Reviewerly/exHarmony huggingface dataset:
Identifying Historical Travelogues in Large Text Corpora Using Machine Learning

Travelogues represent an important and intensively studied source for scholars in the humanities, as they provide insights into people, cultures, and places of the past. However, existing studies rarely utilize more than a dozen primary sources, since the human capacities of working with a large number of historical sources are naturally limited. In this paper, we define the notion of travelogue and report upon an interdisciplinary method that, using machine learning as well as domain knowledge, can effectively identify German travelogues in the digitized inventory of the Austrian National Library with F1 scores between 0.94 and 1.00. We applied our method on a corpus of 161,522 German volumes and identified 345 travelogues that could not be identified using traditional search methods, resulting in the most extensive collection of early modern German travelogues ever created. To our knowledge, this is the first time such a method was implemented for the bibliographic indexing of a text corpus on this scale, improving and extending the traditional methods in the humanities. Overall, we consider our technique to be an important first step in a broader effort of developing a novel mixed-method approach for the large-scale serial analysis of travelogues.
Predicate Change

Abstract Like belief revision, conceptual change has rational aspects. The paper discusses this for predicate change. We determine the meaning of predicates by a set of imaginable instances, i.e., conceptually consistent entities that fall under the predicate. Predicate change is then an alteration of which possible entities are instances of a concept. The recent exclusion of Pluto from the category of planets is an example of such a predicate change. In order to discuss predicate change, we define a monadic predicate logic with three different kinds of lawful belief: analytic laws, which hold for all possible instances; doxastic laws, which hold for the most plausible instances; and typicality laws, which hold for typical instances. We introduce predicate changing operations that alter the analytic laws of the language and show that the expressive power is not affected by the predicate change. One can translate the new laws into old laws and vice versa. Moreover, we discuss rational restrictions of predicate change. These limit its possible influence on doxastic and typicality laws. Based on the results, we argue that predicate change can be quite conservative and sometimes even hardly recognisable.
A walk on max-plus algebra

Max-plus algebra is a kind of idempotent semiring over Rmax:=R∪{−∞} with two operations ⊕:=max and ⊗:=+. In this paper, we introduce a new model of a walk on one dimensional lattice on Z, as an analogue of the quantum walk, over the max-plus algebra and we call it max-plus walk. In the conventional quantum walk, the summation of the ℓ2-norm of the states over all the positions is a conserved quantity. In contrast, the summation of eigenvalues of state decision matrices is a conserved quantity in the max-plus walk. Moreover, spectral analysis on the total time evolution operator is also given.
Analytical Evaluation of k–Anonymity Algorithm and Epsilon-Differential Privacy Mechanism in Cloud Computing Environment

Expected and unexpected risks in cloud computing, which included data security, data segregation, and the lack of control and knowledge, have led to some dilemmas in several fields. Among all of these dilemmas, the privacy problem is even more paramount, which has largely constrained the prevalence and development of cloud computing. There are several privacy protection algorithms proposed nowadays, which generally include two categories, Anonymity algorithm, and differential privacy mechanism. Since many types of research have already focused on the efficiency of the algorithms, few of them emphasized the different orientation and demerits between the two algorithms. Motivated by this emerging research challenge, we have conducted a comprehensive survey on the two popular privacy protection algorithms, namely K-Anonymity Algorithm and Differential Privacy Algorithm. Based on their principles, implementations, and algorithm orientations, we have done the evaluations of these two algorithms. Several expectations and comparisons are also conducted based on the current cloud computing privacy environment and its future requirements.
coisTable: An Individual-and-Spatial-Aware Tabletop System for Co-located Collaboration

We present coisTable, a tabletop system that integrates a shared interactive surface with spatial-aware and identifiable tangible devices to support co-located collaborative work. We describe the underlying technical contributions: a hybrid interaction paradigm of graphical and tangible interfaces to foster the collaboration space; a workspace partition strategy to facilitate coordination; a natural identification to promote participation using tangible terminals. In addition, we detail the system prototype including the hardware implementation and the software architecture. Capabilities and interaction modalities are illustrated with the comparison of three conditions of a project management simulation. Finally, we report on the experimental results and the limitations of the tabletop system.
Optimisation of Lightweight Klein Encryption Algorithm With 3 S-box

Internet of Things (IoT) have offered great opportunities for the growth of smart objects in the last decade. Smart devices are deployed in many fields such as smart cities, healthcare and agriculture. One of the applications of IoT is Wireless Sensor Networks (WSN) that require inexpensive and space-economic design for remote sensing and communication capabilities. This, unfortunately, lead to their inherent security vulnerabilities. Lightweight cryptography schemes are designed to counter many attacks in low-powered devices such as the IoT and WSN. These schemes can provide support for data encryption and key management while maintaining some level of efficiency. Most of these block ciphers provide good security. However, due to the complex cryptographic scheme's efficiency and optimisation is an issue. In this work, we focus on a new lightweight encryption scheme called the Klein block cipher. The algorithms of Klein block cipher are analysed for performance and security optimisations. A new algorithm which consists of 3-layer substitute box is proposed to reduce the need for resource consumption but maintain the security.
Deep Learning Models for Analysis of Traffic and Crowd Management from Surveillance Videos

Deep learning models have been used in the field of object detection and object counting. The problem statement dealt with in this paper aims to achieve the objectives of traffic and crowd management. The Single Shot MultiBox Detector (SSD) model is used in conjunction with a line of counting approach to count the objects of interest in a video captured using surveillance cameras. The proposed model has been used for analyzing traffic surveillance videos to make intelligent traffic decisions to prioritize traffic signals based on the traffic densities. As a sub case of traffic management, a Tesseract OCR model is used to capture the license plate of vehicles violating any traffic regulations. For crowd management, surveillance videos are analyzed to obtain the crowd statistics to handle crowd management in cases of emergencies and huge public gatherings for safety and security.
Improved DM-QIM Watermarking Scheme for PDF Document

In any application of watermarking, the selection of feature vectors from a given multimedia content is important from the perspective of robustness and security. In the case of PDF document, the spaces between words and characters are commonly utilized for embedding watermark in the conventional schemes. Instead of modifying the space values directly, researchers proposed to manipulate the frequency components of the space values by using the quantization index modulation (QIM) watermarking method. The QIM method is further combined with dither modulation (DM) to enhance secrecy of watermark. In this paper, we improve the imperceptibility of watermarked PDF document by investigating into the DM operation. The proposed method selects the best secret key among some candidates which minimizes the degradation caused by embedding watermark. Information about the selected key is also embedded as side information so that the embedded watermark can be extracted without referring to the original PDF document. The degree of distortion caused by watermark embedding is quantitatively measured in our experiment. It is verified that the gain in terms of signal-to-noise ratio in the proposed method is increased with the number of secret key candidates.
A Logic of Knowledge and Belief Based on Abstract Arguments

We introduce a logic of knowledge and belief in a framework in which belief has a standard KD45 characterization and knowledge undergoes the classical tripartite analysis that knowledge is justified true belief, which has a natural link to the studies of logics of evidence and justification. The characterization of knowledge is based on a flexible model that avoids unwanted properties concerned with the problem of logical omniscience. We axiomatize the logic, prove its soundness and completeness, and then extend the logic to a multi-agent setting. We also compare our framework with existing logics of knowledge and belief.
On pruning search trees of impartial games

In this paper we study computing Sprague-Grundy values for short impartial games under the normal play convention. We put forward new game-agnostic methods for effective pruning search trees of impartial games. These algorithms are inspired by the α-β, a well-known pruning method for minimax trees. However, our algorithms prune trees whose node values are assigned by the mex function instead of min/max. We have verified the effectiveness of our algorithms experimentally on instances of some standard impartial games (that is Nim, Chomp, and Cram). Based on the results of our experiments we have concluded that: (1) our methods generally perform well, especially when transposition table can store only a small fraction of all game positions (which is typical when larger games are concerned); (2) one of our algorithms constitutes a more universal alternative to the state-of-the-art algorithm proposed by Lemoine and Viennot.
Driver Gaze Region Estimation Based on Computer Vision

Traditional methods of driver's gaze estimation usually need additional equipment to obtain the driver's facial and eye features, which is difficult to apply in real life. In this paper, a gaze region estimation method based on computer vision is proposed, which reduces the hardware requirements of the experiment. In order to achieve this goal, a new eye feature extraction method and an improved random forest algorithm are adopted in this paper. Finally, the driver's line of sight is determined by combining the head and eye features. The driver's head features are obtained by pose from orthography and scaling with iterations (POSIT) algorithm. Then, the accurate positions of the driver's corner of the eye and the pupils are obtained by the from coarse to fine method, and the eye line direction is estimated according to the relative position of the pupil in the eye area. Finally, the improved random forest is used to estimate the driver's gaze region. The experimental results show that the accuracy of this method is 94.12% in the real driver's gaze data set.
Application Research of English Scoring Based on TF-IDF Clustering Algorithm

Abstract In recent years, with the promotion and application of artificial intelligence technology in all fields, English has also had a lot of attention and automatic grading areas, but not much in the text of the characterization of breakthrough, the traditional technology based on latent semantic analysis of the text said, more latent semantic analysis technology can extract thematic information, and information is ignored. The purpose of this paper is to better reflect the content of the text, evaluate the English level more accurately, and improve students’ English level better. This paper proposes a text representation method based on word vector clustering and a text representation method based on vector space model. A TF-IDF algorithm based on word vector is proposed. It is verified that the quadratic weighted Kappa value of the prediction results of the multi-model fusion algorithm based on word vector is better than the first place in Kaggle international English composition scoring competition, which verifies the effectiveness of the algorithm.
Thai-English and English-Thai Translation Performance of Transformer Machine Translation

In this paper, the machine translation models were applied to the Thai-English and English-Thai machine translation task. We investigated three models of machine translation on Thai and English sentence pairs. The translation performance of the transformer model is better than that of the recurrent neural network and the traditional statistical machine translation models. We found that the BLEU scores of the transformer model were the highest in both Thai-English (44.22%) and English-Thai (46.48%) translations. Besides, the results were also analysed linguistically. In comparison with the three models, the errors about detailed description and wrong word ordering were mostly found in the SMT model, whereas wrong word choice and missing words were mostly found in the RNNs model. Although the transformer model could perform much better than others, three error categories - under-translation, over-translation, and incorrect lexical choice-were also found.
Object Distance Estimation with Machine Learning Algorithms for Stereo Vision

This paper presents a novel distance estimation to calculate distances from the stereo camera to the object accurately. This study collected stereo camera images as a dataset, each object determined at two different lighting environments and five different distances between the stereo camera and the object. To estimate the distance, researchers applied supervised learning methods to approach this task. There were performed with two machine learning algorithms: Linear Regression, and Artificial Neuron Network Regression. In the experimental results, the efficiency of the proposed method was examined by using the evaluation metrics to calculate the distance estimation errors. The results showed the model of convolutional neuron networks operated with densely connected neuron networks has the lowest errors rate in comparison with other models. The model eliminates the error rate of distance estimation at 0.000531, 0.014490, and 0.000048 meters, measured by mean square error, mean absolute error and mean logarithmic error respectively.
A Deep Forest Method for Classifying E-Commerce Products by Using Title Information

E-commerce platforms, such as Amazon, eBay and Tmall, are flooded with various types of products. These platforms need to classify the products to facilitate product management and recommendation, which however can be very costly by using manual work. Recently, ML-based classification technology, e.g. SVM and DL, has been widely used in industry to classify e-commerce products by using the text information in the titles given by the merchants. However, current techniques can be inefficient and inaccurate when the number of categories is large and the data scale is small, as in the e-commerce product classification problem. In this paper, we propose a novel machine learning method for the problem, referred to as gcForest, which utilizes the cascade forest of decision trees and multi-grained scanning mechanisms. After preprocessing the product title information by using a word examination technology, the TF-IDF algorithm, we carry out a serials of experiments with 4000 samples belonging to 35 categories of products. The experiment results show that the classification accuracy using gcForest is 92.38%, which outperforms SVM with RBF kernel (86.88%), SVM with linear kernel (89.73%) and CNN (86.86%).
A Restaurants Recommendation System: Improving Rating Predictions Using Sentiment Analysis

Most users often find themselves in a situation when they need to answer the same question: which product, movie, vacation offer, restaurant or book is the best to choose? In order to answer this question, Recommendation Systems have been developed to generate the best suggestions according to the user's interest. Recommendation Systems play an important role in the user's decision-making process by enriching its experience and satisfaction, considering his peers' actions and preferences. The goal of this paper is to enhance the recommendation process by applying Sentiment Analysis techniques on the input data. Sentiment Analysis is a domain that focuses on classifying information into positive, negative and neutral opinions. The results of a Sentiment Analysis task can be used to define social tendencies, items' popularity and adapting the services for users' needs. The proposed approach combines Sentiment Analysis and Recommendation Systems for defining the best suggestions for a user. Sentiment Analysis is applied for classifying restaurants' text-based reviews into positive and negative. The output of the Sentiment Analysis task is passed to a recommendation system that, using the collaborative filtering algorithm, will predict the rating for a not-visited restaurant and generate a list of top-n restaurants for the user. This approach outperformed the results obtained when the Sentiment Analysis step was not considered in the recommendation process. Therefore, the proposed system increases the accuracy of the recommended items by analyzing, from a sentiment point of view, the text-based reviews offered by users.
An OWL Based Ontology Model for Soils and Fertilizations Knowledge on Maize Crop Farming: Scenario for Developing Intelligent Systems

The exponential growths of electronic data in heterogeneous forms cut across all real-life scenarios and disciplines, agriculture for instance. Besides, the volume and varieties of these data in various repositories across the global space is on one hand a heartwarming development and on the other hand, gradually becoming a challenge in terms of relevant information retrieval as a result of ambiguities in natural languages. Accessing knowledge in respect to soils and fertilizers that can affects maize crop during planting stage is very significant in order to improve and maintain the crop's maximum yields. In lieu of this, a cutting-edge technology that is promising towards mitigating this challenge of retrieving relevant information is by modeling data ontologically. Ontology is a data modeling technique for knowledge representation in a machine understandable format. Therefore, this paper aims to model an OWL-based ontology for soils and fertilization knowledge that can assist in a better knowledge of soil and appropriate measures of fertilizers to apply for maize crop. The domain-based ontology is designed using hybridization of Fox-Gruninger, Methontology and FAO-Based methodologies and written using OWL2 Web Ontology Language RDF/XML syntax. The correctness of the ontology's content and correctness of the ontology development have been constantly validated by the domain experts and via experiments. The proposed system would provide a well-structured knowledge-based system for complex queries on soils and fertilizers knowledge that can affect maize crop in a more accurate and timely information.
A Comparison of Retrieval Result Relevance Judgments Between American and Chinese Users

Relevance judgment plays an extremely significant role in information retrieval. This study investigates the differences between American users and Chinese users in relevance judgment during the information retrieval process. 384 sets of relevance scores with 50 scores in each set were collected from 16 American users and 16 Chinese users as they judged retrieval records from two major search engines based on 24 predefined search tasks from 4 domain categories. Statistical analyses reveal that there are significant differences between American assessors and Chinese assessors in relevance judgments. Significant gender differences also appear within both the American and the Chinese assessor groups. The study also revealed significant interactions among cultures, genders, and subject categories. These findings can enhance the understanding of cultural impact on information retrieval and can assist in the design of effective cross-language information retrieval systems.
Investigation of Feature-Based Nonrigid Image Registration Using Gaussian Process

For a wide range of clinical applications, such as adaptive treatment planning or intraoperative image update, feature-based deformable registration (FDR) approaches are widely employed because of their simplicity and low computational complexity. FDR algorithms estimate a dense displacement field by interpolating a sparse field, which is given by the established correspondence between selected features. In this paper, we consider the deformation field as a Gaussian Process (GP), whereas the selected features are regarded as prior information on the valid deformations. Using GP, we are able to estimate the both dense displacement field and a corresponding uncertainty map at once. Furthermore, we evaluated the performance of different hyperparameter settings for squared exponential kernels with synthetic, phantom and clinical data respectively. The quantitative comparison shows, GP-based interpolation has performance on par with state-of-the-art B-spline interpolation. The greatest clinical benefit of GP-based interpolation is that it gives a reliable estimate of the mathematical uncertainty of the calculated dense displacement map.
Imaging with Distributed Lensless Line Sensors

Recently, mask-based camera has been proposed by replacing the lens with a coded-mask that modulates light as it reaches the sensor. Such mask-based cameras can reliably capture both direction and depth information about the scene. However, current designs of lensless camera contain only one sensor array. In contrast to that, using multiple distributed sensor arrays to image the scene may offer us many advantages, such as more flexible placing strategy and different views of the scene. In this paper, we present the distributed mask-based line cameras, in which we replace a rectangular mask-based sensor with multiple distributed thin line sensors. To retrieve the information from the measurements, we propose an algorithm that jointly estimate the depth and light intensity of the scene voxels. Simulation results are also presented for different placement strategies and widths of the sensor arrays.
Using Combinations of Bio-inspired Feature Selection Algorithms in Software Efforts Estimation: An Empirical Study

Feature selection algorithms are used to extract the most relevant features from a dataset and filter those which may affect the accuracy of the estimation techniques. A variety of features selection algorithms are investigated in the context of software engineering; however, the use of the bio-inspired algorithms has become very popular in the last few years, particularly in the domain of the software efforts estimations. However, sometime a single and standalone bio-inspired algorithm is not fit enough to select the most relevant features from a particular dataset and hence, the researchers have started to employ the hybrid of these algorithms. In this paper, we have investigated the use of various combinations (hybrid) of bio-inspired algorithms with the variety of datasets used and have compared their performance with the state-of-the-art standalone bio-inspired feature selection algorithms. The datasets employed in this case are Albrecht, China, COCOMO, Finnish, Kemerer, Maxwell, Miyazaki and NASA and the estimation techniques used are Support Vector Regression (SVR) and Random Forest (RF). We have obtained different results based on the datasets and estimation techniques.