Reviewerly/exHarmony huggingface dataset:
Weakly Supervised Learning with Discrimination Mechanism for Object Detection

In order to reduce the time consuming and expensive process of manually annotating data, and achieve the purpose of lightweight deployment. In this paper, an object detection method for weakly supervised learning with discrimination mechanism is proposed. We introduce the classification branch and the location branch based on the Darknet-53 backbone network of YOLO model, utilize Global Average Pooling (GAP) and Softmax to complete classification on selected areas, and adopt classification activation map for location. In addition, we use a model compression mechanism for model pruning operations, which reduces the size of the model and achieves the lightweight goal. These can effectively solve the problems of object detection to a certain extent. The results show that the improved model achieves good performance in terms of robustness and stability while maintaining the accuracy and efficiency of object detection, further improving the effectiveness of object detection tasks in practical application scenarios.
Research on Collision Avoidance Path Planning of Double SCARA Robot

Abstract This paper takes double SCARA robot as the research object and uses the computer graphics method to get the collision detection method of two robotic arms at a certain moment. On the basis of collision detection, the C-space method is used to plan the collision avoidance path for the two robots and the two robots considering the end-effector, and the collision avoidance algorithm for the double SCARA robot is obtained. Then, the simulation experiment is carried out to verify the correctness of the algorithm.
Basic Intrusion Technology of Industrial Internet of Things—Based on Machine Learning

Abstract With the continuous advancement of technology and the continuous development of the Industrial Internet of Things, network security has become particularly important, especially in the detection of intrusion attacks. This article briefly introduces the Industrial Internet of Things, and the current network security threats faced by the Industrial Internet of Things, as well as analyzes different intrusion methods. Three machine learning algorithms are applied in this article to analyze and compare their effects in intrusion detection. Through experiments, a more suitable machine learning model for industrial IoT intrusion detection is selected.
Research on Effective Management and Control team System of Power Grid Based on Internet Technology

Abstract In the safe production and operation of electric power, grassroots teams have undertaken the main work. How to effectively control and control teams is an important issue in the power grid. This paper has designed the mobile internet management technology based on "Internet+" for mobile teams, including mobile operating terminals based on Android. Design and development, large-screen display of work order management and control based on GIS and development of web-side background management platform. The team basic management system is convenient and flexible in managing all links of team basic information. Have good Ground scalability central system to meet the development of various new personalized services, effectively improve the basic management of teams, Scientific scientific level.
Illumination-Adapted Long-Term Tracking Based on Multiple Correlation Framework

Abstract Object tracking is a hot field in image processing, among which the kernel correlation filtering algorithm is widely applied based on the advantages of precision and speed. However, due to information decay, it has poor performance in the long-term tracking. In this paper, we develop an illumination-adapted long-term tracking method based on correlation filtering tracker. Firstly, we introduce illumination adaptive normalization and information entropy weighted feature fusion to improve the prediction accuracy. Secondly, we estimate the object scale quickly based on the binary strategy to improve the speed of the algorithm. Then, we design re-detection based on online SVM detection for target loss. Finally, we design an appropriate updating mechanism to combine the whole tracking framework with various judgment thresholds. The experiment on the data set shows that all of our modules are effective and have great improvement as compared with other correlation filter type trackers.
Bitcoin at High Frequency

This paper studies the behaviour of Bitcoin returns at different sample frequencies. We consider high frequency returns starting from tick-by-tick price changes traded at the Bitstamp and Coinbase exchanges. We find evidence of a smooth intra-daily seasonality pattern, and an abnormal trade- and volatility intensity at Thursdays and Fridays. We find no predictability for Bitcoin returns at or above one day, though, we find predictability for sample frequencies up to 6 hours. Predictability of Bitcoin returns is also found to be time-varying. We also study the behaviour of the realized volatility of Bitcoin. We document a remarkable high percentage of jumps above 80%. We also find that realized volatility exhibits: i) long memory, ii) leverage effect, and iii) no impact from lagged jumps. A forecast study shows that: i) Bitcoin volatility has become more easy to predict after 2017, ii) including a leverage component helps in volatility prediction, and iii) prediction accuracy depends on the length of the forecast horizon.
Function Point Estimation for Portal and Content Management Projects

The success of a Software project is determined by how well the initial estimates have done. Hence, the effort and schedule estimates are essential in Software Project planning stages. Portal and Content Management (PCM) projects have been facing critical challenges while estimating the effort. The Organization has adopted the Function Point Analysis technique (FP) to overcome such challenges. Further, the organization has developed guidelines to apply FP on PCM projects. The key objective of this paper is to provide guidelines to estimate the effort of PCM projects by employing FP to avoid cost overruns and unproductive use of resources. Experimental results are proved that the proposed methodology is yielding better results by fixing the potential challenges. It is further ensured that the methodology leads to better estimate of the project cost, optimal resource utilization, on-time project delivery and others.
Predicting code context models for software development tasks

Code context models consist of source code elements and their relations relevant to a development task. Prior research showed that making code context models explicit in software tools can benefit software development practices, e.g., code navigation and searching. However, little focus has been put on how to proactively form code context models. In this paper, we explore the proactive formation of code context models based on the topological patterns of code elements from interaction histories for a project. Specifically, we first learn abstract topological patterns based on the stereotype roles of code elements, rather than on specific code elements; we then leverage the learned patterns to predict the code context models for a given task by graph pattern matching. To determine the effectiveness of this approach, we applied the approach to interaction histories stored for the Eclipse Mylyn open source project. We found that our approach achieves maximum F-measures of 0.67, 0.33 and 0.21 for 1-step, 2-step and 3-step predictions, respectively. The most similar approach to ours is Suade, which supports 1-step prediction only. In comparison to this existing work, our approach predicts code context models with significantly higher F-measure (0.57 over 0.23 on average). The results demonstrate the value of integrating historical and structural approaches to form more accurate code context models.
Massive-scale graph-clustering-based data management based on multi-metrics

With the explosive growth in the amount of data nowadays, we have entered the era of the "big data". In this circumstance, it is becoming more and more significant to effectively and efficiently managing such massive-scale data. Traditionally, data are managed by only one single and unified metric, which might be too restrict due to the incapability to represent their social attributes (i.e., each sample and its socially-connected neighbors in the feature space). To tackle this problem, we propose a multi-metric framework for data similarity management, wherein the multiple metrics are derived through a dense subgraph mining algorithm. More specifically, for each sample, we first extract multi-channel features to characterize it, based on which multiple massive-scale affinity graphs can be constructed accordingly. Afterward, for affinity graph from each feature channel, we discover the densely distributed subgraphs on it. Thereby, each sample's social network can be obtained and the metric can be defined accordingly. Finally, we can fuse the multiple metrics corresponding to social networks from different feature channels. In this manner, we obtain the so-called multi-metric similarity measure, which is socially-aware and optimally fuses multi-channel features. Comprehensive experimental results on six publicly available data sets have demonstrated the competitiveness of our proposed multi-metric in massive-scale data classification and retrieval.
A Aplikasi Pembelajaran Hiragana Bahasa Jepang Berbasis Android Menggunakan Speech Recognition: Aplikasi Pembelajaran Hiragana Bahasa Jepang Berbasis Android Menggunakan Speech Recognition

The alternative to learning Japanese is to use mobile learning. Based on comparisons made on the 7 popular Japanese language learning applications in the PlayStore, there is no Japanese language learning application that has a speech recognition feature for Japanese sentence pronunciation. In this study Japanese hiragana learning application was designed for sentences using speech recognition. This application uses google cloud speech-to-text for speech recognition features. The algorithm that is applied in this application is the levenshtein distance to compare the user's voice input with the text in the database. Android based application for users and the web for admin. Based on the results of testing the accuracy and success of detection, the ideal distance of the application receiving the user's voice input is 50 cm in a noise-free environment with a successful detection of 100%. In testing before and after using the application there was an increase in vocabulary knowledge of 44.48% after users used the application. Based on usability testing using SUS, the result is 73.01 which indicates that the application falls into the acceptable category.
A novice-friendly induction tactic for lean

In theorem provers based on dependent type theory such as Coq and Lean, induction is a fundamental proof method and induction tactics are omnipresent in proof scripts. Yet the ergonomics of existing induction tactics are not ideal: they do not reliably support inductive predicates and relations; they sometimes generate overly specific or unnecessarily complex induction hypotheses; and they occasionally choose confusing names for the hypotheses they introduce.
Enhancing Students’ Vocabulary Mastery in Learning English Through Digital Extensive Reading in Indonesia

The purpose of this research is to enhance the students’ vocabulary mastery through the Digital Extensive Reading activity in Junior High School. There were 30 learners of grade VIII participated in this study for 4 weeks. The qualitative data was analyzed by the three steps: data reduction, data display, and conclusion/verification, while the quantitative data was analyzed using the descriptive statistics. The data were collected from the observation, the interview and the students’ pre-test and post-test. The steps of implementing the digital extensive reading activity were included the digital silent reading, the reading report book, the use of dictionary and the retelling. The results showed that the significant improvement from 45.83 in the pre-test to 62.76 in the post-test of the students’ vocabulary mastery. Therefore, the digital extensive reading activities improved the students’ vocabulary mastery. Keywords : digital extensive reading, vocabulary mastery
Secure Image Transmission in Wireless Network Using Conventional Neural Network and DOST

In digital science, organizations have to need secure transmission especially for image data. Images are encrypted to provide the secure transmission over the network. To encrypt, image processing concepts play a very important role. With this motivation, this paper is proposed image encryption in discrete orthonormal Stockwell transform (DOST) and CNN. For image encryption, pixel shuffling method and Arnold transform are used in DOST domain. The proposed methods are compared and analyzed the performance using mean error, PSNR and Entropy difference. The obtained results are better in compared to existing methods.
A Logic Programming Approach to Regression Based Repair of Incorrect Initial Belief States

This paper explores the challenge of encountering incorrect beliefs in the context of reasoning about actions and changes using action languages with sensing actions. An incorrect belief occurs when some observations conflict with the agent’s own beliefs. A common approach to recover from this situation is to replace the initial beliefs with beliefs that conform to the sequence of actions and the observations. The paper introduces a regression-based and revision-based approach to calculate a correct initial belief. Starting from an inconsistent history consisting of actions and observations, the proposed framework (1) computes the initial belief states that support the actions and observations and (2) uses a belief revision operator to repair the false initial belief state. The framework operates on domains with static causal laws, supports arbitrary sequences of actions, and integrates belief revision methods to select a meaningful initial belief state among possible alternatives.
Model-based Performance Evaluation of a Moving Target Defense System

Moving target defense (MTD), emerging as a game-changer in the cyber defense area, has got a lot of attention and development recently. As a proactive defense technique, MTD dynamically changes system attributes in order to create more uncertainties of the system and has been proved to be effective against cyber attacks. Beyond this, there is still a lack of researches with respect to the quantitative analysis of the effect of MTD on system performance. This paper aims to quantitatively investigate how MTD affects system performance while bringing security. We develop Markov process-based models for two different MTD strategies and derive the formulas for metrics of interest. We carry out simulation experiments to validate our proposed models with Mininet. Furthermore, numerical analysis is conducted for comparing these two different strategies in terms of system performance. The numerical results also show how different parameters affect the evaluation metrics. Our models can help defenders conFigure the MTD system in the most suitable way.
Declarative Pearl: Deriving Monadic Quicksort

To demonstrate derivation of monadic programs, we present a specification of sorting using the non-determinism monad, and derive pure quicksort on lists and state-monadic quicksort on arrays. In the derivation one may switch between point-free and pointwise styles, and deploy techniques familiar to functional programmers such as pattern matching and induction on structures or on sizes. Derivation of stateful programs resembles reasoning backwards from the postcondition.
Descriptive function of numeral classifiers

This chapter aims to define the grammatical function and the relevant meaning of numeral classifiers in Korean. We focus on the descriptive function of numeral classifiers in relation to their head nouns, especially advocating the semantic relevance between numeral classifiers and their possible head nouns. In support of claiming the descriptive function of classifiers, the formation of compound nouns from classifiers and their possible head nouns will be examined. Those classifiers constructing compound nouns retain their descriptive meaning but lose their function as numeral classifiers. We claim numeral classifiers keep the descriptive function in numeral classifier phrases, leaving the task of counting or measuring to the numerals that accompany the classifiers. A corpus analysis has shown that numeral classifiers are decreasing in current Korean and that the general numeral classifier kay ‘CL’ is normally prevalent in use. Also, numerals not accompanying classifiers are often preferred in current Korean. Classifiers may help describe the fundamental nature of their head nouns.
Brief Study to Explore Trust and Security Challenges in Cloud Computing

Abstract Cloud Computing is an online strategy for dynamic allocation of services by reducing the usage of hardware and software resources. Existing models of cloud computing include different components: end-user computers, contact networks, access control frameworks, and software infrastructures. The data and the cloud infrastructure must be protected from any known and unknown attacks on all cloud platforms to achieve comprehensive cloud security. Trust issues arise from uncertainty about certain actions that can be defined through the concept of fuzzy belief. Trust is faith in quality provided by the cloud service provider and third-party agent on consumers and vice-versa where quality can be considered in terms of functionality, security, privacy, data safety, and reliability on cloud resources. Although the members of a social network have a strong level of trust, this trust may not be adequate in certain cases and becomes an obstacle in the successful implementation of cloud services. For example, a storage facility where users are vulnerable to missing, infected, or corrupted data, while providers are at risk of unknown data being hosted from their network. The relationship between two business partners decays more rapidly relative to the trust of users in the social network of the cloud. This paper aims to address the trust issues in existing cloud paradigms and highlights the existing flaws which will be resolved in the future course of action.
Video Action Recognition Based on Spatio-temporal Feature Pyramid Module

Modeling the spatio-temporal information of different actions facilitates their recognition. The mainstream 2D convolutional network has low computational cost but cannot capture timing information; the mainstream 3D convolutional network can extract spatio-temporal features but has a huge amount of calculation and is difficult to deploy. In this paper, a Spatiotemporal Feature Pyramid Module(STFPM) is proposed to extract spatio-temporal feature information. STFPM captures temporal information between frames by dilated convolution and fuses feature information by weighted addition. STFPM can be flexibly inserted into the 2D backbone network in a plug-and-play manner. When equipped with STFPM, 2D ResNet-50 achieves good results on UCF101 dataset and HMDB51 dataset.
Double Negation and NPI Licensing

In English, double negation licenses weak NPIs while strong NPIs do not. It is also the case in Dhaasanac. This chapter is a preliminary investigation of NPI licensing by double negation in Dhaasanac.
MTCNN Based on Gaussian Mixture Image Pyramid

As the first two steps of face recognition system, face detection and key point location are of great significance to the accuracy of face recognition system. The Multi-task Cascaded Convolutional Network(MTCNN) uses a single network model for the first time to complete the task of face detection and key point location. It is easy to find the problem of false detection in the practice of people's face detection. Therefore, this paper introduces multi-scale and multi template data preprocessing in MTCNN model. By increasing the diversity of data, the accuracy of MTCNN model is enhanced. The results show that compared with the original MTCNN model, the recall of the hybrid test set composed of WIDER Face and Celeba is increased by 2% after introducing multi-scale and multi template data preprocessing.
Efficiënt load balancing by optimized flexi max-min algorithm

Abstract Load Balancing is required to distribute tasks across Virtual Machines on one hand and on the other hand in a cloud Environment. Here, a new task scheduling algorithm: ‘Optimized Flexi Max-Min Scheduling Algorithm’ is proposed. The algorithm maintains a data structure which is modeled after a Binary Search Tree (BST) for estimations, enhanced searching, task allocation, and migration of tasks. Cloud-Sim is used to model and simulate the cloud computing environment in order to obtain simulated data. The result of this experiment shows that the proposed algorithm outperforms the traditional Max-Min task scheduling algorithms.
Analytical Online Technologies for Assessing the Readiness of University Management to Effectively Use Social Capital

The article describes the purpose, development process and results of using online technologies to assess the readiness of university leaders to effectively use social capital. A lot of works, which described the impact of social capital on the effectiveness of higher education institutions are devoted to the description of tools for evaluating effectiveness. As a rule, these studies illustrate the principles of empirical analysis of social capital in a real educational institution. In Russian universities, the culture of building and effectively using social capital is not developed. Also, many managers are not ready to effectively use the existing potential of social capital in everyday activity of the university. For assessing this readiness we offer the use of online technologies that are accessible and easy to use. Online instrument allows to evaluate the results independently and prepare data for be processed.
The measure strength of predictive in the poisson regression model with regression correlation coefficient case study of maternal mortality rate in Central Java Province in 2015

Abstract RCC which stands for regression correlation coefficient is an alternative measure strength of predictive that can be applied to the GLM model in which the distribution of response variable is not only normal. The RCC is constructed based on the definition of correlation coefficient by using generalized linear model (GLM). So, the RCC can be defined as a value that states the strength of the relationship between response variable Y and its conditional expectation given predictor variables E [ Y | X ]. The RCC is one measure of predictable power that can satisfies the property like applicability, interpretability, consistency, and affinity. In general, the explicit form of RCC on GLM is difficult to find. However, when RCC is applied to the Poisson regression model and the predictor variables are assumed to be a normal multivariate distribution, an explicit form is found. This explicit form still contains the unknown parameters derived from the Poisson regression model. Therefore, we need to find an estimate of these parameters to obtain an estimator from the RCC. The Poisson regression model which still contains the unknown parameters are estimated using maximum likelihood method. Application of regression correlation coefficient is done in case of maternal mortality rate in Central Java Province in 2015.