Reviewerly/exHarmony huggingface dataset:
k-means and fuzzy c-means fusion for object clustering

Classification methods are carried out in several steps. The most important step is the development of classification rules based on a priori available knowledge; this is the learning phase. This phase uses either deductive or inductive learning. Inductive learning algorithms derive a set of classification rules (or standards) from a set of already classified examples. The goal of these algorithms is to produce classification rules to predict the assignment class of a new case. Among the available knowledge we can mention the choice of the initial cluster centers which is a very important factor in the final definition of clusters. For this purpose, we proposed an evolutionary algorithm EK-means based on the combination of k-means and fuzzy c-means by touching the initialization phase of the centroids. the performance of EK-means is compared with k-means according to the metrics of interclass and intraclass distances. To compare the efficiency of our optimization solution with traditional K-means, we rely on a UCI machine learning repository. The comparative study indicates a remarkable efficiency of our proposal, regardless of the type of data.
Correctness Verification of Aerospace Software Program Based on Hoare Logic

Spacecraft software requires more and more credibility. The correctness of program implementation is the basic requirement of software credibility. However, traditional testing methods cannot guarantee the correctness of the program implementation. Therefore, in order to guarantee the consistency between the designing and realization of the software, the method of verifying the spacecraft software by formal method was studied. This paper studied the feasibility of verifying the correctness of the program in theory by formal verification. Firstly, this paper introduced in detail the method of formal verification, and the principles and reasoning rules of theorem proofing based on Hoare logic for part of C language syntax. Then, according to the Hoare logic tripe and its reasoning rules, a module of an aerospace software was verified by artificial reasoning. Finally, it was concluded that the program verification method based on Hoare logic was feasible in the correctness verification of spacecraft software program.
Internal Search Engine and Recommender System with Natural Language Processing in PaaS

Nowadays, Natural Language Processing con-tributes to the advancement of different areas and applications such as voice recognition, web search engines, social mining, etc. This scientific article applies natural language processing techniques to develop an internal search engine and recommender system in e-commerce. The system is deployed in a PaaS with a NoSQL database and a dataset with 100 documents on literary works. Fifteen fields structure each document, and for the system implementation, only six document fields were considered. The search engine compares a query with the corpus of documents using the Jaccard coefficient, the Sorensen-Dice coefficient, and the Salton cosine. Moreover, the recommender system applies similarity metrics to find books similar to the last ones viewed by the user. The tools were validated through functional and non-functional tests. The functional tests used human validation through a satisfaction survey in 25 people between 23 and 25 years old with a university education. The results showed that at least 65 % of the users rated the tools with the highest satisfaction score. Stress and load tests were carried out for the non-functional tests, obtaining a high latency in some cases because free services were used.
日本語物語文を対象とする空所穴埋め問題データセット

This paper describes our dataset of Japanese cloze questions designed for the evaluation of machine reading comprehension. The dataset consists of questions automatically generated from Aozora Bunko, and each question is defined as a 4-tuple: a context passage, a query holding a slot, an answer character, and a set of possible answer characters. The query is generated from the original sentence, which appears immediately after the context passage on the target book, by replacing the answer character with the slot. The set of possible answer characters consists of the answer character and the other characters who appear in the context passage. Because the context passage and the query share the same context, a machine that precisely understands the context may select the correct answer from the set of possible answer characters. The unique point of our approach is that we focus on characters of target books as slots to generate queries from original sentences because they play important roles in narrative texts and a precise understanding of their relationship is necessary for reading comprehension. To extract characters from target books, manually created dictionaries of characters are employed because some characters appear as common nouns, not as named entities.
Surface Vessel: Local Positioning System Based on Computer Vision

Object detection is a technology capable of finding instances of objects in images. Among the main applications in many different areas the following can be highlighted: traffic monitoring, robots and security. A typical object detection algorithm takes an image as an input, finds the object of interest in it, and provides a bounding box. The output bounding box is usually axis-aligned, and it is enough for many applications. However, there are situations when the angle of rotation of the bounding box is essential and should be calculated. In this paper, different approaches for local positioning of a single object with obtainment of center coordinates and direction angle are studied and tested. Firstly, a basic image processing approach that uses binarization by the color range of the object of interest is considered. Furthermore, this simple algorithm finds the final bounding box with an angle of rotation using geometrical properties of the surface vessel. This approach obtains angle by getting the minimum bounding box using the contour of the surface vessel and principal component analy-sis. In addition, deep learning segmentation approaches were considered. Considering all algorithms, the image processing approach based on threshold binarization performs faster than deep learning approaches, suiting into the problem of local positioning of a surface vessel in a suitable way. Moreover, it provides comparable accuracy and execution time with the deep learning segmentation approaches. The final algorithm obtained can detect the surface vessel and obtain its linear (x, y) and angular (a) coordinates. It is robust to the appearance of other objects with the same colors as the object of interest and of smaller size. It is also robust to the appearance of objects of any size not in the color range of the object of interest.
Descriptive and Predictive Analytics on Electronic Health Records using Machine Learning

Electronic Health Records are an electronic version of a patient’s health records. Real-time data of a patient’s health history, medications, treatments, diagnosis, immunizations, procedures, laboratory tests and allergies. It is patient-centered data that is made available to authorized users, especially the doctors, and medical professionals, who prescribe different medications based on the ailments. This information is shared between different health care providers to allow access to patients’ medical records to make decisions about patients’ care plans and treatment. Electronic Health Records supports bbuilding an intelligent system that can easily detect the dissimilarities in patient’s medication and can target the provider for relevant educational content. Also helps the health care organizations to get refreshed and updated at minimum risk of the wrong diagnosis during the course of treatment with superior quality of health care services. Real-time data of a patient’s health history and medication processes is used to develop a predictive model. This model provides educational content to healthcare providers, to minimize the risk during the course of treatment, compares the actual practice to clinical guideline, and also increase the quality of health care services.
On Using Artificial Intelligence in the Search of the Best Professional Resumes

Digital transformation has changed how companies develop, manufacture and deliver their products and services. The search for competitiveness and greater market share have been driven companies to use automation and digital technologies to become more attractive and continue to deliver value to their customers. However, the human factor remains decisive for the company´s success. In this context, the Human Resources (HR) team has the role of strategically improving the company composition. In this paper, we present the development of an application, based on Artificial Intelligence and Digital Data Processing tools, with the aim of helping HR teams to seek talents and, thereby, to contribute to the business success. This application was tested and validated by using real-world databases of professional resumes.
Summary of Works on Image Classification with Noisy Labels

Deep learning has achieved great success in recent years in various areas of machine learning, but it requires an enormous amount of data for training. Data collection techniques such as web crawling have been developed to solve this problem, but these techniques run the risk of generating incorrect labels. When a deep learning model is trained for image classification problem with a dataset that contains noisy labels, its generalization performance is severely degraded. To solve this problem, we have proposed two approaches: one is to correct the incorrect labels using graph label propagation, and the other is to apply consistency regularization to the samples selected as clean. In this paper, we describe the details of the two approaches and show that our methods achieve higher generalization performance on CIFAR-10 and CIFAR-100 dataset with noisy labels than baseline approaches.
Real-Time Pedestrian Detection Using Enhanced Representations from Light-Weight YOLO Network

Pedestrian detection is one of the significant tasks in Autonomous Vehicles (AVs). There are two kinds of networks which are widely used for pedestrian detection: single-stage networks and region-based networks. Single-stage networks, such as YOLO, solve the bounding box regression and classification problems simultaneously which makes them faster than region-based networks such as Faster R-CNN. Nonetheless, the main structure of YOLO is too complex and slow for the pedestrian detection task in AVs and cannot detect small pedestrians. Furthermore, unlike region-based networks where all features of the region containing a pedestrian is used in classification, in YOLO only the features of a cell in which the center of anchor box lies is used in classification. In this paper, these issues related to YOLO will be addressed such that it can be better used for pedestrian detection.
An Intelligent Recognition of English Translation Based on Improved GLR Algorithm

English is a global language. In the process of China’s internationalization, overcoming the difficulty of understanding English is the only way to achieve cultural exchanges, economic exchanges, and even scientific and technological exchanges. Especially in the context of globalization, translation between languages has become the focus of transnational communication. However, there are still problems such as low accuracy and singularity in current machine translation. Aiming at the above problems, based on the improved GLR algorithm (IGLR), this paper proposes a recognition method to solve the English translation problem. First, a corpus is built, and the number of label words reaches tens of thousands. In this way, the automatic search function of the phrase is realized. In addition, create an intelligent method for translation; and plan the intelligent recognition model with data collection, processing, and output; extract characteristic parameters to realize intelligent translation. Conduct an experimental analysis on the designed English translation method and record the experimental data. Through the experiment, it can be seen that the designed translation method can achieve accurate translation results and meet the actual needs.
A Method of Using Control Chart to Monitor Software Key Performance Evolution

In order to objectively analyze how the key performance of software evolves during the maintenance period, this paper studies the statistical process control and process capability. Based on the performance data of software use process, a method of monitoring the evolution of software key performance using control chart is proposed. According to the monitoring requirements of software, collect the target performance samples, draw the control chart, judge whether the measured value meets the statistical steady state; according to the abnormal mode and out of control point, analyze the software abnormal and make the maintenance plan; under the condition of the normal distribution of the measured value, synthesize the process capability index and process performance index to judge whether the measured value meets the technical steady state; calculate the unqualified product rate to determine whether the failure rate of software products meets the user's requirements; define the reproducibility of the sample set, and examines the reproducibility from two aspects: the quantity value of reproducibility and the statistical steady state of the mean value of the sample set. The method has been fully verified in a task management system.
Some Ways of Integrated Teaching of Mathematics and Natural Sciences

The aim of this study is to identify key concepts for finding ways to integrate teaching mathematics and science, in the context of the prospects for teaching natural sciences in modern educational institutions. The basis of the methodological approach in this scientific study is a combination of methods of system analysis of the methods of teaching mathematics currently used in the practice of educational institutions with an analytical study of the real prospects for building ways of integrated teaching of mathematics and other sciences of the modern natural science system. In the course of this scientific research, results were obtained that testify to the need to develop the principles of integrating mathematics in teaching it with other scientific disciplines, as they have proven their high efficiency in building the educational process. The results of this scientific research formulated on their basis, are of significant practical importance for teachers and students of modern educational institutions involved in the process of studying mathematics in the context of its relationship with other disciplines.
A Relation Prediction Method for Industrial Knowledge Graph with Complex Relations

In the industrial knowledge graph, relations between entities has important physical significance. In some cases, the relations determines product quality or fault prediction accuracy. Therefore, relation prediction is particularly important. The existing translation models are poor in relation prediction, because the current method of generating incorrect triples cannot generate incorrect triples with incorrect relations. Therefore, this paper develops an incorrect triple generation method by combining uniform distribution and Bernoulli distribution to improve the relation prediction accuracy. Then UBTransH model is developed by combining TransH with the incorrect triples based on uniform distribution and Bernoulli distribution. In the UBTransH, the relation replacement probability and entity replacement probability of the correct triples are first obtained through uniform distribution, then the head entity replacement probability and tail entity replacement probability are calculated with the entity replacement probability and Bernoulli distribution. Second, the incorrect triples are generated by replacing the relation/head entity/tail entity with the relation/head entity/tail entity replacement probability. Third, the incorrect triples are applied to train TransH. Finally, based on relation prediction, the developed UBTransH are compared with TransH on several typical datasets. Experimental results show that the developed UBTransH significantly improves the relation prediction accuracy.
Teacher’s Perspective in Teaching Reading Comprehension Using Reciprocal Strategy

The Indonesian government has determined that English is a compulsory subject in junior high schools, senior high schools, and universities. English in junior high school includes four mandatory skills: listening, reading, speaking, and writing. In the teaching process, teachers are expected to be perfect. The teacher must be "perfect"; it means they have to be active, creative, innovative, interesting, and achievers so that students can focus on the lessons in class. However, most students have difficulty learning English, especially reading or understanding texts. This research was conducted with the aim of describing the Teacher's Perspective in Teaching Reading Comprehension Using a Reciprocal Strategy. This research was conducted at SMPN 1 Jabon using qualitative methods, data obtained from interviews and observations. The subject in this study is an English teacher at SMPN 1 Jabon. The results of this study indicate that mastery of good learning strategies is an important component of education in supporting the professional duties of teachers as educators. Teachers use reciprocal teaching as a strategy in their learning. Reciprocal strategies can improve students' reading comprehension because students know the essence of what they read globally. In addition, the teacher can also assist students in understanding the meaning contained in the reading text material that has been explained during the reading class.
Current trends and development of the virtual reality market

The article discusses a brief history of the emergence of virtual reality and the scope of its application, as well as the trends in its development. The volumes of investments in this area are presented. The possibilities of using virtual and augmented reality technologies in the process of teaching at higher educational institutions are analyzed; examples of using these technologies in the educational sphere, as well as their advantages and disadvantages are considered.
Study and Analysis of Liu’s Algorithm for Image Segmentation

In this paper the Liu’s algorithm is studied and analyzed. Adaptive control of the range and strength of the iterative pixels is performed here. A novel dissimilarity function is formed to extract the pixels with similar local indices. In the Liu’s method, the over-segmentation which is the first step is performed with mean shift algorithm to keep more number of discontinuities of the image. From this, the method follows HMRF-FCM by modifying its prior probability function and dissimilarity function novelly to include spatial information of the image. A novel prior probability function is formed to distinguish whether a pixel belongs to a homogeneous region or heterogeneous region. The method seems to prevent over smoothing and the results produced on synthetic images, natural images, and radar images imply good segmentation accuracy of the method compared with other state-of-the-art models.
Research on the System Construction of Spaceborne Adaptive Software and Its Key Technologies

With the deployment of large-scale satellite constellation, the complexity and scale of spaceborne control software have shown remarkable increases. The operating environment has also shown dynamism and variability. Constantly changing software requirements require higher levels of flexibility and scalability of the software system in an open environment. However, it is difficult to dynamically change the variability model for most spaceborne control software, leading to difficulties for the system to adapt to required environments for desired changes during an operation. The objective of this study was to develop a construction technology for the adaptive system of the spaceborne control software. Firstly, this study designed an overall framework of the target software system with its key functions. We established a unified description model of variability such as working mode and uplink injection to support software dynamic adjustment. We then unified the API using the design message middleware for hardware assessments. Furthermore, adaptive software parameter adjustment mechanism was discussed in this study. We extracted behavior parameters to form independent external configuration files and implant the connector with data flow constraints in functional units. Finally, an adaptive strategy was designed to exhibit an adaptive adjustment effect on the behavior and structure made by the adaptive system under the change of demand for certain satellite control software.
State estimation in range coordinate using range-only measurements

In some tracking applications, due to the sensor characteristic, only range measurements are available. If this is the case, due to the lack of full position measurements, the observability of Cartesian states (e.g., position and velocity) are limited to particular cases. For general cases, the range measurements can be utilized by developing a state estimation algorithm in range-Doppler (R-D) plane to obtain accurate range and Doppler estimates. In this paper, a state estimation method based on the proper dynamic model in the R-D plane is proposed. The unscented Kalman filter is employed to handle the strong nonlinearity in the dynamic model. Two filtering initialization methods are derived to extract the initial state estimate and the initial covariance in the R-D plane from the first several range measurements. One is derived based on the well-known two-point differencing method. The other incorporates the correct dynamic model information and uses the unscented transformation method to obtain the initial state estimates and covariance, resulting in a model-based method, which capitalizes the model information to yield better performance. Monte Carlo simulation results are provided to illustrate the effectiveness and superior performance of the proposed state estimation and filter initialization methods.
Fine Tuning (Diagnosis) of Machine Learning Algorithm (Model) for optimization

When any machine learning algorithm is deployed/tested on new set of unseen data, and makes unacceptable large error in its prediction then it became mandatory to diagnose present hypothesis and evaluate it by implementing the one, some or all of the following a) get more training data. b) implementing smaller set a features. c) implementing additional features. d) adding polynomial features. e) decreasing or increasing regularization parameter λ. Machine learning diagnosis is a way to find out what is working and what not for an algorithm. The developer must determine what may be changed to increase the performance of an algorithm on unseen set of data generated in future. Diagnosis of any machine can take time to understand and implement but, they can also enable machine for the better decision making.