Reviewerly/exHarmony huggingface dataset:
Image Saliency Detection Based on Background Prior and Multi-feature Fusion

Image saliency region calculation is a very challenging computer vision problem. It is difficult to highlight the salient object uniformly with a single feature, integrating more effective prior knowledge is the feasible direction to improve the saliency region detection. In order to solve the problem of poor detection effect of image with complex background, this study proposes an image saliency detection algorithm based on background prior and multi feature fusion. Firstly, the color, texture, edge and other feature information of the original image are extracted, and the original image is divided into different size and non-overlapping image patch structure by synthesizing the color and spatial distribution of pixels. Then, taking the space distance between image patches and the space distance between image patches and the center of original image as the weight, the color, gray and background prior contrast of image patches are calculated, and the image saliency region is calculated by contrast fusion. Finally, different segmentation scales are used to construct the image patch structure, and the saliency is calculated respectively, and multi-scale fusion is carried out to enhance the saliency of the image. In order to evaluate the algorithm, it is tested on MSRA-1000, which is an international public data set. The experimental results show that the algorithm can better highlight the salient object, suppress the background noise, and get the saliency image which is more consistent with the visual perception.
An innovative user interface under awfully rigorous conditions

Our research team developed a test question browser app for test-takers with print disabilities. This technology had to fully overcome three challenges: (1) the characteristic rigorousness and fairness demanded of East Asian university entrance exams, (2) problems inherent in assistive technologies for persons with print disabilities, and (3) the unique demands of the Japanese language in accurately displaying text and reading it aloud.
Cloud Computing Virtual Machine Migration Energy Measuring Research

This paper research virtual machine migration energy measuring on IPv4/IPv6 network based on cloud computing infrastructure platform, it conducts a research on the energy measuring in IPv4/IPv6 cloud computing platform, and presents a dynamic energy measuring mathematical model based on analyzing CPU energy consumption changes brought by random assignment works. The research determines mathematical model parameter values and it completes the IPv4/IPv6 cloud computing platform virtual machine migration experimentally. This paper achieves the energy consumption of IPv4/IPv6 transition prior-period, mid-period and last-period cloud computing platform virtual machine migration, the conclusions in line with the cloud energy measurement needs, it builds the theoretical foundation for cloud computing platform energy consumption optimize.
Development of information and analytical tools based on adaptive classifier Cascade ARTMAP

Abstract The article deals with methods of teaching incremental classifiers capable to adapt to incoming online streaming data, using the example of environmental monitoring systems. It is proposed to use Cascade ARTMAP neural network architecture, compatible with symbolic representation based on IF- THEN rules. The knowledge gained during Cascade ARTMAP network training can be transformed into a compact set of decisive rules for classification of source data that can be easily interpreted by experts in the field of environmental assessment.
The Detecting of Damage Conductor with Guided Image filter

Abstract The Damage of electrical conductor of high voltage transmission system is very common. This problem effected the distribution of electrical power from the sources into the costumer place. The main cause of this problem if come from the damage of electrical conductor. There were many ways to detect damage conductor so far. The most common is through compute the impedance of transmission line. This method is accurate, but it needs to switch of the line for a while. This disruption contribute losses for the costumers and energy provider. In order to handle this problem, our proposed method is to detect the damage conductor with Guided Image Filter. The input image is a visible image and the reference image is invisible image. The processing is very simple. The result obtained has high accuracy. The accuracy is shown on the detecting map.
Results from a Replicated Experiment on the Affective Reactions of Novice Developers When Applying Test-Driven Development

Test-Driven Development (TDD) is an incremental approach to software development. Despite it is claimed to improve both quality of software and developers’ productivity, the research on the claimed effects of TDD has so far shown inconclusive results. Some researchers have ascribed these inconclusive results to the negative affective states that TDD would provoke. A previous (baseline) experiment has, therefore, studied the affective reactions of (novice) developers—i.e., 29 third-year undergraduates in Computer Science (CS)—when practicing TDD to implement software. To validate the results of the baseline experiment, we conducted a replicated experiment that studies the affective reactions of novice developers when applying TDD to develop software. Developers in the treatment group carried out a development task using TDD, while those in the control group used a non-TDD approach. To measure the affective reactions of developers, we used the Self-Assessment Manikin instrument complemented with a liking dimension. The most important differences between the baseline and replicated experiments are: (i) the kind of novice developers involved in the experiments—third-year vs. second-year undergraduates in CS from two different universities; and (ii) their number—29 vs. 59. The results of the replicated experiment do not show any difference in the affective reactions of novice developers. Instead, the results of the baseline experiment suggest that developers seem to like TDD less as compared to a non-TDD approach and that developers following TDD seem to like implementing code less than the other developers, while testing code seems to make them less happy.
Development of nomograms to assess the risk of clinical outcome

A methodology is presented for developing nomograms for assessing and stratifying the risk of a clinical outcome based on the created virtual data set using the R software environment. The virtual data set included input numerical and factor variables (variable types correspond to the R software documentation) and outcome. For quantitative variables, descriptive statistics were calculated at all levels of the outcome variable, and mosaic diagrams were constructed for factor variables. As a model that describes the association of input variables with the outcome, a logistic regression model was used. A bootstrap method was applied to validate and evaluate the model performance. The calculated validity indicators showed an acceptable discriminatory ability of the predictive model. The statistical calibration demonstrated the proximity of the model’s calibration curve to the ideal calibration curve. Based on the logistic regression coefficients, a nomogram was constructed using which the risk value of a specific outcome was calculated for each subject (patient). It is shown that with the help of the presented technique it is possible to stratify patients effectively by the risk of an adverse outcome, thus adequately altering the diagnosis and treatment tactics. The use of a nomogram greatly simplifies risk assessment and can be used in paper form as a supplement to the patient examination protocol. The article contains the codes of the R programming language with explanations.
The Role of Applications in the Support of Electronic Commerce �Application of Instagram as a Model� A sample of Members of Al-Kamil Community- Saudi Arabia

Background/Objectives: To identify the determinants of the practice of e-commerce in the full province. Methods/Statistical analysis:This study used 16 of previous researches as reference.The study was conducted on a sample of members of the full conservative society.The researcher used the descriptive analytical method to suit this study.• The existence of a relationship between the applications of e-commerce and the customs and traditions of the Saudi society in the province of Al-Kamil.• A statistically significant relationship between e-commerce applications and the comparative advantages and competitiveness of the organization.• There is an inverse relationship between the application of the information and the sale and purchase of products and services.The article recommended the following important issues:• Adopting the technology education policy for the members of the society in Al-Kamil governorate to demonstrate the importance of electronic commerce through training and education programs in professional and administrative institutions, with the aim of spreading technical awareness in educational and academic institutions in accordance with customs and traditions.• Resolving the obstacles facing the spread of e-commerce in non-civil societies by providing fast Internet, and raising awareness of the importance and benefits of e-commerce and keeping abreast of the global developments in the process of marketing products through the Internet.Findings: Instagram application is one of the best and most popular free applications used by users to share and share photos on the Internet, giving users the ability to take photos and video clips, and then share them through the various social networks and the network it.Last month, Windows Phone lacked many features and options, such as shooting videos, alerting users to people in the photos, and not allowing users to take pictures through the application.The researcher finds a set of recommendations calling for taking into account as a future work to be done by others:-• Adopting the technology education policy for the members of the society in Al-Kamil governorate to demonstrate the importance of electronic commerce through training and education programs in professional and administrative institutions, with the interest of spreading technical awareness in educational and academic institutions in accordance with customs and traditions.Improvements/Applications: Saudi Arabia is one of the societies of the modern world seeking technological revolution.For decades, Saudi Arabia has witnessed a great turnaround in the transformation of a technical society based on the advantages of communication technology in particular and modern technology in general in all fields.The concept of e-commerce since it has become a real reality, we must deal with it, but there is a significant reluctance in the practice of
Mining Frequent Itemsets over Vertical Probabilistic Dataset Using FuzzyUeclat Algorithm

Data mining is the technique of extracting relevant information from large heterogeneous data sources and present in a meaningful and systematic way for users. Nowadays, data mining has become a trend and all computer-related industries are engaged with data mining. Currently, many data mining algorithms focused on computing the frequent pattern from certain and uncertain databases. In this paper, a new algorithm named FuzzyUeclat is being proposed which uses Fuzzy operators to extract frequent items from the vertical probabilistic dataset. Previously, the frequent items were computed on the basis of expected minimum support derived from the existential probability associated with items. Therefore, as the size of the database increases, the value of expected support is decreased at each level of iterations and subsequently, it approaches zero. To avoid the loss of frequent itemset, the expected minimum support is being replaced with Fuzzy min and Fuzzy max operator to compute the support for an itemset. The experimental result shows that the algorithm FuzzyUeclat increases the frequent patterns and improves the performance for the large uncertain databases.
Voted-Perceptron Approach for Kazakh Morphological Disambiguation

This paper presents an approach of voted perceptron for morphological disambiguation for the case of Kazakh language. Guided by the intuition that the feature value from the correct path of analyses must be higher than the feature value of non-correct path of analyses, we propose the voted perceptron algorithm with Viterbi decoding manner for disambiguation. The approach can use arbitrary features to learn the feature vector for a sequence of analyses, which plays a vital role for disambiguation. Experimental results show that our approach outperforms other statistical and rule-based models. Moreover, we manually annotated a new morphological disambiguation corpus for Kazakh language.
Detecting Pattern Efficiently with Don’t Cares

In this paper, we introduce our efficient simple method which can locate all occurrences of pattern P of k subpatterns with “don’t cares” of length m in text S of length n. Our algorithm employs advanced data structure and the Kangaroo method, which can be applied to selected suffixes of the suffix tree of S to answer subsequent queries in O(k) time using a predefined computational method to find all occurrences of pattern P with “don’t cares” in text S in a fast and effective manner.
Research on Software Risk Assessment Model based on AHP- Fuzzy Comprehensive Evaluation

With the development of global science and technology, computer network technology has been popularized and widely applied in every corner of people's life. At the same time, the field of software development has also been driven and developed rapidly. With the increasing complexity and difficulty of software project development, as well as the increasing needs of customers, software project management has gradually become a hot topic for scholars. In order to effectively prevent the risk of failure of software project development, the awareness and ability of software project risk management must be strengthened, and software project risk assessment is an important part of software risk management. At present, the research on software risk assessment in China is still lack of experience. Considering the fuzziness and uncertainty of the software project itself, this paper combines the Analytic Hierarchy Process (AHP) with the Fuzzy Comprehensive Evaluation (FCE) to determine the evaluation indexes, establishes the software risk evaluation system and evaluates the risk of the software project, which provides a new way to reduce the risk of the software project, enhance the ability of software to resist the risk, protect the basic interests of customers and improve the quality of software development, and has certain theoretical and practical value.
Rule-based joint fuzzy and probabilistic networks

One of the important challenges in Graphical models is the problem of dealing with the uncertainties in the problem. Among graphical networks, fuzzy cognitive map is only capable of modeling fuzzy uncertainty and the Bayesian network is only capable of modeling probabilistic uncertainty. In many real issues, we are faced with both fuzzy and probabilistic uncertainties. In these cases, the proposed method of this paper can take into account both types of uncertainty with a new and different approach. In this method, we avoid fuzzy transformations to probabilities and vice versa, and fuzzy uncertainties and probabilities are considered jointly. For this purpose, in the original graphical model, first, the type of uncertainty of each node is identified, and accordingly two separate fuzzy and probabilistic networks are constructed. In these networks, relations between nodes are expressed in terms of a set of rules. In each network, fuzzy and probabilistic inference is individually constructed and ultimately the values obtained from each network are combined. This method has been tested on a real problem of localization in wireless sensor networks. In this case, a sensor with uncertain location should be able to predict its location from the received power of its adjacent sensors. In the given scenario, 60 sensors with uncertain locations and 121 sensors with a specific location are considered. Meanwhile, the average location error of sensors has been used to evaluate the methods. The simulation results show the efficiency of the proposed method well.
ARAÇ DEĞERLENDİRME VERİSİ ÜZERİNDE VERİ MADENCİLİĞİ ALGORİTMALARININ HİPERPARAME

Data mining is the process of obtaining valuable data from large-scale data. Several algorithms are used for revealing the relationships between data and making accurate predictions. There are several cases that may affect the performance of these algorithms. One of these is choosing most suitable hyper parameters. To optimize these parameters provide us to improve algorithm results. In our project, we optimized hyper parameters of different data mining algorithms on car evaluation dataset for improving classification accuracy. Hyper parameter optimization was performed on support vector machines, k-nearest neighbor, random forest, multi-layer perceptron and gradient boosting algorithms. Results of hyper parameter values and default parameter values were compared. The calculations show that gradient boosting with hyper parameter optimization method produces best prediction of the car evaluation by 99.42%.
Evaluation of an Efficient Method for Object Detection in Video

In this real world, many public or open areas are facilitated with cameras at multiple angles to monitor the activities of human for safety of people or infrastructure. The object detection is a fundamental concept of computer vision that focused on the detection of instances of objects of a certain class (such as person, animal, ghost, buildings, or vehicles) in videos. This manuscript presents a method for object detection using background subtraction and morphology. The core of the proposed work is the simple background subtraction method. In the first step we developed a background model based on some video frames that only consists of static background without any moving object. A suitable scheme is applied for updating the background model so that the challenges like camera shake, dust and fog particles in air should be resolved. The scheme uses the “learning rate” for the entire frame. In the second step we extract the foreground pixels which are in motion. After the initial foreground extraction, the morphological operators are applied for noise cleaning.
Word Order Communicates User Intent in Search Queries

One of the most intriguing features of the human-computer interaction is that human users rarely use full-fledged natural language utterances when communicating with computers. I investigate properties of linguistic expressions that arise in the context of human-computer interaction, focusing specifically on word order in search queries. I report results of two experimental studies that suggest that word order is meaningful and communicates user intent: the attribute that is more important or desirable to the user tends to be mentioned first. Study 1A and Study 1B show that these results hold for typed and spoken queries. Study 2 suggests that users do not have meta-awareness of this strategy. These findings can contribute to a better detection of user intent. Incorporation of word order sensitivity into technology design can lead to more efficient and satisfactory human-computer interactions.
Government information and documents and technical reports

The increased reliance, development, and access policies, programs, and services related to digital, full-text, online access to all forms of Government Documents and reports literature continues. The presence of online catalogues of libraries’ holdings is nearly universal and aggregating them in databases, such as WorldCat (in all of its formats), is commonplace.
Learning Interpersonal Stance in Voice

This work presents a simulation-based training application to enhance people’s ability to express interpersonal stance using their voice. The results show that the application helps people to learn the principles of Leary’s interpersonal stance theory, and distinguish between the eight basic stances. Nevertheless, it remains difficult for people to express them without mixing the categories. Although the increase was small, the training application improved the users performance in expressing interpersonal stance in their voices after three days of training. The results also indicate that one of the main barriers of the learning process is that people need time to incorporate or refine interpersonal stances.
Attribute analysis with classification algorithm on election participation

Abstract Stages of General Elections of the President, DPD, DPR, Provincial DPRD and Regency / City DPRD in Indonesia are determined by institutions namely the General Election Commission (KPU), where there is a measure of success in holding direct, general, and free. Another component of the implementation of elections is that there are contestants and voters. In the voter factor, this is also a measure for success in the overall process of implementation, namely success if high community participation in the administration of elections. However, vice versa, if community participation is low, one of them is the level of public confidence in the organizers (government) decreases. Data mining classification analysis and modification of attributes in prediction classes “Hadir” and “Tidak Hadir” on the final voter list (DPT). The number of datasets is 4249 instances, and the number of attributes is 11. The percentage results are 89.3417% for the Naive Bayes algorithm for prediction classes in the Presidential Election, DPD, DPR, Provincial DPRD and Regency/City DPRD in 2019. Further analysis is done on eliminating some attributes to obtain information, whether it has a significant effect on the results of predictions. And in this analysis, for the ten attributes with the removal of “statusKK” (highest rank gain ratio) for the prediction class, the results are the worst. = After nine attributes, the removal of the “rt” and “tps” attribute (second and third rank) for the prediction class is the best result. There is the highest percentage difference for the prediction class on the classification algorithm for modified and or unmodified status attributes, the results of the percentages and classification algorithms are different.
Deep learning method for rain streaks removal from single image

In this study, a deep learning method for rain streaks removal from a single image is proposed. The main idea is to reuse the original image in the network because the original image can provide more details of the background. These details can be useful for the restoration of the image after rain streaks removal. The authors concatenate the input image of the network and the feature maps generated by the former layers before entering the subsequent layers. The convolutional layers reusing the original image are called ROIC (Reusing Original Image Convolutional) layers. They take the original image as a part of their inputs. The network consists of five convolutional layers: one regular convolutional layer and four ROIC layers. Despite the fact that the network is trained on the synthetic data, experimental results show that the proposed method has comparable performance on both synthetic images and real-world images to the state-of-the-art methods.