Reviewerly/exHarmony huggingface dataset:
Algorithmic Censorship on Social Platforms: Power, Legitimacy, and Resistance

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Design and application of national reserved cultivated land resources investigation and evaluation database management system

The investigation and evaluation data of reserved cultivated land resources are of great significance to the research and policy-making about cultivated land protection, it is necessary to establish stable and efficient information systems to manage and use the massive data. To this end, this article adopt a hybrid storage strategy, uses relational database, non-relational database and file database to establish the national investigation and evaluation database of reserved cultivated land resources. Automatic and parallel database building technology, spatial big data calculation technology and vector data dynamic rendering technology are researched on, which has solved the technical bottleneck of large-scale spatial data storage, management and visualization. On this basis, developed the database management system, it has been applied in the government administration of cultivated land resources in China and exerts positive results.
FSCR: A Deep Social Recommendation Model for Misleading Information

The popularity of intelligent terminals and a variety of applications have led to the explosive growth of information on the Internet. Some of the information is real, some is not real, and may mislead people’s behaviors. Misleading information refers to false information made up by some malicious marketer to create panic and seek benefits. In particular, when emergency events break out, many users may be misled by the misleading information on the Internet, which further leads them to buy things that are not in line with their actual needs. We call this kind of human activity ‘emergency consumption’, which not only fails to reflect users’ true interests but also causes the phenomenon of user preference deviation, and thus lowers the accuracy of the personal recommender system. Although traditional recommendation models have proven useful in capturing users’ general interests from user–item interaction records, learning to predict user interest accurately is still a challenging problem due to the uncertainty inherent in user behavior and the limited information provided by user–item interaction records. In addition, to deal with the misleading information, we divide user information into two types, namely explicit preference information (explicit comments or ratings) and user side information (which can show users’ real interests and will not be easily affected by misleading information), and then we create a deep social recommendation model which fuses user side information called FSCR. The FSCR model is significantly better than existing baseline models in terms of rating prediction and system robustness, especially in the face of misleading information; it can effectively identify the misleading users and complete the task of rating prediction well.
On the design of the URL

Abstract Notations can affect the way we think, and how we operate; consider as a simple example the difference between Roman Numerals and Arabic Numerals: Arabic Numerals allow us not only to more easily represent numbers, but also simplify calculations and the manipulation of numbers. One of the innovations of the World Wide Web was the URL. In the last 30 years URLs have become a ubiquitous element of everyday life, so present that we scarcely even grant them a second thought. And yet they are a designed artefact: there is nothing natural about their structure – each part is there as part of a design. This paper looks at the design issues behind the URL, what a URL is meant to represent, how it relates to the resources it identifies, and its relationship with representational state transfer (REST) and the protocols that REST is predicated on. We consider, with hindsight, to what extent the design could have been improved.
An Improved Parallel SVM Algorithm on Distributed System

Support vector machine (SVM), a popular machine learning method, has good generalization performance while solving classification problems and has been applied to a wide range of domains such as medicine, finance, biotechnology for supervised learning. In order to handle large-scale problems, various parallel SVM algorithms have been developed. Existing parallel algorithms usually use the entire dataset for training SVM, however there are very few samples contribute to the final model. The task of this paper is to design an improved parallel SVM algorithm, which can effectively reduce the size of dataset before the training process to accelerate SVM. Meanwhile, in order to maintain the classification accuracy of the final model, we propose a method to recover effective data from the eliminated samples. We implement the proposed algorithm using MPI and compare it with LIBSVM - a popular sequential SVM software - and parallel sequential minimal optimization (PSMO). Experiments show that our approach significantly reduces the computation overhead and maintains the classification accuracy.
Overview of Earthquake Early Warning Information Release System in Fujian Province, China

Abstract Earthquake early warning is a technical method that can effectively reduce earthquake disasters. In May 2018, the Fujian Earthquake Agency officially provided information services such as earthquake early warning and earthquake quick report to the public through dedicated receiving terminal and the smart phone application named “Fujian Earthquake Early Warning”. This article briefly introduces the preliminary work of the Fujian Earthquake Early Warning Data Processing System, and focuses on the construction of the Fujian Earthquake Early Warning Information Release System. Finally, taking the M6.4 earthquake in Yilan in 2019 as an example, the actual effect of the earthquake early warning information service is introduced.
GPU Accelerating for Color-Texture Image Clustering Based on Neuro-Morphological Approach

In this paper, we present a GPU acceleration of color-texture features extraction method, followed by a proposed neuro-morphological segmentation approach. in fact, we extract both texture and color information from each pixel of the image, the extraction phase is executed on an NVIDIA GPU using the CUDA environment to compute the color-texture features in parallel for each pixel. In this study, we chose to use Fractal and Haralick texture features combined with color features presented in RGB color space. The proposed neuro-morphological segmentation approach is based on the presentation of these features on a Kohonen map, followed by a morphological segmentation of this map using our watershed transformation. In addition to validating this segmentation approach by comparing it with k-means, the experimental results highlight the performance gap between the proposed GPU-based implementation of the extraction phase and the sequential implementation based on CPU.
On kurtoses of two symmetric or asymmetric populations

In this paper, the asymptotic distribution for the ratio of the sample kurtoses of two independent datasets is derived. It is then applied to construct the confidence interval and also the hypothesis test for the ratio of the kurtoses of two independent populations. The ability of the introduced technique is studied through a simulation study. The results verify that the values of coverage probability are very close to the considered size, especially when the sample sizes grow. The applicability of proposed method is also investigated using a medical dataset.
Group Activity Recognition by Exploiting Position Distribution and Appearance Relation

Group activity recognition in multi-person scene videos is a challenging task. Most previous approaches fail to provide a practical solution to describe the person relations and distribution within the scene, which is important for understanding group activities. To this end, we propose a two-stream relation network to simultaneously deal with both position distribution information and appearance relation information. For the former, we build Position Distribution Network (PDN) to obtain the spatial position distribution. For the latter, we propose Appearance Relation Network (ARN) to explore the appearance relation of the individuals in scene. We fuse the two clues, i.e. position distribution and appearance relation, to form the global representation for group activity recognition. Extensive experiments on two widely-used group activity datasets demonstrate the effectiveness and superiority of the proposed framework.
Cultivating Biafran agenda in Nigeria: Evaluation of the influence of radio Biafras rhetoric of ethnic marginalization on rural dwellers in the South-east

The systematic exclusion of the Igbos from key national leadership positions in Nigeria made the rise of secession-seeking groups and demand for self-government inevitable in the region.IPOB is one of these secession seeking groups which uses its radio station to communicate its agenda.Following its stirring rhetoric and extremist views, much has been heard of the place of radio Biafra in the ongoing IPOB agitation in the south east.Owing to radio's ability to cultivate social and political realities in the minds of people, rhetoric of marginalization which forms the basis of radio Biafra's broadcast may not leave people free from thinking of what is being communicated and what could be done to actualize the desired self-freedom which the station cultivates.Against this background, this study examined respondents' level of exposure to radio Biafra's broadcast, their perception of the broadcast messages and whether the messages persuade them into believing the course being preached from the station.The study was anchored on cultivation and social exchange theories with survey and FGD as research methods in studying 400 respondents purposively selected from 1,306,739 which forms the population of the study.Findings revealed that respondents believed in the mission but lacks the will-power to realize the dream.The study, therefore, recommended that radio Biafra content presenters should reconsider the messages they dish out to the people to avoid a repeat of the Rwandan episode in Nigeria.It was also recommended that government should look into these marginalization complaints from South-Eastern states.
Identification of soldiers and weapons in military armory based on comparison image processing and RFID tag

Introduction/purpose: The process of issuing and retrieving weapons in the military should be fast enough and should provide immediate availability of accurate information on the status of weapons. Methods: This paper deals with the problem of digitizing the recording of issuing and returning weapons through the use of modern Edge computing technology. The problem is presented through two approaches. The first approach is based on the application of machine learning algorithms for recognizing the serial number of a weapon based on the camera image, while the second approach concerns the application of RFID technology. User authentication is based on the application of biometrics. Results: The results obtained from testing the architecture for identifying weapons using a camera indicate that such an architecture is not suitable for identifying weapons. A weapon identification solution using RFID technology overcomes the problems of the previously mentioned solution. However, RFID technology requires additional modifications regarding the implementation of tags on or into weapons so that readings can be made. Conclusion: The implemented weapon identification solution based on RFID technology and a user identification solution with biometric authentication enables easy and reliable identification, speed of issuing and retrieval of weapons, network relieving, and real-time monitoring of the weapon status.
CrossPriv

The design and implementation of artificial intelligence driven software that keeps user data private is a complex yet necessary requirement in the current times. Developers must consider several ethical and legal challenges while developing services which relay massive amount of private information over a network grid which is susceptible to attack from malicious agents. In most cases, organizations adopt a traditional model training approach where publicly available data, or data specifically collated for the task is used to train the model. Specifically in the healthcare section, the operation of deep learning algorithms on limited local data may introduce a significant bias to the system and the accuracy of the model may not be representative due to lack of richly covariate training data. In this paper, we propose CrossPriv, a user privacy preservation model for cross-silo Federated Learning systems to dictate some preliminary norms of SaaS based collaborative software. We discuss the client and server side characteristics of the software deployed on each side. Further, We demonstrate the efficacy of the proposed model by training a convolution neural network on distributed data of two different silos to detect pneumonia using X-Rays whilst not sharing any raw data between the silos.
Quality of Telehealth Services in Primary Care in the State of Minas Gerais

Objective: Telemedicine is seen as an important tool to face contemporary health challenges. The factors that help improve quality in these services must be studied. The objective of this work was to assess the quality of telehealth primary care services offered in the State of Minas Gerais —Brazil, based on the ISO 13131 standard. Methods: This cross-sectional analytical study with a quantitative approach was conducted in the cities in the state of Minas Gerais that use telehealth services. A stratified sample composed of 385 cities was used. A questionnaire, based on ISO 13131 on the quality of telehealth services, was prepared, used, and verified for its consistency. Quality levels in telehealth were built from the data. To analyze the quality of care, data from the Ministry of Health's quality improvement program were used, involving 366 surveyed cities. Logistic regression was performed to verify the association between quality of telehealth and quality of care. Results: The research identified that 64% of the cities had records of telehealth activities, and 51% of cities had high quality telehealth services. There was no association between quality of telehealth and quality of care; only the dimensions of Quality and Risk Management were associated with quality of care. Conclusion: The developed instrument enabled the quality of telehealth actions to be verified. The State of Minas Gerais has high-quality telehealth services.
A Novel Optimization Algorithm Inherited From Gravitational and Spherical Search Dynamics

The meta-heuristic is becoming important in the field of modern optimization, and gained more and more attention. In the past thirty years there has been a wide range of meta-heuristic, such as gravitational search algorithm (GSA), has achieved a great success. Spherical search (SS) is the one of newest proposed meta-heuristic algorithms. SS performs search effectively in exploration, but due to the lack of local exploitation ability, it converges slowly and can't exploit the small region around the current promising solution. This paper proposes a novel optimization algorithm, namely SSGSA, which is inherited from the SS and GSA to combine the effective exploration and exploitation of each algorithm, respectively. To evaluate the effectiveness of SSGSA, we compared it with the original SS, original GSA, particle swarm optimization, and whale optimization algorithm on the IEEE CEC'17 benchmark function suit. Experimental results show that the proposed new method outperforms its competitors in terms of convergence speed and solution accuracy.
The Main Trends of Integration of Russian Education into the Global Space

The beginning of the XXI century is characterized by the accelerated process of globalization of all spheres of human life and the rapid growth of science-intensive technologies. These processes have a significant impact not only on the economy but also on education. In this regard, the author focuses on the problems of integration of Russian education into the world community. The study was conducted on the basis of methods of comparative analysis, systematic and situational approaches. The theoretical basis of the research is the work of Russian and foreign scientists aimed at studying the key trends in the development of the world community and the main challenges facing the system of world education. The author also conducted an analytical review of scientific literature, the results of which summarized the foreign and Russian experience.The author concludes that a new model of education is being formed, the principal difference of which is that education becomes continuous throughout life and actively develops in the conditions of digitalization. At the same time, the modern system of higher education becomes the Foundation of globalization, erasing ethnic, cultural and information differences.
Brokering Votes with Information Spread via Social Networks

Politicians rely on political brokers to buy votes throughout much of the developing world. We investigate how social networks facilitate these vote-buying exchanges. Our model suggests that brokers should be well-placed within the network to learn information about citizens, and that information about non-copartisansâ€™ reciprocity is particularly valuable to target transfers effectively. As a result, parties should recruit brokers who are central among non-copartisans. We combine village network data from brokers and citizens with broker reports of vote buying. We show that networks diffuse politically-relevant information about citizens to brokers who leverage it to target transfers. In particular, among those citizens who are not registered to their party, brokers target reciprocal citizens about whom they can learn more through their network. Moreover, recruited brokers are significantly more central than other citizens among non-copartisans, but not among copartisans. These results highlight the importance of information diffusion through social networks for vote buying and ultimately for political outcomes.
Learning Latent Perception Graphs for Personalized Unknowns Recommendation

The fast-growing online-learning platforms, which are very convenient and contain rich course resources, have attracted many users to explore new knowledge online. However, the learning quality of online-learning is generally not as effective as offline classes. In offline studies in classrooms, teachers can interact with students and teach students in accordance with personal aptitude from students' feedback in classes. Without such real-time interaction, it is difficult for users to be aware of personal unknowns. In this paper, we consider an important issue to discover “user unknowns” from the question-giving process in online-learning platforms. A novel personalized learning framework, called PagBay, is devised to recommend user unknowns in the iterative round-by-round strategy, which contributes to applications such as a conversational bot. The flow enables users to progressively discover their weakness and to help them progress. However, discovering personal unknowns is quite challenging in online-learning platforms. Even though solving the problem with previous recommender algorithms provides solutions, they often lead to suboptimal results for unknowns recommendation as they simply rely on the user ratings and contextual features of questions. Generally, questions are associated with perceptions, and mining the relationships among users, questions, and perceptions potentially provide the clue to the better unknowns recommendation. Therefore, in this paper, we develop a novel recommender framework by borrowing strengths from perception-aware graph embedding for learning user unknowns. Our experimental studies on real data show that the proposed framework can effectively discover user unknowns in online learning services.
The geometrical analysis of localization error characteristic in stereo vision systems

The parameters of a stereo camera directly determine the accuracy of a stereo vision positioning system. In this paper, the relationship between camera parameter error and positioning error is established by using the geometric analysis method with a clear physical meaning. The error models of the relative position error and camera resolution error of the stereo camera are established. The relative position error of the stereo camera is represented by the rotation and translation of the coordinate system, and the positioning error is derived from image coordinates through the pinhole camera model. For the camera resolution error, the two pixels of the stereo camera are projected into an octahedral uncertain region in space. We use geometric methods to derive the maximum size of the octahedron in the three-axis direction. The maximum size in the three-axis direction is used to evaluate the impact of camera resolution errors. The depth of field and angle of view are used to represent the measured area. The effects of resolution error and camera parameters on the measurement error of each axis are analyzed. Finally, a large number of simulations verified our conclusion. By comparing the effects of the magnitude of the error, we can conclude that the baseline and pixel size of the camera have a greater impact on the positioning accuracy.
Particle Filters Revisited

Particle filters are often explained by either heuristics arguments or complex mathematics. Present day particle filters rely on various methods such as importance sampling, resampling method and resampling strategy. Moreover, there are different derivations for discrete and continuous time dynamic models. In this paper we offer a new simple derivation which works both in the discrete and continuous case, and which avoid importance sampling and resampling. The resulting algorithm has a performance as good as the standard MATLAB particle filter.
Monitoring an anonymity network: Toward the deanonymization of hidden services

Anonymity networks are an example of Privacy Enhancing Technology (PET) whose historical goal is to avoid censorship, preserve users privacy, and promote freedom of speech. Such networks, however, also provide a “safe haven” for criminal activity: previous research observed a dominance of commerce platforms delivered as hidden services within The Onion Router (Tor) network, undoubtedly the most popular anonymization technology at the time of writing, largely around narcotics and illegal financial services. Extensive research has been conducted on locating hidden services on the Tor network, but a general method that is able, given a service delivered via anonymity network, to effectively produce a list of candidate nodes responsible for delivering the service still remains an open research problem. In this paper we describe the infrastructure we have designed and implemented for monitoring the Invisible Internet Project (I2P) network, which is a smaller scale anonymity network compared to Tor but already proven to be used for illicit activities, and how its output can be used to enable such general method.