Reviewerly/exHarmony huggingface dataset:
Semi-automated counting model for arbuscular mycorrhizal fungi spores using the Circle Hough Transform and an artificial neural network

Arbuscular Mycorrhizae (AM) are mutualistic associations between Arbuscular Mycorrhizal Fungi (AMF) and the roots of many plant species. AMF spores give rise to filaments that develop in the root system of plants and contribute to the absorption of water and some nutrients. This article introduces a semi-automated counting model of AMF spores in slide images based on Artificial Neural Network (ANN). The semi-automated counting of AMF spores facilitates and accelerates the tasks of researchers, who still do the AMF spore counting manually. We built a representative database of spore images, processing images through the Circle Hough Transform (CHT) method and training an ANN to classify patterns automatically. The classification analysis and the performances of the proposed method against the manual method are presented in this paper. The accuracy for the identification of spores by CHT in conjunction to ANN classification in the images was 90%. The results indicate that this method can accurately detect the presence of AMF spores in images as well as count them with a high level of confidence.
Heuristic Scheduling Algorithm for Cloud Workflows with Complex Structure and Deadline Constraints

Nowadays, many large-scale scientific workflows are deployed in the cloud which provides a platform to run workflow at lower cost without any infrastructure. However, there are many challenges about how to effectively schedule and deploy workflow applications to guarantee QoS of different users. In this paper, a heuristic scheduling algorithm, named DR-LS (Dependency Relationship-List Scheduling) is proposed to minimize the cost of workflow application while satisfy the user-defined deadline constraint. In the proposed algorithm, we introduce the concept of task dependency during the task priority calculation phase, and use the heuristic method, i.e. Probabilistic Upward Rank to distribute the whole deadline fairly to each task, then select the resources with the least cost increment for the current task to satisfy its corresponding sub-deadline. Our approach is verified by WorkflowSim for four well-known scientific workflows with different sizes, and the experiment results show it outperforms IC-PCP and ProLis, especially for the workflows with complex topological structures, such as Montage.
A Fast Path Planning Method for Mobile Robot Based on Voronoi Diagram and Improved D* Algorithm

Fast path planning is hard to achieve for a mobile robot due to the difficulties in balancing the efficiency and distance security requirements between the desired path and obstacle. To address this problem, a practical path planning method is proposed by combining Voronoi and D* (VD*) algorithm. A Voronoi-based method is presented to re-divide the map and re-fit the obstacles so that the intensity of the sampling points is specified satisfactorily and the consuming time can be mitigated. Moreover, by utilizing the improved VD* algorithm, the search step size is regulated adaptively to speed up the planning convergence. Thus, an efficiently-planned and smooth path can be guaranteed. Moreover, the effectiveness and superiority of the proposed method is verified by simulations in two mobile robot situations, which show reduced calculation burden while achieving a smooth and short path.
Dramatic Hermeneutics as a Perspective Technology in the Artistic Education

Modern artistic education uses widely innovative technologies. The technology of dramatic hermeneutics synthesizes the achievements of practical hermeneutics with the compositional and dramatic properties of art. In the vector of artistic education, it enriches artistic and creative activity with the elements of theatrical pedagogy. In the organizational and methodological aspects, it allows to combine individual and creative, team and group work based on the search and disclosure of artistic, interpretive, figurative and semantic contexts of the works of art. The artistic and semantic basis of the technology of dramatic hermeneutics is the theory of conflict, life contradictions, the removal of which leads to change and transformation of the existence. Dramatic hermeneutics as an innovative technology forms understanding of deep life collisions reflected in works of art through personalities. For the educational process, such personalities are artists, teachers, listeners, characters of the work, anybody who are directly or indirectly involved in the artistic and communicative process. Dramatic hermeneutics as an integration of drama, practical hermeneutics and artistic creativity is used in three varieties, namely: understanding the experience of the creative personality; integral processes of artistic thinking; collective presentation of the artistic idea interpretation. This predetermines the formation of the dramatic and hermeneutic competence of future art teachers – their ability to apply the pedagogical potential of linking art of different styles and genres with the realities of life in artistic and educational processes.
An introduction to the civil registration and vital statistics systems with applications in low- and middle-income countries

Abstract In collaboration with development partners, the World Bank Group (WBG) has been working to strengthen civil registration and vital statistics (CRVS) systems in low- and middle-income countries through lending operations, technical assistance projects, advisory services and analytics, and knowledge sharing at various international, regional, and national conferences and fora and through publications. In 2017, it launched a comprehensive CRVS eLearning course, which provides practical tools and approaches to achieving twenty-first-century state-of-the-art CRVS systems that are linked to identity management systems and are tailored to local contexts. Some of the key lessons learned from the various initiatives and projects are presented in the eight peer-reviewed manuscripts included in this issue.
Aeroradiospectrometry in the spatial formation characterization of Ogun State, south-western, Nigeria

In this study, aeroradiospectrometric data sets of Ogun State, Latitudes (6.2° to 7.8° N) and Longitudes (3.0° to 5.0° E), Nigeria were analyzed for lithological characterization. The data used comprised of Total count (TC), Uranium (U), Potassium (K) and Thorium (Th). The major objectives of this study are to characterize and identify the anomalies associated with various Interpreted Radiometric Lithological (IRL) units with respect to the three radioelements as well as Total count. The lithologies of the study area were described by three formations (basement, weathered basement and sediment), from which forty- seven IRL units were deduced. The study made use of the geological map in conjunction with the petrographic map as a tool to examine the three formation groups. The Interpreted Radiometric Lithologic (IRL) units in each formation group were subjected to the inferential test to examine the homogeneity in the total count of various rock units by adopting T-test and F-test. Multivariate statistical analysis was also conducted on the radioactive data to ascertain the radioactive variable that was associated with the same bedrock source. The results obtained showed that the anomalous responses of radiometric parameters to bedrock units were observed to be more prevalent in the Basement (B) and Weathered Basement group (WB). Majority of the radioelements deposition of 67.5%, 48.6% and 48.5% for potassium, thorium, and uranium respectively; were found in sediment (S) formation. This may be attributed to geomorphic activity such as weathering, leaching, and intrusion. Inferential test results showed that up to 26 and 32 pairs in the basement while 3 and 5 pairs in weathered basement were found to be F-test and T-test statistically and aeroradiometrically homogeneous at 5% significant level. Of the 13 pairs identified for the two tests in sediment formation, only 5 pairs were found to be uniquely homogenous. The results also revealed that most of the formation groups have their uranium and thorium values above the world average. Multivariate analysis revealed thorium and uranium as the major constituent of the total count. This study has therefore successfully characterized and identified the formations associated with anomalous response in bedrock units of the study area.
Part 1:- quality assurance mechanisms for digital forensic investigations: Introducing the Verification of Digital Evidence (VODE) framework

Quality assurance measures in the field of digital forensics play a vital role for upholding and developing investigatory standards. Coupled with the fast pace of technology, practitioners in this discipline are often faced with the challenge of interpreting previously unseen or undocmented forms of potentially evidential digital data, content which may be crucial to a current case under investigation. Mechanisms to support this interpretative process offer support for the practitioner, helping to guide them through this task and the steps involved in ensuring any reported information is accurate. This work presents the Verification of Digital Evidence (VODE) framework, designed to support digital forensic practitioners when testing and verifying their interpretation of digital data. The stages of VODE are discussed and its application placed in context.
Developing an Arabic Handwritten Recognition System by Means of Artificial Neural Network

The matter of handwritten text recognition is as yet a major challenge to mainstream researchers.A few ways deal with this challenge have been endeavored in the most recent years, for the most part concentrating on the English pre-printed or handwritten characters space.Consequently, the need to effort a research concerning to Arabic texts handwritten recognition.The Arabic handwriting presents unique technical difficulties because it is cursive, right to left in writing and the letters convert its shapes and structures when it is putted at initial, middle, isolation or at the end of words.In this study, the Arabic text recognition is developed and designed to recognize image of Arabic text/characters.The proposed model gets a single line of Arabic text, which convert and segments into words and then segments into letters.A multilayer feed forward neural network is trained to recognize these segments as characters.The final results indicate and clarify that the proposed system perform an effective accuracy of recognition rated up to 83% for Arabic text.
Robust Spoken Language Understanding with Acoustic and Domain Knowledge

Spoken language understanding (SLU) converts user utterances into structured semantic forms. There are still two main issues for SLU: robustness to ASR-errors and the data sparsity of new and extended domains. In this paper, we propose a robust SLU system by leveraging both acoustic and domain knowledge. We extract audio features by training ASR models on a large number of utterances without semantic annotations. For exploiting domain knowledge, we design lexicon features from the domain ontology and propose an error elimination algorithm to help predicted values recovered from ASR-errors. The results of CATSLU challenge show that our systems can outperform all of the other teams across four domains.
Abnormal Crowd Behavior Detection Based on Predictive Neural Network

In recent years, malignant crowd incidents that endanger public safety occur frequently, which alarms humans of the security in public areas. Focus on the detection of abnormal crowd behavior has been heightened in the field of computer vision. To solve this problem, the predictive neural network is innovatively applied to crowd anomaly detection by enlarging the differences between predictive frames and real frames in moving object regions. Firstly, aiming at the drawback of slow convergence speed of running Gaussian average model for object detection, an improved attenuation strategy based on learning rate is proposed. Then the processed foreground data set is put into the predictive neural network, and we define the degree of anomaly as the differences between predictive frames and real frames. By adjusting the threshold adaptively, the abnormal behavior in the crowd can be judged. The experimental results show that, compared with optical flow method and social force model, this method is not only fast in calculation, but also can detect crowd anomalies accurately and effectively. The accuracy rate of this method in UMN data set is up to 97.7%.
Manifesting a mobile application on safety which ascertains women salus in Bangladesh

This paper reflects on the indemnity of women in our society. The proposed model ensures the embodiment of a mobile application. The algorithm, we developed for this model focuses the safety issues which is applicable to both inside as well as outside of the house for the women in Bangladesh. The solution of this problems can be done through some interrelated features such as i) SOS button pressing which ensures automatic calling, instant location tracking system through GPS of the phone and sending tracked location to all trusted numbers, automatically secrete video recording system ii) voice command detection which assures exact same features as SOS button pressing iii) phone shaking features serve user instant immunity by calling a trusted number. This research also assures experimented data analysis at Dhaka city based on respond time, the time it takes to arrive the SMS and Phone call and current location of the victim. Also do a short comparison among the most popular safety related mobile applications.
Enabling Deeper Linguistic-Based Text Analytics—Construct Development for the Criticality of Negative Service Experience

Significant progress has been made in linguistic-based text analytics particularly with the increasing availability of data and deep learning computational models for more accurate opinion analysis and domain-specific entity recognition. In understanding customer service experience from texts, analysis of sentiments associated with different stages of the service lifecycle is a useful starting point. However, when richer insights into issues associated with negative sentiments and experiences are desired to inform intervention, deeper linguistic analyses such as identifying specific touchpoints and the context of the service users become important. While research in this direction is beginning to emerge in some domains, we are yet to see similar efforts in the domain of healthcare. We present in this paper the results from our construct development effort for quantifying how critical a negative patient experience is using different elements of the available textual feedback as a key basis for prioritizing interventions by service providers. This involves the identification of the different dimensions of the construct, associated linguistic markers and metrics to compute the criticality index. We also present the results of the application of our developed conceptualization to linguistic-based text analysis of a small dataset of patient experience feedback.
Assessment of Cr(VI) Biosorption from Aqueous Solution by Artificial Intelligence

This work investigates the efficiency of date palm fiber as a biosorbent to the removal of Cr(VI) from aqueous solution. The effect of four main factors including, the initial pH, initial Cr(VI) concentration, biosorbent dosage, and time were assessed. The results revealed that, the maximum 89% removal of Cr(VI) at time 50 min, initial pH 3.2, initial Cr(VI) 181 ppm, and biosorbent dosage 0.8% (w/v) was obtained. The artificial neural network (ANN) model was developed to predict the Cr(VI) removal as a function of the studied variables. A number of data points were considered as test data to validate the model. The results indicated a high accuracy of the ANN modeling in estimating the target variable.
Image Lag Elimination Algorithm for Pulse-Sequence-Based High-Speed Image Sensor

When the pulse-sequence-based image sensor films high-speed moving objects, the image lag problem may appears as blurring in reconstructed images. In this paper, an algorithm is proposed to suppress lags by distinguishing the lag area and calculating ratio of bright to dark part in one trigger interval. The algorithm bases on the sensor's characteristics and image lag principle that the change of light intensity cannot be immediately reflected on the pulse generated from photoelectric accumulation. The effectiveness of the algorithm is verified both on the sensor's behavior-level model and the real image sensor. In both experiments the curves of LSF (line spread function) and MTF (modulation transfer function) show the reconstructed edges after lag elimination algorithm are similar to the real edges both in sharpness and position. The indexes to evaluate image similarity including LMD (location mean deviation), PSNR (peak signal to noise ratio) and SSIM (structural similarity index) improve respectively. In the experiment on sensor's model, when the simulation velocity of edge is low, the algorithm can reconstruct the perfect edges as input images. As velocity increases, the reconstructed edges are still sharp but with slight shifts. In the real experiment to film a turntable with a rotation speed of 6000r/min, the LMD, PSNR and SSIM improve from 22.2833 to 2.5167, 18.2485 to 21.8603 and 0.8345 to 0.9364 respectively. These results show that the algorithm can suppress image lag and improve reconstructed image quality.
TC-GAN

Face frontalization has always been an important field. Recently, with the introduction of generative adversarial networks (GANs), face frontalization has achieved remarkable success. A critical challenge during face frontalization is to ensure the features of the original profile image are retained. Even though some state-of-the-art methods can preserve identity features while rotating the face to the frontal view, they still have difficulty preserving facial expression features. Therefore, we propose the novel triangle cycle-consistent generative adversarial networks for the face frontalization task, termed TC-GAN. Our networks contain two generators and one discriminator. One of the generators generates the frontal contour, and the other generates the facial features. They work together to generate a photo-realistic frontal view of the face. We also introduce cycle-consistent loss to retain feature information effectively. To validate the advantages of TC-GAN, we apply it to the face frontalization task on two datasets. The experimental results demonstrate that our method can perform large-pose face frontalization while preserving the facial features (both identity and expression). To the best of our knowledge, TC-GAN outperforms the state-of-the-art methods in the preservation of facial identity and expression features during face frontalization.
Learn Smart with Less: Building Better Online Decision Trees with Fewer Training Examples

Online decision tree models are extensively used in many industrial machine learning applications for real-time classification tasks. These models are highly accurate, scalable and easy to use in practice. The Very Fast Decision Tree (VFDT) is the classic online decision tree induction model that has been widely adopted due to its theoretical guarantees as well as competitive performance. However, VFDT and its variants solely rely on conservative statistical measures like Hoeffding bound to incrementally grow the tree. This makes these models extremely circumspect and limits their ability to learn fast. In this paper, we efficiently employ statistical resampling techniques to build an online tree faster using fewer examples. We first theoretically show that a naive implementation of resampling techniques like non-parametric bootstrap does not scale due to large memory and computational overheads. We mitigate this by proposing a robust memory-efficient bootstrap simulation heuristic (Mem-ES) that successfully expedites the learning process. Experimental results on both synthetic data and large-scale real world datasets demonstrate the efficiency and effectiveness of our proposed technique.
Reconfigurable Silicon Photonic Processor Based on SCOW Resonant Structures

Reconfigurable photonic processors, which can be programmed to perform multiple photonic processing tasks by using the same hardware platform, own the advantages of higher flexibility and more cost-effectiveness compared with application-specific photonic integration circuits (ASPICs). In this paper, we present a novel programmable photonic processor based on two-dimensional meshes of self-coupled optical waveguide (SCOW) resonant structures. The proposed processor can be configured for realizing various basic optical components, as well as cascaded and coupled components. As a proof-of-principle, we experimentally demonstrate the concept with a 3 × 1 SCOW-based processor on the silicon platform, including tunable couplers, variable optical attenuators, and phase shifters. We implement eight different configurations using the chip, including ring resonators, Mach-Zehnder interferometers, Fabry-Perot resonators, and composite structures built of these basic components. These results demonstrate that the proposed processor can be a promising candidate for multi-functional photonic processors.
Digital technology for Indonesia’s young people

Indonesia has recognized the growing demand for information technology, with the most rapid adoption of this technology being amongst its young people. Within the perspective of a balanced knowledge information society, the adoption of technology is crucial for improving social development in developing countries and is considered a particularly important tool in education. The education sector is well-placed to harness the potential of ICT for the millennial generation in these societies. Based on a survey of students enrolled in a university in Jakarta, this study attempts to identify the learning and development opportunities available through digital technology by exploring both how university students in Indonesia use this technology, and how they experience and perceive the benefits of digital learning. In particular, we examine how their access to and use of technology is associated with their digital literacy and their perceived usefulness of ICT. In general, Indonesian university students are actively engaged in a number of diverse activities online, including educational activities, which in turn result in increased online learning outcomes. A better understanding of the role of digital literacy and learning environments and effective use of technology for learning can provide important insights into education and technology use in Indonesia and similar contexts.
Image Steganography Using LSB Substitution: A Comparative Analysis on Different Color Models

Information hiding for secure communication has been used for a long time. Steganography is a very popular technique of hiding secret information in other media. The object used to hide the secret information is called the cover object. Different types of cover objects like text, audio, image, and video are used, but image is the most popular among all cover objects. Nowadays color image processing is widely utilized in multimedia, graphics and computer vision applications. Color image can hold a large amount of secret data as compared to the grey image since they have different color components. One of the popular approaches is the substitution of the least significant bits of the cover image by the secret information. Different color models, namely, RGB, YCbCr, YUV, YIQ, CMY, HIS, HSV are used to represent the color image. We have proposed a LSB substitution based algorithm and its performance has been compared for different color models (RGB, YCbCr, YUV, and CMY).