Reviewerly/exHarmony huggingface dataset:
Amplification Techniques of Recombinase &amp; Polymerase and their Application in Parasite Detection

To study the application principles of recombinase polymerase amplification (RPA) and the specific situations of detecting parasites, the principles of RPA are analyzed to find the optimal temperature conditions, advantages, and disadvantages. Then, the parasites are detected to observe the application characteristics of the RPA method. The results show that RPA is a kind of novel isothermal nucleic acid amplification technology, which is an open detection method. It has high sensitivity and specificity when being operated at 37-42°C, which makes it very suitable for early detection of pathogen infection. Besides, it also has high sensitivity and specificity in parasite detection. Therefore, the RPA technology has better performances and excellent applications in parasite detection, which has a certain significance for the future application of the technology in more fields.
Googlish as a resource for networked multilingualism

Abstract This paper discusses the ways in which incomprehensible English resulting from failed Google Translation, or ‘Googlish,’ has become an important networked resource for creators and viewers of parody videos on YouTube. This paper analyzes playful Googlish talk in a YouTube series called Google Translate Sings (GTS). In the series, the creator performs songs with lyrics that have been translated back and forth between English and several other languages using Google Translate, leading to accidental humor. Understanding YouTube as texts (Benson, 2015), this study demonstrates how what seems to be nonsensical English is given new textual identities by global web users.
Superpixel-Based Saliency Guided Intersecting Cortical Model for Unsupervised Object Segmentation

Unsupervised object segmentation aims to assign same label to pixels of object region with feature homogeneity, which can be applied to object detection and recognition. Intersecting cortical model (ICM) can simulate human visual system (HVS) to process image for many applications, and at the same time, saliency detection can also simulate HVS to locate the most important object in a scene. Based on saliency detection, a novel approach for unsupervised object segmentation, termed as saliency guided intersecting cortical model (SG-ICM), is proposed in this paper. Instead of using gray-scale and spatial information to motivate ICM neurons traditionally, it is better to exploit saliency characteristic to guide ICM. In this paper, we plan to do saliency detection exploiting an improved dynamic guided filtering to analyze significance of different regions in same scene. The proposed saliency feature lies on: (1) the proposed saliency detection is based on region instead of pixel; (2) the dynamic guided filter is designed to accelerate the filtering; (3) in order to improve SG-ICM for object segmentation, at the each iteration, we use adaptive and simple threshold, which can raise the speed of this model. We check the proposed algorithm on common database of DOTI, color image from public database of MSRA with ground truth annotation. Experimental results show that the proposed method is superior to the others in terms of robustness of object segmentation, furthermore, it does not need any training. In addition, this method is effective for aerial image, the detection results reveal that this model has great potential in aerial reconnaissance application.
Traffic optimization for UAV using a method of decomposition-Coordination

This paper presents an innovative solution for the traffic optimization problem, for an autonomous quadcopter Unmanned Aerial Vehicle (UAV). The overall purpose of this work is to allow the flying robot to navigate safely to the desired position, in a dynamic environment. We consider a supervisory unit receiving all the necessary information on the dynamic obstacles, and the moment they may appear on the road so that it set a first optimal /collision-free trajectory. The UAV navigate accordingly until a dynamic obstacle comes across, the supervisory unit provides then a safe trajectory that the quadcopter should reach to avoid the collision. To solve this nonlinear problem, we propose a Decomposition-Coordination method (DC) that enables the autonomous UAV to compute an optimal control to reach the next planned path in real-time. The principal of this method is based on a decomposition of the nonlinear system into a set of interconnected subsystems, to treat the non-linearity locally, we make use then of the Lagrange multipliers to achieve the coordination. This parallel processing minimizes enormously the computation time and offers a faster re-activity to the UAV. The convergence and stability of the method are dully proven, and the numerical application along with the simulation results are presented to affirm the potential of our approach.
Optimal Dubins Path Generation, System Identification and Control of a Paramotor

This paper presents a system identification, control and guidance framework for a small scale paramotor. The paramotor’s nonlinear state space model is initially introduced. Subsequently, a nonlinear model system identification procedure based on the Trust Region Reflective (TRR) least squares algorithm is used to obtain the model’s coefficients from actual recorded flight data. A Model Predictive Controller (MPC) is then designed for both the lateral and longitudinal control aspects of the paramotor based on the aforementioned model coefficients with simulations carried out to test the validity of the designed control scheme. A Dubins curve based guidance technique is then developed for generating a 2D in-flight trajectory for the paramotor’s autonomous flight navigation. The path planning algorithm is developed and simulated on the nonlinear model to verify its performance.
Fast global motion estimation on single instruction multiple data processors for real-time devices

Global motion estimation is a fundamental tool for image processing and computer vision. Since low-cost real-time devices have limited computational resources, the computational burden of global motion estimation remains considerable for low-cost devices. Hence, a fast global motion estimation algorithm for single instruction multiple data (SIMD) processors with which a CPU in modern mobile computing devices is commonly equipped is proposed. Conventional projection-based global motion estimation is modified to improve the prediction accuracy for videos containing large motions. To significantly increase the parallelism, the exact sizes of the variables in each processing step of the proposed algorithm are determined by considering the size of an SIMD register. Simulation results demonstrate that the proposed scheme is considerably fast and accurate, even for videos containing large motions.
MatLab Application Development of Accurate Detection and Instant Scoring System for Shooting Drills

Abstract The objective of this research was to make it easy to calculate the score on shooting drills, to help replace operators, to help evaluate the athlete’s performance in shooting practice, and to avoid cheating in shooting calculations. At this time in doing the calculations score on practice shooting still use score calculation manually. In addition to requiring time and effort is wasted so in the shoot practice and needed a long time when the calculation of shot scores. Image processing method used is the edge detection method, include image restoration, image enhancement, data compaction, image analysis, and image reconstruction. By using some method on image processing to detect and assess on the target into helping operators in the field. The result of this study is that the image segmentation using multiple functions in MatLab can avoid the shadow effects on image. On these systems, the image itself is limited in the form of a circular firing target thickness 0.1 mm circles line up to 0.22 mm and the diameter of 154 mm. His own bullet-sized diameter 4.5 mm. Percentage error rates on application own 4 % and the percentage accuracy detection 98.01% for 100 data. It can be concluded that this tool is capable of reading the image of a bullet trace in the form of a circle, and to reduce the impact of shadows on the image of the bullet.
Research on Applications of Distributed Ledger Technologies in the Balancing Market

This paper proposes potential applications for Distributed Ledger Technologies (DLT) in the balancing energy market. Starting with illustrating the state of the art of DLT, analyzing the advantages and disadvantages and carrying out a comparison of the different concepts Blockchain, Tangle and Hashgraph. In addition, the structure of the process behind the balancing energy market is investigated and presented. Furthermore, the identification of potential applications for DLT in the balancing market is carried out. The basis for this is the execution and analysis of expert interviews and an analysis of publicly available documents.
Rate-1 Trapdoor Functions from the Diffie-Hellman Problem

Trapdoor functions (TDFs) are one of the fundamental building blocks in cryptography. Studying the underlying assumptions and the efficiency of the resulting instantiations is therefore of both theoretical and practical interest. In this work we improve the input-to-image rate of TDFs based on the Diffie-Hellman problem. Specifically, we present:
Third Eye: Exploring the Affordances of Third-Person View in Telepresence Robots

Social interaction through telepresence robots can be challenging for a robot operator due to lack of spatial awareness caused by limited idiothetic cues and narrow field of view of a robot’s camera. We explore the use of a third-person perspective, popular in video game design, to provide missing spatial cues to remote robot operators. We present the design and implementation of Third Eye, a system that enables controlling telepresence robots through a third-person view. Third Eye comprises a controllable third-person camera with a wide field of view, attached to a robot, and bimanual controls for remote operation. Observations from a user study show that Third Eye enabled the robot operators to have a better awareness of the robot ‘bodies’ they controlled. This, in turn, afforded new behavior for operators. In addition, the camera design supported ecologically valid interaction for social telepresence. Quantitative data shows that Third Eye has comparable navigation efficiency to existing systems.
Korelasi Kebiasaan Membaca dan Kemampuan Literasi Sains

This study aims to determine the correlation between reading habits and the ability of science literacy in students of SMP Negeri 2 Bua Ponrang. The research method used is quantitative descriptive, to determine the correlation used product moment correlation. The results show that the r count value is smaller than r table, so it can be concluded that there is no significant relationship between reading habits and scientific literacy skills.
Scale Invariant Feature Transform Descriptor Robustness Analysis to Brightness Changes of Robowaiter Vision Sensor System

Abstract The purpose of this research is to identify problem detection features in computer vision that are affected by changes in brightness. The presented descriptor is Scale Invariant Feature Transform (SIFT). The method used in this study is an algorithm in computer vision to detect and describe local feature in image which robustly identify object and invariant to uniform scaling, orientation, brightness changes, and partially invariant to affine distortion. We implement this algorithm to Robowaiters object detection system that must detect and recognize objects around its task like food, beverage, refrigerator, and any kitchen objects. For this analysis case, we use beverage box image for sample image. The algorithm detects and recognize the image in normal brightness, and then the image brightness value increased and decreased. The result is that the algorithm successfully detects and recognizes the object presented and distinguishes it with a success rate of 93.5% increase in image brightness and 95.5% decrease in image brightness. it can be concluded that the SIFT algorithm is robust enough to change the lighting for our case.
Professional development of education managers in modern reality: psychological resources and risks

The article shows the importance of human capital development in the field of management for the effective solution of the problems of modernization of modern education. This article is aimed at identifying specific resources and risks that are reflected in the process of professional development of the Manager today. For the successful activity of the Manager in the field of education, it is important to update his motivation for growth and competence, complex reflection of clusters of individual risks and resources, focus on continuous self-development. The article studies and describes the content characteristics of managers ’ ideas about psychological factors that can facilitate or block professional and personal growth. The attention is focused on the role of the education Manager in ensuring effective activity, the level of subjective control, which reflects the degree of internality, autonomy and responsibility for management decisions and the nature of professional activity. It is emphasized that the role of subjective control of the Manager increases in the context of risks of modernization of the modern educational system. Levels of acceptance of information on own resources and risks as bases of modeling of further development are revealed.
Relation Extraction with Synthetic Explanations and Neural Network

The state-of-the-art for Relation Extraction, defined as the detection of existing relations between a pair of entities in a sentence, relies on neural networks that require a large number of training examples to perform well. To address that cost, Distant Supervision has become the preferred choice for collecting labeled sentences. However, Distant Supervision has many limitations and often introduces noise into the training set. Recent work has shown an alternative way of training neural methods for relation extraction, namely to provide a small number of annotated sentences and explanations for why those sentences express the relation. Training classifiers with this approach results in accuracy comparable to Distant Supervision, but requires humans to annotate the sentences and provide the explanations. In this paper, we show a way to generate synthetic explanations from a small number of relational trigger words, for each relation, whose resulting explanations achieve comparable accuracy to human produced ones. We validate the method on five relation extraction tasks with different entity types (person-person, person-location, etc.). Furthermore, experiments on two public datasets demonstrate the effectiveness of our generated synthetic explanations, with 6% improvement in accuracy on relation extraction and 19% improvement in F1-score on generating labeled training sentences compared to the next best methods.
Modeling of Intelligent Systems Architecture Based on the Brain Topology

The article presents the results of the unification of the architecture of computer intelligent systems based on the topology of the functional systems of the human brain intelligence. A pragmatic conceptual framework is proposed that eliminates the polysemy characteristic of this area, a set of models and algorithms is developed that unifies and standardizes the decision-making process. As part of the multi-agent approach, a special unified system architecture and the corresponding program library were developed and implemented. The effectiveness of the approach is demonstrated by an example of solving an applied problem.
Towards a Discipline of Multimodality: Parallels to Mathematics and Linguistics and New Ways Forward

Multimodality, involving the study of meaning arising from the integration of language with images and other resources in multimodal texts, interactions and events, addresses the fundamental need to understand human communication in the current age of digital technology. However, multimodality is not considered to be a discipline per se at present. By drawing parallels between mathematics and linguistics, it is proposed that if multimodality is to become a discipline, then abstract context-based frameworks for modeling multimodal semiotic resources and methodologies for investigating patterns of human communication are required. An example of how this could be achieved is provided. From here, multimodality has the potential to provide the foundations for a range of multimodal sciences, in much the same way that mathematics and linguistics underpin the mathematical and language sciences respectively. In doing so, it may become possible to track the changes in human communication arising from digital technology and the resultant impact on thought and reality.
Bitcoin, Distributed Ledgers and the Theory of the Firm

The Bitcoin system implements a coordination mechanism enabling peer-to-peer transactions in adversarial environments, without using a central third party. Some scholars make exuberant claims about the significance of this mechanism, called Distributed Ledger Technology. We use Incomplete Contracting models of the Theory of the Firm to dismiss those claims and analyze the benefits and limitations of Distributed Ledgers. Introducing a two-layer model, we show that they create markets whose design layer employs traditional forms of governance and whose execution layer represents an original contract enforcement mechanism, governed as a hierarchy. Permissioned and private Distributed Ledgers only provide cryptographic assurances in addition to the functions offered by traditional distributed systems. They may be implemented by electronic organizations when trusted central operators cannot be formed. Permissionless Distributed Ledgers allow users to resist censorship and expropriation, by making some tradeoffs: lower throughput, higher coordination costs, and a transfer of risks to the users.
Discrimination Model of QAR High-Severity Events Using Machine Learning

The Quick Access Recorder (QAR) is an airborne equipment designed to store raw flight data, which contains a mass amount of safety related parameters such as flap angle, airspeed, altitude, etc. The assessment of QAR data is of great significance for the safety of civil aviation and the improvement of pilots skills. The existing QAR assessment approaches mainly utilizes the exceedance detection (ED) that relies on the pre-defined parameter threshold, which could miss potential flight risks. In this paper, we perform anomaly detection on the takeoff and landing phases based on an improved random forest (RF) method. The evaluation is performed on the dataset generated by a fleet of B-737NG, which shows that the method is able to discriminate the high-severity events accurately on the high dimensional multivariate time series, which also shows that the model can identify the events with potential risk pattern on the imbalanced dataset even if the event has not been pre-defined before.
For the Jubilee of Vladimir Mikhailovich Chernov

Abstract On April 25, 2019, Vladimir Chernov celebrated his 70th birthday, Doctor of Physics and Mathematics, Chief Researcher at the Laboratory of Mathematical Methods of Image Processing of the Image Processing Systems Institute of the Russian Academy of Sciences (IPSI RAS), a branch of the Federal Science Research Center “Crystallography and Photonics” RAS and part-time Professor at the Department of Geoinformatics and Information Security of the Samara National Research University named after academician S.P. Korolev (Samara University). The article briefly describes the scientific and pedagogical achievements of the hero of the day.
A Web Service Composition Method Based on OpenAPI Semantic Annotations

Automatic Web service composition is a research direction aimed to improve the process of aggregating multiple Web services to create some new, specific functionality. The use of semantics is required as the proper semantic model with annotation standards is enabling the automation of reasoning required to solve non-trivial cases. Most previous models are limited in describing service parameters as concepts of a simple hierarchy. Our proposed method is increasing the expressiveness at the parameter level, using concept properties that define attributes expressed by name and type. Concept properties are inherited. The paper also describes how parameters are matched to create, in an automatic manner, valid compositions. Additionally, the composition algorithm is practically used on descriptions of Web services implemented by REST APIs expressed by OpenAPI specifications. Our proposal uses knowledge models (ontologies) to enhance these OpenAPI constructs with JSON-LD semantic annotations in order to obtain better compositions for involved services. We also propose an adjusted composition algorithm that extends the semantic knowledge defined by our model.
Diverse Layout Generation for Graphical Design Magazines

We propose a system that automatically generates layouts for magazines that require graphical design. In this system, when images or texts are input as the content to be placed in layouts, an appropriate layout is automatically generated in consideration of content and design. The layout generation process is performed by randomized processing in accordance with a rule set of minimum conditions that must be satisfied for layouts (minimum condition rule set), where a large number of candidates are generated. An evaluation of appearance, style, design, and composition of the candidates are combined with an evaluation of their diverseness. Top candidates of the combined evaluation are returned. The automation makes the layout creation task performed by users such as graphical designers to be much more efficient. It also allows the user to choose from a wide range of ideas to create attractive layouts.
Content Curation, Evaluation, and Refinement on a Nonlinearly Directed Imageboard: Lessons From Danbooru

While linearly directed imageboards like 4chan have been extensively studied, user participation on nonlinearly directed imageboards, or “boorus,” has been overlooked despite high activity, expansive multimedia repositories with user-defined classifications and evaluations, and unique affordances prioritizing mutual content curation, evaluation, and refinement over overt discourse. To address the gap in the literature related to participatory engagement on nonlinearly directed imageboards, user activity around the full database of [Formula: see text] submissions to Danbooru, a prominent nonlinearly directed imageboard, was evaluated using regression. The results illustrate the role played by the affordances of nonlinearly directed imageboards and the visible attributes of individual submissions in shaping the user processes of content curation, evaluation, and refinement, as well as the interrelationships between these three core activities. These results provide a foundation for further research within the unique environments of nonlinearly directed imageboards and suggest practical applications across online domains.