' information, as well as data mining techniques and big data analytics tools, provided well as the students' right to privacy is protected. However, the student records shown the potential for enhanced information sharing throughout academic institution departments which raised up the privacy concern. Therefore, this study proposes the enhancement of Privacy Preserving Data Mining (PPDM) model for predicting students' learning outcomes performance strikes a better accuracy of data mining while preserving better data privacy. This PPDM combines two privacy preserving approaches which are k-anonymization and homomorphic encryption. On the other hand, for the data mining, classification algorithms such as Random Forest, Support Vector Machine, and Naïve Bayes are compared to find the best accuracy. The outcome of the proposed PPDM model is expected to give better prediction accuracy for students learning outcomes performance as well as high privacy preservation.
E-mail autocomplete function using RNN Encoder-decoder sequence-to-sequence model

Text Auto-complete feature suggests a stream of words which complete a user's text as the user types each character. Such a feature is used in search engines, email programs, source code editors, database query tools etc. Earlier people have used traditional language models for this problem. But for better performance, a Neural network-based language model is needed. Here, an encoder-decoder based sequence-to-sequence language model has been used for performing text generation and the empirical results show that the model effectively suggests the incomplete sentences.
Multi-granularity Interaction Fusion for Neural Answer Source Selection

Selecting a credible answer source from a massive number of sentences in a large amount of documents is essential for the real-world factoid Question Answering (QA) system. Neural text matching has been widely studied for answer source selection, which serves to measure the relevance between the target question and each candidate answer source. The interaction-based matching methods have achieved remarkable performance. However, the interaction is conditioned on either the coarse-grained word embeddings or the fine-grained encoder states. The interaction of different-granular latent information is omitted. We propose a multi-granularity interaction fusion model. It learns to perceive the interaction not only between the same granular latent information, but that of different-granular latent information. We reconcile these diverse interactions for the computing of global question-source relevance. We experiment on WikiQA. The results show that our method achieves competitive performance compared to the state of the art.
Tree-Based Methods

This chapter explores tree-based methods for demand prediction. These methods are widely used given their strong predictive power. We consider three types of methods: Decision Tree, Random Forest, and Gradient Boosted Tree. We apply these methods under both the centralized and decentralized approaches. For each method, we briefly discuss the underlying mathematical framework, present a common practical way to select the parameters, and detail the implementation process by providing the appropriate codes. We conclude by comparing the different methods in terms of both prediction accuracy and running time.
Designing Functions Using Types

Object-Oriented (OO) programming is currently the dominant design approach in almost all software development. In OO, the natural unit of work is, unsurprisingly, the “object” or “class,” and design effort is focused on defining classes that have the right shapes, behaviors, and relationships for the tasks at hand. In F#, by contrast, the natural units of work are types, which describe the shape of data, and functions, units of code that take some (typed) input and produce some (typed) output in a predictable fashion. It makes sense, therefore, to start our journey into stylish F# coding by looking at how best to design and code relatively simple types and functions. It’s a surprisingly rich and rewarding topic.
Some Implications of Interval Approach to Dimension for Network Complexity

An intervalKůrková, Věra approach to the concept of dimension is presented. Implications of exponentially growing quasiorthogonal dimension for estimates of network complexity are analyzed. Bounds on correlations of computational tasks represented by high-dimensional vectors are derived. Network complexity is explored from the point of view of the concentration of measure phenomenon.
Concatenated Image Compression and Encryption

In recent years, there have been increasing demands for applications of image storage, sharing and transmission across data networks. For these data-intensive and sensitive applications, it is imperative to perform both image compression and encryption. In this paper, two different approaches of image compression and encryption were studied and compared. In the first approach, image compression was followed by encryption; while, in the second approach, image encryption was followed by compression. Both approaches were implemented based on standard schemes, i.e., JPEG 2000 lossless compression and DES encryption. Experiments were conducted for two different types of testing images: a natural image and a medical image. The efficiency performance of the two approaches was compared based on key parameters such as MSE, PSNR, entropy, image file size, compression ratio and time consumption. We found that the first approach outperformed the second for both testing images. The results are presented and analysed along with recommendations for related future work.
E-governance and university of Ha’il institutional excellence in the light of the Kingdom's vision 2030: An empirical study on faculty members

This research aimed to determine the impact of e-governance on achieving institutional excellence at the University of Ha'il from its faculty members' viewpoint regarding the Kingdom's vision 2030. The study was conducted on a random sample of 152 faculty members from various university faculties. Data were collected using the questionnaire as a means of data collection after confirming its suitability for measurement. The questionnaire was distributed electronically, due to the study suspension because of the Corona pandemic. The study relied on the social survey method. Means and standard deviations, simple correlation coefficients, and multiple regression analysis were used in the data analysis. The study found that the University of Ha'il practices e-governance. Besides, there is a statistically significant positive correlation between e-governance and institutional excellence. The combined dimensions of e-governance explain 78.9% variance in institutional excellence. Like most of the previous research studies, the present study also finds that the most investigative dimension of e-governance was the infrastructure's availability, followed by transparency mechanisms, accounting accountability, effective participation, and job empowerment. The least of those dimensions was the human resource development dimension. It is suggested after analyzing the data that universities pay attention to practicing e-governance. It increases their competitiveness and promotes the chances of distinguishing their performance. There is a dire need to validate the necessity of refining the faculty members' skills concerning implementation and dealing with various electronic transactions.
Second-Order Trust-Region Optimization for Data-Limited Inference

Neural networks generally require large amounts of data to adequately model the domain space. In situations where the data are limited, the predictions from these models, which are typically obtained from stochastic gradient descent (SGD) minimization algorithms, can be poor. In these cases, the use of more sophisticated optimization approaches becomes even more crucial to increase the impact of each training iteration. In this paper, we propose an optimization algorithm that uses second-derivative information that exploits curvature information for avoiding saddle points, which can result in limitations on the learning process. In particular, we utilize a Hessian-free approach where we do not explicitly store the second-derivative matrix; rather, we apply a conjugate gradient method and require the Hessian matrix only to compute matrix-vector products. Our approach is based on trust-region methods, which do not require the Hessian to be positive definite and which differentiates our approach from existing Hessian-free methods. We present numerical experiments which demonstrate the improvement in classification accuracy using our proposed approach over a standard SGD approach.
GIS-AHP Based Approach for Largescale Solar PV Site Selection: A Case of Tunisia

Constructing largescale solar photovoltaic (PV) power plants is a major long-term investment. Therefore, identifying the optimal locations is of a prime importance since it has a direct impact on the PV system performance. The main objective of this study is to identify the most promising sites to host largescale solar PV power plants in Tunisia using geographical information systems (GIS) combined with a multi-criteria decision-making approach. The analytical hierarchy process (AHP) technique was used to give weights to the considered criteria, while the weighted overlay tool within the ArcGIS was used to compute the spatial analysis to generate the suitability index map of the potential sites. A sensitivity analysis was performed considering three different scenarios (AHP, Equal weight, Higher location and orography). The final overlaid map revealed that almost 80% of the study area was suitable for hosting largescale solar PV power plants whereas areas of extreme and high suitability, accounting for 1% and 8%, respectively, were densely scattered in the southern and central regions with a few spots in the northwest and eastern parts of the country. In addition, the estimated technical potential varied between 406 and 164 TWh/year based on the selected technology.
Neural Ensemble Recognition for Lung Cancer Credentials

A neural network ensemble is a knowledge hypothesis wherever numerous neural networks are mutually recycled to resolve a difficult problem. Here an involuntary irrational analysis technique named Neural Ensemble Recognition (NER) is projected, which exploits a neural network ensemble to recognize lung cancer data obtained from the GEMS dataset to be analyzed. The ensemble builds on the two-level ensemble architecture. The first-level RSAR compacts with the improbability and impreciseness of factual esteemed gene appearance datasets to judge whether a cell is normal cell or cancer cell. In this forecast of these networks, the evolutionary search impression enhances the subset assortment progression by means of a better method presented here. The second level neural ensemble recognition evaluates the performances of the component classifier combined with the first level classifiers, where every single network has a binary output. The learning efficiency in combination algorithms can be measured. Using these novel techniques, NER achieves a high overall identification rate, but also a low false negative identification rate, which is important in saving lives because missing diagnoses of patients with lung cancer are reduced.
Two-level monotonic multistage recommender systems

A recommender system learns to predict the user-specific preference or intention over many items simultaneously for all users, making personalized recommendations based on a relatively small number of observations. One central issue is how to leverage three-way interactions, referred to as user-item-stage dependencies on a monotonic chain of events, to enhance the prediction accuracy. A monotonic chain of events occurs, for instance, in an article sharing dataset, where a ``follow'' action implies a ``like'' action, which in turn implies a ``view'' action. In this article, we develop a multistage recommender system utilizing a two-level monotonic property characterizing a monotonic chain of events for personalized prediction. Particularly, we derive a large-margin classifier based on a nonnegative additive latent factor model in the presence of a high percentage of missing observations, particularly between stages, reducing the number of model parameters for personalized prediction while guaranteeing prediction consistency. On this ground, we derive a regularized cost function to learn user-specific behaviors at different stages, linking decision functions to numerical and categorical covariates to model user-item-stage interactions. Computationally, we derive an algorithm based on blockwise coordinate descent. Theoretically, we show that the two-level monotonic property enhances the accuracy of learning as compared to a standard method treating each stage individually and an ordinal method utilizing only one-level monotonicity. Finally, the proposed method compares favorably with existing methods in simulations and an article sharing dataset.
Human Limb Motion Segmentation by PCA-ARMA Methods

Effective segmentation of motion capture (MoCap) data is becoming a crucial issue for further human motion posture and behavior analysis, which requires both robustness and computation efficiency in the algorithm design. In this paper, we propose an unsupervised segmentation algorithm based on limb bone partition angle body structural representation, principle component analysis (PCA) for sequences dimension reduction, and autoregressive moving average (ARMA) model fitting. The collected MoCap data is converted into the angle sequence formed by the human limb bone partition segment and the central spine segment. The PCA algorithm is used to reduce the dimension of limbs sequence into a two-dimensional sequence of upper limb and lower limb. The limb angle sequences are matched by the ARMA model, and the segmentation points of the limb angle sequences are distinguished by analyzing the good of fitness of the ARMA model. A medial filtering algorithm is proposed to ensemble the segmentation results from individual limb motion sequences. A set of MoCap measurements are also conducted to evaluate the algorithm including typical body motions collected from subjects of different heights, and are labeled by manual segmentation.
Path Tracing and Object Avoidance Algorithm for Robotic Manipulators Incorporating Constrained Filters

This paper presents an algorithm for object avoidance while sustaining the trajectory of the end effector of the robotic manipulators. Multiple filters are developed that are based on classified constraints and applied to configuration space to obtain a collision-free path. The recursive and iterative nature of the algorithm makes it possible to acquire results under the permissible zone of error. The identification of desired configurations from millions of choices is achieved to obtain smooth and least deviated movements. MATLAB software is used to design, implement, visualize, and test the proposed model.
A reinforcement learning guided adaptive cost-sensitive feature acquisition method

Most of the existing feature selection methods tend to pursue the learning performance of the selected feature subset while ignoring the costs of acquiring each feature. However, in real-world problems, we often face the tradeoff between model performance and feature costs because of limited resources. Moreover, in some applications (e.g. medical tests), features are acquired sequentially in the learning process instead of having known the information of the whole feature set in advance. To solve these problems we design a reinforcement learning agent to guide the cost-sensitive feature acquisition process and propose a deep learning-based model to select the informative and lower-cost features for each instance adaptively. The whole process of feature acquisition will be determined by an agent according to what it has observed from inputs. In particular, a Recurrent Neural Network (RNN) model will learn the knowledge from the current sample and the agent will give the instructions on whether the RNN model will continue to select the next feature or stop the sequential feature acquisition process. Moreover, the proposed method can also select the features per block thus it can deal with high dimensional data. We evaluate the effectiveness of the proposed method on a variety of datasets including benchmark datasets, gene datasets, and medical datasets. Compared with the state-of-the-art feature selection methods, the proposed method can achieve comparable learning accuracy while maintaining lower feature costs. • A RNN model is proposed to make a tradeoff between feature’s performance and costs • A RL agent is adopted to guide the features acquisition adaptively for each instance • The model can select multiple features at a time and deal with high-dimensional data • The model is verified on benchmark datasets and real medical testing datasets
Zilla-64: A Bangla Handwritten Word Dataset Of 64 Districts` Name of Bangladesh and Recognition Using Holistic Approach

Bangla Handwritten Word Recognition (BHWR) is a very challenging task due to the high curvature nature of the character, overlapping between characters, and flourishes in the writing style of Bangla Handwritten Word (BHW). Despite the importance and challenges of BHWR, the handwritten word dataset of Bangla is very few. In this paper, a new challenging Bangla word dataset which, we called ‘Zilla-64’, is introduced, and also the preparation of the dataset is discussed. To the best of our knowledge, this is the first well-labeled Bangla word dataset. This dataset can be used for gender, age, and education level handwritten related researches. Deep learning shows tremendous success in the handwritten recognition area. Because of the popularity of deep learning methods and for testing the performance of the dataset, a holistic approach based, Deep Convolutional Neural Network (DCNN) is applied on the dataset and achieved 93.30% accuracy. The Zilla-64 dataset is made publicly available at this link https://github.com/MahadiHasanNahid/Zilla-64-Dataset.
The Problem of Motion Sickness and Its Implications for Automated Driving

Motion sickness impairs user experience and reduces engagement in non-driving related tasksNon-driving related task (NDRT) in automated driving. The present chapter provides an overview on research on motion sickness and discusses strategies to prevent motion sickness in automated driving. First, we describe major theories on the emergence of MS, and introduce relevant research methods. Second, we provide a selective review on studies on motion sickness in driving. Here, we mainly focus on motion sickness in the context of automated driving but also describe relevant studies from research on non-automated driving. Based on the reviewed theoretical and empirical work, a final part discusses implications and derives recommendations on how motion sickness in automated driving can be effectively prevented.
A Survey of Recent Advances in Recommendation Systems

Present recommendation schemes such as content grounded filtering and collaborative filtering practice dissimilar databases to create references. Content-based filtering creates recommendations built on customer favorites for product types. Collaborative filtering mimics user-to-user recommendations. It forecasts customer’s favorites as a linear, weighted grouping of other user preferences. Both approaches have limits. Content-based filtering can recommend a new item, but requests more data of customer preference in order to include the finest match. Like, collaborative filtering wants huge dataset with lively customers who valued a product before in order to create precise predictions. Arrangement of these dissimilar recommendation schemes is named hybrid systems. These schemes can blend the topographies of the item itself and the favorites of other customers. This paper reviews recent advances in recommendation approaches and their findings.
Positive Unlabeled Gradient Boosting

Classification applied to medical datasets from diagnosis or survey application data often must address the challenge that such data is weakly labeled, with only some positive labels and the rest of the data instances mostly being unlabeled. Standard classifiers struggle to learn the correct class for these positive but unlabeled instances, particularly within imbalanced datasets. The standard Gradient Boosting Classifier is one algorithm that works well on balanced data with completely labeled examples but performs poorly otherwise. In order to improve upon this state-of-the-art method, we propose a modification to its loss function that empowers it to learn a decision boundary more reflective of the data's true distribution. We call this the novel gradient boosting classifier. Our experimental study demonstrates that our proposed new classifier outperforms the state-of-the-art by 8.3% on average across several public medical data sets. This classifier can be applied to healthcare settings, where imbalanced and positive unlabeled data sets are common.
Analysis of Word-level Embeddings for Indic Languages on AI4Bharat-IndicNLP Corpora

This paper presents the analysis of non-contextual word embeddings trained on AI4Bharat-IndicNLP corpus containing 2.7 billion words covering 10 Indian languages. We share the pre-trained embeddings for research and development in Indic languages. These embeddings are evaluated on several evaluation tasks like word similarity and analogy evaluation, classification tasks on multiple datasets. The analysis of word embeddings is expected to give researchers a better understanding of the Indic Languages. We show that Word2Vec skip-gram and FastText skip-gram embeddings are the best performing models for NLP tasks on Indic languages. All the embeddings are made freely available.
Blockchain and Energy Commodity Markets: Legal Issues and Impact on Sustainability

In this article, we discuss the application of blockchain, a type of distributed ledger technology (DLT), in the field of energy trading. We use crude oil trade as an example. In Part I, we discuss the architecture of blockchain and the different types of blockchain that might be applied in this sector. In Part II, we examine the current oil trading markets, particularly their relevant components, as part of the discussion on blockchain application. In Part III, we look at how blockchain and its different types may be applied to the markets and examine the advantages and disadvantages of such application. In Part IV, we look at the legal issues that may arise from such application and the potential solutions. Finally, in Part V, we explore the potential impact of blockchain technology on the United Nations Sustainable Developmental Goals in the future and how a green tech application can be developed.