Reviewerly/exHarmony huggingface dataset:
Hydrostratigraphy of the Malmani Subgroup dolomites within the northeastern escarpment (Limpopo and Mpumalanga, South Africa)

Abstract The Late Archaean to Early Proterozoic Malmani Subgroup comprises of dolomites and limestones forming part of the Chuniespoort Group within the Transvaal Supergroup, outcropping as an arc structure east of the Pretoria Group along the Limpopo and Mpumalanga escarpment. These rocks form a fractured karst aquifer in the area and have a high degree of heterogeneity and anisotropy. The aquifers are unconfined to semi-confined, with compartmentalisation by dolerite dykes being a possible effect (if the dykes are large and extensive enough) due to the dykes acting as aquitards or barriers to groundwater flow. The contact zones between the dolomite formations and dolerite dykes are usually fractured however, and along with any other faults and fractures result in preferential dolomite dissolution and the development of groundwater flow paths in the area. Borehole yields ranges between 2 to 5 l/s and potentially &amp;gt;10 l/s per borehole in the vicinity of large regional fractures or dolerite intrusions. Groundwater from the Malmani Subgroup generally meets the drinking water quality standards for major constituents and it is of Mg-Ca-HCO3 nature. Groundwater development within this particular hydrostratigraphy is linked to potential well field target zones that take cognisance of various surface water-groundwater interaction affecting surface water discharge rates as well as groundwater over-abstraction concerns. Preliminary results have indicated that given a groundwater potential of 44 hm3/a, the aquifer will be able to support abstractions of up to 29 hm3/a if systematically developed adaptively and could be used and managed conjunctively with surface water to alleviate the pressure on the already stressed Olifants Water Management Area.
Guideline for Academic Support of Student Career Path Using Mining Algorithm

In general, higher education is an important step in preparing a career for students in the future. Graduates should have qualifications that are recognized by both entrepreneurs and society. Therefore, every higher educational institution should make an effort to consider how to assist students' performance. This research aims to analyze the relationships between courses that are likely to produce a future career for students using the Apriori algorithm. The data used in the operation of the association rule was the student's grades from 25 main courses in the field of information technology, Department of Information Technology, Faculty of Science and Technology, Suan Sunandha Rajabhat University. This data was recorded between 2011 and 2019 and stored in the registration and graduate career system. The 14 association rules were determined from the operation by using the Weka 3.8.3 data mining software, this indicated that there were a few courses in which students could have future careers. Most importantly, the results can contribute to guidelines for the academic support of students' future career.
Texture Deformation Based Generative Adversarial Networks for Multi-domain Face Editing

Despite the significant success in image-to-image translation and latent representation based facial attribute editing and expression synthesis, the existing approaches still have limitations of preserving the identity and sharpness of details, and generating distinct image translations. To address these issues, we propose a Texture Deformation Based GAN, namely TDB-GAN, to disentangle texture from original image. The disentangled texture is used to transfer facial attributes and expressions before the deformation to target shape and poses. Sharper details and more distinct visual effects are observed in the synthesized faces. In addition, it brings faster convergence during training. In the extensive ablation studies, we also evaluate our method qualitatively and quantitatively on facial attribute and expression synthesis. The results on both the CelebA and RaFD datasets suggest that TDB-GAN achieves better performance.
Speech Error Detection depending on Linguistic Units

In this research, we aim at the construction of a system which detects, points out and corrects speech error (slip of the tongue) of a human speech that occurs in a dialogue system (example: Pepper, Amazon Echo, Google Home) and a human dialogue. In the present dialogue system, even if human makes a speech error, the system cannot recognize it, which could lead to broken communication. So far, we have created a system to detect speech error using deep learning. In this study, we propose a method to augmented training data used for deep learning. The training data is a corpus that collects examples of speech error. At present, the number of training data is insufficient to detect with high accuracy. Therefore, it is necessary to augment the training data. Specifically, the feature of the speech error is examined from an existing speech error corpus, and extended rules are created. The data augmentation of training data is performed by generating dialogue sentence which made the speech error based on the rule. As a result of evaluation experiment, detection accuracy was improved in LSTM model by data augmentation.
Simultaneous Exploration and Coverage by a Mobile Robot

In this paper, we propose a problem of simultaneous exploration and coverage for a mobile robot, combining the problems of area coverage with exploration and mapping. The primary task here is to completely cover an initially unknown region. Here we combine the advantages of online and off-line coverage path planning algorithms by using the exploration as an aid. The robots perform intermittent exploration during coverage in order to update the map of the environment, which in turn is used to generate the coverage path. We illustrate and demonstrate the problem using the off-line version of Spanning Tree Coverage algorithm with a frontier-based exploration strategy. The simulation results demonstrate that the robot successfully achieves complete and non-repetitive coverage.
Identification of Usability Impact of Mobile Learning STEM in a Local University Context

Despite the rapid progress of the Science, Technology, Engineering, and Mathematics (STEM) subjects in developed countries, in Malaysia, STEM is still lagging. Malaysia also lacks the local digital content for STEM, including mobile learning. Furthermore, from previous studies, the Human Computer Interaction (HCI) of local cultural aspects has been overlooked, which is unfortunate, as they can contribute to better usability design of the mobile devices and their apps. Therefore, this study was conducted to investigate the insights of the impact of the usability factors in terms of the general usability and culturally appropriate design factors of mobile devices, such as smart phones. The methodology deployed is largely qualitative with focus group discussions with respondents from a local university. The findings highlight insights, such as general usability and the culturally appropriate design of smart phones. This study can be of benefit as it provides insights concerning the usability impact factors for the local context.
Graph Embedding for Offline Handwritten Signature Verification

Due to the high availability and applicability, handwritten signatures are an eminent biometric authentication measure in our life. To mitigate the risk of a potential misuse, automatic signature verification tries to distinguish between genuine and forged signatures. Most of the available signature verification approaches make use of vectorial rather than graph-based representations of the handwriting. This is rather surprising as graphs offer some inherent advantages. Graphs are, for instance, able to directly adapt their size and structure to the size and complexity of the respective handwritten entities. Moreover, several fast graph matching algorithms have been proposed recently that allow to employ graphs also in domains with large amounts of data. The present paper proposes to use different graph embedding approaches in conjunction with a recent graph-based signature verification framework. That is, signature graphs are not directly matched with each other, but first compared with a set of predefined prototype graphs, in order to obtain a dissimilarity representation. In an experimental evaluation, we employ the proposed method on two widely used benchmark datasets. On both datasets, we empirically confirm that the learning-free graph embedding outperforms state-of-the-art methods with respect to both accuracy and runtime.
The square root rule – a case study of a scaling factor for machines with dynamic similitude

This paper aims to present a case study where a scaling factor was used to design a series of toggle style injection molding machines for the plastics industry. The series ranged from 100 to 1000 US tons of clamping capacity. Every machine in the series was scaled down or up from a prototype that had undergone extensive fatigue testing before the decision to design an entire range of machines. In this study, a comparison is made between a prototype machine with 250 US tons of clamping force and a scaled-up machine with 1000 US tons of clamping force. Finding dimensionless scaling factors when designing a series of machines requires machines that have dynamic similitude. To qualify, there must be geometric similarity, dynamic similarity, and kinematic similarity. In the case study presented below, the geometric (static) similarity will be forced to exist, and the dynamic similarity and kinematic similarity will be shown to result from this. A dimensionless scale factor (square root rule) was derived by scaling dimensions based on the square root of the clamping force ratio. As each size machine was built, measurements showed that stresses in all critical points were the same as at corresponding points on the prototype. Also, deflections in each direction followed the scale factor. Static relationships in the toggle systems followed the scale factor, and critical positions of the linkage system all occurred at the same link angles. By using this scale factor, the company saves thousands of man-hours in design effort, completes the design for the entire line and gets to the market in less than half the weeks normally required for such an effort. Additional benefits are: (i) very high confidence level in resistance to failure by metal fatigue, (ii) very accurate advanced information on tooling requirements for manufacturing, and (iii) very accurate advanced marketing and sales information on performance specifications, floor space requirements, and energy requirements for the entire range.
Syntactic Cut-Elimination and Backward Proof-Search for Tense Logic via Linear Nested Sequents

We give a linear nested sequent calculus for the basic normal tense logic $$\mathsf {Kt}$$ . We show that the calculus enables backwards proof-search, counter-model construction and syntactic cut-elimination. Linear nested sequents thus provide the minimal amount of nesting necessary to provide an adequate proof-theory for modal logics containing converse. As a bonus, this yields a cut-free calculus for symmetric modal logic $$\mathsf {KB}$$ .
Research on Matching Accuracy of Underwater Terrain Matching Algorithm Based on State Augmented Unscented Kalman Filter

Underwater high-precision positioning is the basis of safe sailing and smooth operation for underwater autonomous vehicle (AUV). With the continuous expansion of marine surveys, the requirements for navigation accuracy of AUVs are also increasing. As the unscented Kalman filter (UKF)-based terrain matching method can meet the high-precision positioning demand of underwater navigation, it becomes one of the hot research content in recent years. However, due to the insufficient robustness of UKF algorithm, under the condition of non-additive state noise, the initial distribution of Sigma points is prone to be unreasonable, which will reduce filter accuracy and even cause the recursive result divergence. In order to improve the accuracy and robustness of the underwater terrain matching algorithm, this paper introduces the method of nonlinear filtering state expansion. The system noise and measurement noise information are added into the system state matrix and the variance matrix, and obtains the state expansion UKF-based terrain matching algorithm. Since the noise and the estimation parameters are both participating in the filtering operation, the inaccurate prediction influence of initial noise can be reduced, and finally improves the accuracy and applicability of the underwater terrain matching algorithm.
Faster Quantum Alternative to Softmax Selection in Deep Learning and Deep Reinforcement Learning

Deep learning and deep reinforcement learning are, at present, the best available machine learning tools for use in engineering problems. However, at present, the use of these tools is limited by the fact that they are very time-consuming, usually requiring the use of a high performance computer. It is therefore desirable to look for possible ways to speed up the corresponding computations. One of the time-consuming parts of these algorithms is softmax selection, when instead of selecting the alternative with the largest possible value of the corresponding objective function, we select all possible values, with probabilities increasing with the value of the objective function. In this paper, we propose a significantly faster quantum-computing alternative to softmax selection.
Joint Correlation Measurements for PRNU-Based Source Identification

Camera fingerprint, namely PRNU, is a multiplicative noise source contained in each image captured by a given sensor. Source camera identification derives from similarity assessment between camera fingerprint and a candidate image; it requires the extraction of image PRNU and the estimation of camera fingerprint. To this aim, a denoising procedure is commonly applied in both cases and correlation is used for assessing similarity. However, correlation measure strongly depends on the accuracy of camera fingerprint estimation and PRNU image extraction. This paper presents a method for making more robust correlation-based source camera identification. It consists of more than one estimation of camera fingerprint; then, identification consists of the quantification of the amount of concurrence between correlation measures. It is expected higher correspondence between measures whenever the candidate image has been captured by a given device (match case); while lack of correspondence is expected whenever the image does not come from the considered device (no match case). Preliminary experimental results show that the proposed joint correlation measurements contribute to improve the precision of correlation-based source camera identification methods, especially in terms of a reduced number of false positives.
An improved blind watermarking method based on SWT and LU decomposition

In order to improve the security of traditional digital image watermarking algorithm, and solve the problem of digital watermark is sensitive to signal processing and geometric distortion. A new digital watermarking algorithm combined with stationary wavelet transform (SWT) and LU decomposition is proposed. Unlike the common transform domain watermarking techniques, this scheme decomposes the whole image with SWT firstly, and then divides the image into 8 x 8 blocks, taking SVD decomposition for each block and selecting an embedded position of each block to add watermarking. Here, instead of embedding the watermark value directly, we decompose the image by LU and embed the key values into S matrix. By calculating the average of all the embedded position value, and comparing each embedded position value with average value, getting the key which is used to realize the blind detection. Experiments show the blind watermarking algorithm is proposed in this paper not only has good fidelity, but also has good robustness to various attacks.
On Three-Valued Acceptance Conditions of Abstract Dialectical Frameworks

Abstract Dialectical Frameworks (adfs) are generalizations of Dung's abstract argumentation frameworks (afs) where each argument has an associated acceptance condition expressed by a boolean formula. The resulting extension is robust enough not only to model the attack relation original to afs, but also others types of dependencies and interactions between arguments. A recent development in adfs proposed an alternative formalization involving three-valued acceptance conditions, connecting the original definitions of adfs to the concept of three-valued argument labellings, a core concept in computational argumentation literature. In this paper, we revise some of the main semantics defined under this three-valued approach and prove our definitions hold equivalence to well-known semantics of afs.
Universal Industrial Interface - Mobile

Robot programming can be a tedious task, especially in industrial environments, where different types of robots are required and in flexible automation lines, where robots need to be frequently reconfigured. There are several shortcomings to be solved in this regard. The main issue being time efficiency. This paper presents a novel solution proposed for robot programming, a mobile application, called Uii Mobile, meant to introduce the smartphone on the shop floor, while facilitating robot programming for operators with basic technical knowledge. The use of Uii Mobile's intuitive graphical user interface (GUI) reduces the time a worker allocates for learning different programming languages. Uii Mobile also enables faster reconfiguration of robots for small, flexible tasks, such as pick and place. Nonetheless a user study using the System Usability Scale (SUS) rates Uii Mobile with a score of 75.38/100 which makes it an above average system, regarding its usability.
An efficient multiple-classifier system for Arabic calligraphy style recognition

This work endeavors to present an automatic system for Arabic-calligraphy style classification. The main issue one faces in order to fulfill such a task is the unavailability of data due to the lack of related works. Therefore, we put forward, and make publically available, a new Arabic calligraphy style dataset. Additionally, we introduce a system for Arabic calligraphy style recognition based on the combination of multi-classifier decisions.
A Secret Image Restoring Scheme Using Threshold Pairs of Unordered Image Shares

Secret image sharing has been widely used in numerous areas such as remote sensing and information security. The goal is to share and recover one or more images using several image shares. However, most schemes only consider ordered shares. In addition, the dependency between the images and the share is determined, that is, which image the share belongs to is determined. In this paper, by using the generalized Chinese remainder theorem method, we propose a secret image sharing scheme using threshold pairs of unordered image shares, where t out of n share pairs are needed to recover the original secret images, even if the affiliation between the shares and the images is disturbed. Compared with existing schemes, the proposed sharing algorithm is more secure and the recovery algorithm is more effective. Moreover, the conditions for the success or failure of recovering two images from their unordered shares are obtained. Simulations are also presented to show the efficiency of the proposed algorithms.
Transforming Derivational Complexity of Term Rewriting to Runtime Complexity

Derivational complexity of term rewriting considers the length of the longest rewrite sequence for arbitrary start terms, whereas runtime complexity restricts start terms to basic terms. Recently, there has been notable progress in automatic inference of upper and lower bounds for runtime complexity. We propose a novel transformation that allows an off-the-shelf tool for inference of upper or lower bounds for runtime complexity to be used to determine upper or lower bounds for derivational complexity as well. Our approach is applicable to derivational complexity problems for innermost rewriting and for full rewriting. We have implemented the transformation in the tool AProVE and conducted an extensive experimental evaluation. Our results indicate that bounds for derivational complexity can now be inferred for rewrite systems that have been out of reach for automated analysis thus far.
Semantic Modeling of Textual Relationships in Cross-modal Retrieval

Feature modeling of different modalities is a basic problem in current research of cross-modal information retrieval. Existing models typically project texts and images into one embedding space, in which semantically similar information will have a shorter distance. Semantic modeling of textural relationships is notoriously difficult. In this paper, we propose an approach to model texts using a featured graph by integrating multi-view textual relationships including semantic relationships, statistical co-occurrence, and prior relationships in knowledge base. A dual-path neural network is adopted to learn multi-modal representations of information and cross-modal similarity measure jointly. We use a Graph Convolutional Network (GCN) for generating relation-aware text representations, and use a Convolutional Neural Network (CNN) with non-linearities for image representations. The cross-modal similarity measure is learned by distance metric learning. Experimental results show that, by leveraging the rich relational semantics in texts, our model can outperform the state-of-the-art models by 3.4% on 6.3% in accuracy on two benchmark datasets.
Beyond p-Boxes and Interval-Valued Moments: Natural Next Approximations to General Imprecise Probabilities

To make an adequate decision, we need to know the probabilities of different consequences of different actions. In practice, we only have partial information about these probabilities—this situation is known as imprecise probabilities. A general description of all possible imprecise probabilities requires using infinitely many parameters. In practice, the two most widely used few-parametric approximate descriptions are p-boxes (bounds on the values of the cumulative distribution function) and interval-valued moments (i.e., bounds on moments). In some situations, these approximations are not sufficiently accurate. So, we need more accurate more-parametric approximations. In this paper, we explain what are the natural next approximations.
Face Recognition Method Based on Fixed and PTZ Camera Control for Moving Humans

Face recognition methods have been generally used for authentication purposes, in which subjects position their faces in the camera field. Lately, face recognition has begun to be used for security applications at specific locations as well as for authentication. Face recognition for security purposes involves human and face detection based on the image created by a fixed camera. Consequently, the success rate for face recognition is low for low-resolution cameras. To address this problem, this study proposes a face recognition method in a multi-camera environment that includes both a fixed and a pan-tilt-zoom (PTZ) camera. The proposed method involves detecting humans within the fixed camera's field of view while tracking the detected person by adjusting the PTZ camera's pan and tilt to keep the person in view. By zooming simultaneously with tracking to obtain a high-resolution facial image, the proposed method outperformed conventional face recognition methods that use only a fixed camera.
Research on Network Security Protection Strategy

Network security strategy is a series of requirements, norms or operations to standardize network security protection, ensure the normal use of the network, and give full play to network efficiency. It is the basis of network security project management. Network security protection strategy is to take full account of the possible harm to the system caused by various security incidents, and make a reasonable and effective network security protection strategy by using the idea of "preparation in advance - defense in the event - response after the event".