Reviewerly/exHarmony huggingface dataset:
An Innovative Huffman Forest-Based Method to Detected Railroad Station Anomalies

Detecting railroad station anomalies is a critical task prior to segmentation and making optimization decisions for each cluster. Three types of anomalies (local clustered, axis paralleled, and surrounded by normal instances) caused by the specialty of railroad operations bring the existing methods non-trivial challenges in detecting them accurately and efficiently. To tackle this limitation of existing methods, this paper proposes a novel anomaly detection method named Huffman Anomaly Detection Forest (HuffForest) to detect station anomalies, which leverages Huffman encoding to measure abnormalities in certain railroad scenarios with high accuracy. The proposed method establishes a Huffman forest by constructing trees from the perspective of data points and subsequently computes anomaly scores of instances considering both local and global information. A sampling-based version is also developed to improve scalability for large datasets. Taking advantage of the encoding mechanism, the proposed method can effectively recognize the underlying patterns of railroad stations and detect outliers in various complicated scenarios where the conventional methods are not reliable. Experiment results on both synthesized and public benchmarks are demonstrated to show the advances of the proposed method compared to the state-of-the-art isolation forest (iForest) and local outlier factor (LOF) methods on detection accuracy with an acceptable computational complexity.
A New Accelerated Fixed-Point Algorithm for Classification and Convex Minimization Problems in Hilbert Spaces with Directed Graphs

A new accelerated algorithm for approximating the common fixed points of a countable family of G-nonexpansive mappings is proposed, and the weak convergence theorem based on our main results is established in the setting of Hilbert spaces with a symmetric directed graph G. As an application, we apply our results for solving classification and convex minimization problems. We also apply our proposed algorithm to estimate the weight connecting the hidden layer and output layer in a regularized extreme learning machine. For numerical experiments, the proposed algorithm gives a higher performance of accuracy of the testing set than that of FISTA-S, FISTA, and nAGA.
Theorem Proving as Constraint Solving with Coherent Logic

In contrast to common automated theorem proving approaches, in which the search space is a set of some formulae and what is sought is again a (goal) formula, we propose an approach based on searching for a proof (of a given length) as a whole. Namely, a proof of a formula in a fixed logical setting can be encoded as a sequence of natural numbers meeting some conditions and a suitable constraint solver can find such a sequence. The sequence can then be decoded giving a proof in the original theory language. This approach leads to several unique features, for instance, it can provide shortest proofs. In this paper, we focus on proofs in coherent logic, an expressive fragment of first-order logic, and on SAT and SMT solvers for solving sets of constraints, but the approach could be tried in other contexts as well. We implemented the proposed method and we present its features, perspectives and performances. One of the features of the implemented prover is that it can generate human understandable proofs in natural language and also machine-verifiable proofs for the interactive prover Coq.
Computer Image Content Retrieval considering K-Means Clustering Algorithm

The traditional computer image content retrieval technology can only meet the specific requirements of customers; because of its general features, it cannot comply with the requirements of various environments, purposes, and time simultaneously. This study presents a computer image content retrieval method for a K-means clustering algorithm (KCA). The information collected by computer is preprocessed by K-means clustering algorithm, and the unacquired computer image is labeled based on the optimal learning order according to the KCA. The K-means clustering algorithm classifies the color, pattern, shape, and content of computer images and takes advantage of the invariance advantages of image content retrieval such as scale, rotation, illumination, and blur correction to effectively solve the recurring problems of computer images during retrieval and increase its accuracy. The results of the experiment indicate that the proposed K-means clustering algorithm can enhance the efficiency and performance effectively during image retrieval by computer as compared to the traditional content search algorithm and also help to quickly converge to the query content; it also shows that KCA has good robustness.
“What makes a question inquisitive?” A Study on Type-Controlled Inquisitive Question Generation

We propose a type-controlled framework for inquisitive question generation.We annotate an inquisitive question dataset with question types, train question type classifiers, and finetune models for type-controlled question generation.Empirical results demonstrate that we can generate a variety of questions that adhere to specific types while drawing from the source texts.We also investigate strategies for selecting a single question from a generated set, considering both an informative vs. inquisitive question classifier and a pairwise ranker trained from a small set of expert annotations.Question selection using the pairwise ranker yields strong results in automatic and manual evaluation.Our human evaluation assesses multiple aspects of the generated questions, finding that the ranker chooses questions with the best syntax (4.59), semantics (4.37), and inquisitiveness (3.92) on a scale of 1-5, even rivaling the performance of human-written questions.
Feature-based CBCT self-calibration for arbitrary trajectories

Development of an algorithm to self-calibrate arbitrary CBCT trajectories which can be used to reduce metal artifacts. By using feature detection and matching we want to reduce the amount of parameters for the BFGS optimization and thus reduce the runtime.Each projection is 2D-3D registered on a prior image with AKAZE feature detection and brute force matching. Translational misalignment is calculated directly from the misalignment of feature positions, rotations are aligned using a minimization algorithm that fits a quartic function and determines the minimum of this function.We did three experiments to compare how well the algorithm can handle noise on the different degrees of freedom. Our algorithms are compared to Broyden-Fletcher-Goldfarb-Shanno (BFGS) minimizer with Normalized Gradient Information (NGI) objective function, and BFGS with distance between features objective function using SSIM, nRMSE, and the Dice coefficient of segmented metal object.Our algorithm (Feature ORiented Calibration for Arbitrary Scan Trajectories with Enhanced Reliability (FORCASTER)) performs on par with the state-of-the-art algorithms (BFGS with NGI objective). nRMSE: FORCASTER = 0.3390, BFGS+NGI = 0.3441; SSIM: FORCASTER = 0.83, BFGS + NGI = 0.79; Dice: FORCASTER = 0.86, BFGS + NGI = 0.87.The proposed algorithm can determine the parameters of the projection orientations for arbitrary trajectories with calibration quality comparable to state-of-the-art algorithms, but faster and with higher tolerance to errors in the initially guessed parameters.
ACL2s Systems Programming

ACL2 provides a systems programming capability that allows one to write code that uses and extends ACL2 inside of ACL2.However, for soundness reasons, ACL2 bars the unrestricted use of certain kinds of programming constructs, like destructive updates, higher-order functions, eval, and arbitrary macros.We devised a methodology for writing code in Common Lisp that allows one to access ACL2, ACL2s, and Common Lisp functionality in a unified way.We arrived at this methodology in the process of developing the ACL2 Sedan (ACL2s) and using it as a key component in formalmethods-enabled projects relating to gamified verification, education, proof checking, interfacing with external theorem provers and security.The methodology includes a library for performing ACL2 queries from Common Lisp, as well as guidelines and utilities that help address common needs.We call this methodology "ACL2s systems programming," to distinguish it from ACL2 systems programming.We show how our methodology makes it possible to easily develop tools that interface with ACL2 and ACL2s, and describe our experience using it in our research.
Impact Of Motivation on Employee Performance in The Hospitality Industry

The main objective of the study was to investigate the impact of motivation on employee performance in the hospitality enterprises in Kosovo.For that purpose, the paper uses primary data obtained through a survey of 130 employees in several hospitality enterprises in Kosovo.The survey questionnaire consisted of 4 close-ended questions.The data were analysed using regression model statistical tools.First, cross-tabular analyses were performed where each motivation tool was crossed with the employee performance and the coefficient of determination and the correlation coefficient were calculated in order to determine the individual impact of each motivation tool on employee performance This research argues that each motivation tool affects the increase of employee performance in the hospitality industry in Kosovo.The strongest motivational tool for employees in the hospitality enterprises in Kosovo is the salary, followed by the opportunities for advancement and working conditions.Then, multiple linear regression was calculated in order to determine the overall cumulative impact of all motivation tools (motivational package) on employee performance.This research argues that there is a very strong direct relationship between the whole motivational package and employee performance in the hospitality industry in Kosovo.
Blockchain Technology Applications for Industry 4.0

Abstract: Blockchain: Industry 4.0 involves innovations with upcoming digital technologies, and blockchain is one of them. Blockchain can be incorporated to improve security, privacy, and data transparency both for small and large enterprises. Industry 4.0 is a synthesis of the new production methods that allow manufacturers to achieve their target more rapidly. Research has been conducted on various Industry 4.0 technologies like Artificial Intelligence (AI), Internet of Things (IoT), Big data, and Blockchain, and how they could create significant interruptions in recent years. These technologies provide various possibilities in the world of manufacturing and supply chain. Blockchain is a technology that has gained much recognition and can enhance the manufacturing and supply chain environment. Various fields now have fascinating insights into the advantages of blockchain. Several research articles on “Blockchain” and “Industry 4.0” from Google Scholar, Scopus, and other relevant sources are identified and reviewed for this study. This paper discusses the major potential of Blockchain in Industry 4.0. Various drivers, enablers, and associated capabilities of Blockchain technology for Industry 4.0 are discussed for insights. Different Industry 4.0 spheres/sub-domains for Blockchain technology realisation are also discussed. Finally, we have identified and studied fourteen significant applications of Blockchain in Industry 4.0. It is a range of new developments and hope for immense opportunities that are changing Industry 4.0. This technology would work to achieve amplified outcomes and work individually to enhance the process. Keywords: Education, Online Product learning, Ledgers, Bitcoin, Blocks, Smart Contracts, EOS.
A Framework for Blockchain Based E-Voting System for Iraq

Election is a basic democratic tool that offers an official mechanism for the people to express their opinions in order to form a government by democratic means. Electronic voting has evolved into the most significant application of e-governance and e-democracy. Few countries have recently taken the opportunity to test and use electronic voting systems. Also, many countries expect that internet voting will become a reality during the next decade. In the conventional voting system, the electors authenticate themselves by displaying credentials; this move is open to the public and is validated by poll workers. During this authentication, they are manually checked before being eligible to vote. The standard system of voting is more costly and needs more human capital. Because of these factors, the whole world is heading toward the trend of e-voting. Electronic voting devices are supposed to be the answer to the shortages. A secure e-voting system must meet many criteria, including uniqueness, performance, fairness, stability, safety, authentication, and anonymity. Blockchain is a modern security solution, which is capable of securing e-voting system. Blockchain is distributed ledger. Each block contains only one transaction. Block chain provides security using cryptography and hashing. Blockchain provides a perfect infrastructure to provide secure e-voting netwms.ork. Blockchain along with smart contracts need hour to improvise an e-voting system. This paper presents a secure blockchain based framework for an e-electronic voting in Iraq.
Continual Sequence Modeling With Predictive Coding

Recurrent neural networks (RNNs) have been proved very successful at modeling sequential data such as language or motions. However, these successes rely on the use of the backpropagation through time (BPTT) algorithm, batch training, and the hypothesis that all the training data are available at the same time. In contrast, the field of developmental robotics aims at uncovering lifelong learning mechanisms that could allow embodied machines to learn and stabilize knowledge in continuously evolving environments. In this article, we investigate different RNN designs and learning methods, that we evaluate in a continual learning setting. The generative modeling task consists in learning to generate 20 continuous trajectories that are presented sequentially to the learning algorithms. Each method is evaluated according to the average prediction error over the 20 trajectories obtained after complete training. This study focuses on learning algorithms with low memory requirements, that do not need to store past information to update their parameters. Our experiments identify two approaches especially fit for this task: conceptors and predictive coding. We suggest combining these two mechanisms into a new proposed model that we label PC-Conceptors that outperforms the other methods presented in this study.
Detection of Fake Currency Using KNN Algorithm

Abstract: Bank currency is our country's most valuable asset, and to cause inconsistencies in money, criminals use phony notes that seem identical to the real one on the stock exchange. During demonetization time it is seen that so much fake currency is floating in the market. In general, for a human being, it is very difficult to identify forged notes from the genuine not instead of various parameters designed for identification as many features of forged notes are similar to the original one. To discriminate between fake bank currency and original note is a challenging task. So, there must be an automated system that will be available in banks or ATMs. To design such an automated system there is a need to design an efficient algorithm that can predict whether the banknote is genuine or forged bank currency as fake notes are designed with high precision. In this paper, six supervised machine learning algorithms are applied to the dataset available on the UCI machine learning repository for the detection of Bank currency authentication. To implement this we have applied Support Vector Machine, Random Forest, Logistic Regression, Naïve Bayes, Decision Tree, K- Nearest Neighbor by considering three train test ratios 80:20, 70:30, and 60:40 and measured their performance based on various quantitative analysis parameters like Precision, Accuracy, Recall, MCC, F1-Score and others. And some SML algorithms are giving 100 % accuracy for a particular train test ratio. Keywords: Support Vector Machine, Bank currency, Supervised Machine Learning
Limitations of a proposed correction for slow drifts in decision criterion

Trial history biases in decision-making tasks are thought to reflect systematic updates of decision variables, therefore their precise nature informs conclusions about underlying heuristic strategies and learning processes. However, random drifts in decision variables can corrupt this inference by mimicking the signatures of systematic updates. Hence, identifying the trial-by-trial evolution of decision variables requires methods that can robustly account for such drifts. Recent studies (Lak’20, Mendonça‘20) have made important advances in this direction, by proposing a convenient method to correct for the influence of slow drifts in decision criterion, a key decision variable. Here we apply this correction to a variety of updating scenarios, and evaluate its performance. We show that the correction fails for a wide range of commonly assumed systematic updating strategies, distorting one’s inference away from the veridical strategies towards a narrow subset. To address these limitations, we propose a model-based approach for disambiguating systematic updates from random drifts, and demonstrate its success on real and synthetic datasets. We show that this approach accurately recovers the latent trajectory of drifts in decision criterion as well as the generative systematic updates from simulated data. Our results offer recommendations for methods to account for the interactions between history biases and slow drifts, and highlight the advantages of incorporating assumptions about the generative process directly into models of decision-making.
Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection

An important task for designing QA systems is answer sentence selection (AS2): selecting the sentence containing (or constituting) the answer to a question from a set of retrieved relevant documents. In this paper, we propose three novel sentence-level transformer pre-training objectives that incorporate paragraph-level semantics within and across documents, to improve the performance of transformers for AS2, and mitigate the requirement of large labeled datasets. Specifically, the model is tasked to predict whether: (i) two sentences are extracted from the same paragraph, (ii) a given sentence is extracted from a given paragraph, and (iii) two paragraphs are extracted from the same document. Our experiments on three public and one industrial AS2 datasets demonstrate the empirical superiority of our pre-trained transformers over baseline models such as RoBERTa and ELECTRA for AS2.
Analysis of formation methods of graphic competence of future engineering teachers

The article substantiates the need to improve the formation methods of graphic competence of future engineering teachers by means of digital technologies. To achieve the goal of the article the analysis of normative documents of training of future engineering teachers in the field of digital technologies, methods of teaching graphic disciplines in Higher Education Institutionі, prospects of improving graphic training of future engineering teachers by means of digital technologies are determined. Based on the analysis of work programs of the cycle of disciplines of graphic training, we conclude that today the method of formation of graphic competence in future engineering teachers in the field of digital technologies needs to be improved.&#x0D; It is established that the most effective way to reform the system of training future engineering teachers in the field of digital technologies is the use of modern learning technologies, which include design, research, integrative technologies that provide personality-oriented learning and graphic competence of future engineering teachers in the field digital technologies. It is determined that it is necessary to improve the teaching methods of the discipline "Engineering Computer Graphics", which will include the study of two-dimensional graphics, three-dimensional spatial modeling, 3D printing technologies and the creation of 4D objects in KOMPAS-3D.
Supporting AI-Explainability by Analyzing Feature Subsets in a Machine Learning Model

Machine learning algorithms become increasingly prevalent in the field of medicine, as they offer the ability to recognize patterns in complex medical data. Especially in this sensitive area, the active usage of a mostly black box is a controversial topic. We aim to highlight how an aggregated and systematic feature analysis of such models can be beneficial in the medical context. For this reason, we introduce a grouped version of the permutation importance analysis for evaluating the influence of entire feature subsets in a machine learning model. In this way, expert-defined subgroups can be evaluated in the decision-making process. Based on these results, new hypotheses can be formulated and examined.
Adaptively Promoting Diversity in a Novel Ensemble Method for Imbalanced Credit-Risk Evaluation

Ensemble learning techniques are widely applied to classification tasks such as credit-risk evaluation. As for most credit-risk evaluation scenarios in the real world, only imbalanced data are available for model construction, and the performance of ensemble models still needs to be improved. An ideal ensemble algorithm is supposed to improve diversity in an effective manner. Therefore, we provide an insight in considering an ensemble diversity-promotion method for imbalanced learning tasks. A novel ensemble structure is proposed, which combines self-adaptive optimization techniques and a diversity-promotion method (SA-DP Forest). Additional artificially constructed samples, generated by a fuzzy sampling method at each iteration, directly create diverse hypotheses and address the imbalanced classification problem while training the proposed model. Meanwhile, the self-adaptive optimization mechanism within the ensemble simultaneously balances the individual accuracy as the diversity increases. The results using the decision tree as a base classifier indicate that SA-DP Forest outperforms the comparative algorithms, as reflected by most evaluation metrics on three credit data sets and seven other imbalanced data sets. Our method is also more suitable for experimental data that are properly constructed with a series of artificial imbalance ratios on the original credit data set.
Spatio-temporal prediction and reconstruction network for video anomaly detection

The existing anomaly detection methods can be divided into two popular models based on reconstruction or future frame prediction. Due to the strong learning capacity, reconstruction approach can hardly generate significant reconstruction errors for anomalies, whereas future frame prediction approach is sensitive to noise in complicated scenarios. Therefore, a solution has been proposed by balancing the merits and demerits of the two models. However, most methods relied on single-scale information to capture spatial features and lacked temporal continuity between the video frames, affecting anomaly detection accuracy. Thus, we propose a novel method to improve anomaly detection performance. Because of the objects of various scales in each video, we select different receptive fields to extract comprehensive spatial features by the hybrid dilated convolution (HDC) module. Meanwhile, the deeper bidirectional convolutional long short-term memory (DB-ConvLSTM) module can remember the temporal information between the consecutive frames. Experiments prove that our method can detect abnormalities in various video scenes more accurately than the state-of-the-art methods in the anomaly-detection task.