Adjacent Users Research Articles

Electronic Surveillance in Immigration Court: Evidence from the CalGang Database

Through a mixed-methods approach that draws on over 4000 pages of organizational documents and two quantitative data sets from federal and state entities, the authors ask, has the growth of electronic surveillance by local criminal justice agencies, with corresponding database interoperability and information sharing, changed how federal agencies—specifically immigration agencies—operate? We focus on the CalGang Database, California’s statewide gang intelligence database, to document how, since its inception in 1998, CalGang has grown both in the number of individuals under surveillance and the number of what we call adjacent users, which we define as people who can access CalGang data but were not initially intended as users and who, in pre-digital surveillance eras, would have a much more difficult time obtaining the information. In this paper we focus specifically on adjacent users in the immigration system who determine the detention and deportation of noncitizens by leveraging an information sharing agreement that gave U.S. Immigration and Customs Enforcement (ICE) access to Calgang between January 2006 and October 2016. Using a two-way “difference in differences” approach, we estimate the impact of CalGang interoperability on federal adjacent users by comparing outcomes across immigration cases that began before or after the data sharing agreement ended. Our qualitative and quantitative analyses both demonstrate that immigration adjudicators’ access to local electronic surveillance information is associated with an increase in the number of immigrants facing criminal immigration charges that do not necessarily require a formal criminal conviction in state court, and that deportation rates are higher when ICE has access to local electronic surveillance.

A Deep Community Detection Approach in Real Time Networks

Community detection in real time networks is one of the important aspect of social network analysis. Deep learning has been applied successfully in a variety of research fields in recent years. Proximity matrix is frequently used as the representation of the network structure. However, there are issues with the proximity matrix's insufficient spatial contiguity information. As a result, this research provides a deep learning applied community identification approach that combines the reorganization of the matrices, spatial attribute uprooting, and community identification. For obtaining a spatial proximity matrix, the primary proximity matrices in a real time graph is recreated using the highest weight and adjacent users. The dimensional proximity matrix can obtain a subdomain of the network, allowing the convolutional neural network (CNN) to draw out dimensional localization more easily and fast. Ten different real time datasets of social networks are used in tests to examine our proposed approach. Our results show that the proposed community identification approach has higher compatibility than existing deep learning-based strategies. As a result, the proposed deep community identification approach is capable of detecting the excellent clusters in real time networks.

Influence maximization algorithm based on group trust and local topology structure

Influence maximization is one of the important contents of social network analysis. Many classical influence propagation models assume that there is a stable information propagation phenomenon between adjacent users, and do not consider the influence of internal structure information of the network on the actual information propagation. Therefore, an influence maximization algorithm based on group trust and local topology structure is proposed. In order to make full use of the important role of group in information propagation, the concepts of intra-group connectivity, inter-group diffusion and group trust are defined based on the characteristics such as group tightness. Then, an influence propagation algorithm based on the local topological structure of the group is proposed to extract the local structure information of different topological positions in the group, and calculate the propagation probability between users. Finally, the seed nodes were selected according to the credibility ranking of the group for influence propagation. Experiments on multiple data sets show that compared with other algorithms, the algorithm can achieve higher propagation efficiency and wider influence effect, which verifies the rationality and effectiveness of the method.

Collaborative Virtual 3D Object Modeling for Mobile Augmented Reality Streaming Services Over 5G Networks

This paper presents a collaborative virtual 3D object modeling system for mobile augmented reality (AR) streaming services over 5G networks. The objective of the proposed system is to provide AR streaming services with low latency and high virtual object quality by effectively leveraging mobile edge cloud (MEC) and device-to-device (D2D) communication. MEC is utilized to perform computation-intensive 3D object modeling, such as virtual object slicing and mesh simplification, with abundant computing and storage resources. D2D communication is also utilized to reduce transmission delay by sharing virtual objects of interest among adjacent users. To achieve this goal, we propose a part-segment quality selection algorithm that can control the quality of each part segment and the transmission source by considering the users network condition. In the proposed system, all elements in the AR cloud, MEC, and mobile device are technically integrated to work together seamlessly. Finally, the proposed system is fully implemented using well-known open-source frameworks, including WebGL and AR.js. The system was then examined in real wireless network environments. The experimental results demonstrate the performance of the proposed system. The proposed system outperforms conventional systems in terms of service latency and visual 3D object quality.

D2D Assisted Multi-Antenna Coded Caching

A device-to-device (D2D) aided multi-antenna coded caching scheme is proposed to improve the average delivery rate and reduce the downlink (DL) beamforming complexity. Novel beamforming and resource allocation schemes are proposed where local data exchange among nearby users is exploited. The transmission is split into two phases: local D2D content exchange and DL transmission. In the D2D phase, subsets of users are selected to share content with the adjacent users directly. In this regard, a low complexity D2D mode selection algorithm is proposed to find the appropriate set of users for the D2D phase with comparable performance to the optimal exhaustive search. During the DL phase, the base station multicasts the remaining data requested by all the users. We identify scenarios and conditions where D2D transmission can reduce the delivery time. Furthermore, we demonstrate how adding the new D2D phase to the DL-only scenario can significantly reduce the beamformer design complexity in the DL phase. The results further highlight that by partly delivering requested data in the D2D phase, the transmission rate can be boosted due to more efficient use of resources during the subsequent DL phase. As a result, the overall content delivery performance is greatly enhanced, especially in the finite signal-to-noise (SNR) regime.

Effects of non-heating users in heating residential buildings on heat consumption

Building heat consumption is affected by factors such as building characteristics, surrounding non-heating users, and outdoor temperature. Therefore, in order to achieve precise and efficient heating, it is of great importance to study the effects of building characteristics and the existence of non-heating users on the building heat consumption. This paper proposed two factors to evaluate the influences of the building characteristics and the non-heating users on heat consumption in a multi-storey heating residential building. Based on the heat consumption without non-heating adjacent users, the top-side users, top-middle users, middle-side users, middle-middle users, bottom-side users and bottom-middle users are identified as six types of typical-location users. Taking the heat consumption of middle-middle users as the benchmark, location factors (LFs) are introduced to consider the influence of building characteristics on the heat consumption of different typical-location users. For each typical-location user, non-heating user factors (NHFs) are introduced to consider the influences of different distributed non-heating adjacent users on the heat consumption of each typical-location user. The results show that, for low-temperature hot water floor radiant heating system, the impact of upstairs adjacent non-heating users is greater than that of same-floor adjacent non-heating users, and the impact of downstairs adjacent non-heating users is minimal. Besides, it can be considered the non-heating user factors vary slightly with the outdoor temperature when the outdoor temperature is below 0 °C. The results provide a guide for heating operation regulation of residential buildings with non-heating users.

An E-commerce Personalized Recommendation Algorithm based on Fuzzy Clustering

The key to the performance of recommendation system lies in which recommendation technology to choose. Personalized recommendation technology has attracted the extensive attention of more and more merchants and researchers with its humanization and potential commercial value, and earliest and most widely used personalized recommendation technology. However, with the increasing number of users and commodities, the recommendation system is facing some problems, such as the difficulty of ensuring the real-time performance and the decline of recommendation quality. Many scholars put forward many solutions to the shortcomings of collaborative filtering. So as to obtain better recommendation effect. Based on different angles, this paper adopts fuzzy clustering recommendation algorithm. Using fuzzy clustering algorithm to increase population diversity can avoid the lack of diversity of adjacent users; The feature of flexible partition of fuzzy c-means clustering algorithm is used to reduce the dimension of data, so as to effectively solve the problem of recommendation quality degradation caused by data sparsity.

Pseudo-channel matrix truncation based spatial correlation mitigation in massive MIMO

Massive multiple-input multiple-output (MIMO), a technique that can greatly increase spectral efficiency (SE) of cellular networks, has attracted significant interests in recent years. One of the major limitations of massive MIMO systems is pilot contamination, which will deteriorate the SE. The superimposed pilot-based scheme has been proved to be a viable method for pilot contamination reduction. However, it cannot break through another limitation of massive MIMO, i.e., spatial correlation. In addition, it will also lead to interference between the pilot and user data since they are imposed together. In this paper, we try to tackle these two issues, which will be described as follows. Firstly, a column-wise asymptotically orthogonal matrix, named as pseudo-channel matrix, is developed by orthogonalization of received signal. To recover the information about the large-scale fading (LSF) coefficients, the pseudo-channel matrix is truncated according to the cardinality of adjacent users set (CAUS). By this means, spatial correlation can be mitigated effectively. Secondly, robust independent component analysis (RobustICA) is used to reduce the interference caused by user data, and as a result the system performance can be further improved. Numerical simulation results demonstrate the effectiveness of the proposed method.

Multiuser joint downlink channel reconstruction based on spatial consistency

Massive multiple-input multiple-output (MIMO) systems working in frequency division duplex (FDD) transmission mode are facing great challenges because of the heavy training and feedback overhead. In this paper, we propose a novel algorithm for joint downlink channel reconstruction of multiple close-by users with reduced complexity and feedback overhead. Based on the spatial consistency, we extend the orthogonal matching pursuit algorithm to the multiuser case to jointly estimate the common channel parameters of these users. Specifically, a hierarchical dictionary is introduced to iteratively identify the common paths with reduced computational complexity. On another hand, based on the spatial reciprocity, the common path parameters are frequency-independent and can be obtained in the uplink, and only frequency-dependent channel parameters are then estimated in the downlink with reduced downlink training and feedback overhead. Numerical results verify the effectiveness and robustness of the proposed algorithm, and demonstrate that the proposed scheme can reduce the feedback overhead of channel estimation of adjacent users.

Comparative study of Existing Models for Online Social Network

Today, Online Social Networks becomes the first choice for businesses to broadcast their campaigns for branding, publicity, strategies, advertising, marketing, social influence and so many other areas. Social Network is a platform for communicating with social actors and Social Media is used by companies for broadcasting information. Online Social Network used by businesses for number of purposes but the primary concern is build new social connections that helps to target most audiences for successful campaign purposes. In OSNs sites the social objects are represented by nodes and the term edge used for connection between nodes under the graph theory. Today Social Network sites have becomes most exploded as compared to traditional sites because of impact of so many influence models over traditional models.Some of popular websites of OSN such as MySpace, Facebook, Flickr, YouTube, Google Video, Orkut, LinkedIn, Live Journal and BlogSpot have great impact on customer when targeting the sales marketing funnel for businesses. Adjacent users sometimes called engaged users tend to have more trust level as compared to random pairs users on the social media sites. Already have so much research that helps to calculate the trust factor using influence modeling. So influence models play a vital role to predict the behavior of the customer that helps to fulfill the goal of the business. The key contribution of this work is study of online social networking models.

Fairness-Aware Intragroup Cooperative Transmission in Wireless Powered Communication Networks

This article investigates fairness-aware transmission in a wireless powered communication network (WPCN), in which a group of adjacent users receive radio frequency energy and then cooperatively transmit information to a remote access point (AP) by using the harvested energy with time division multiple access (TDMA) scheme. Considering the constraints of practical energy harvesting electric circuits, a non-linear energy harvesting model is adopted in this paper. To guarantee both throughput fairness and signal-to-noise-ratio (SNR) fairness among users in the group, we propose a cooperative transmission protocol which contains three phases: wireless energy transmission (WET) phase, single information transmission phase and cooperative information transmission phase. Each user harvests wireless energy in WET phase, and then divides the harvested energy into two parts to be used in the rest two phases. In single information transmission phase, each user broadcasts its information to the AP and all the other users. In cooperative information transmission phase, all the users transmit information jointly to the AP. At the AP, the same signals in cooperative information transmission phase are combined with these in single information transmission phase by using maximum ratio combining (MRC) method. The common SNR of the received signals at the AP from all the users is achieved in both single information transmission phase and cooperative information transmission phase. Furthermore, the throughput fairness is achieved by common SNR and equal time allocation for information transmission at each user. Besides, we optimize the max-min throughput by joint WET time and harvested energy allocation. The optimal time allocation ratio and harvested energy allocation ratio are derived in closed forms. The cooperative diversity gain is achieved and numerical results demonstrate that the proposed protocol has low complexity and high reliability. Moreover, it also achieves a higher minimum throughput and is more energy-efficient in harsh transmission condition than other transmission protocols in the literature.

Resource Allocation Strategy for D2D-Assisted Edge Computing System With Hybrid Energy Harvesting

Due to the limited battery capacity and computing capability of mobile users, the resource allocation strategy in device-to-device (D2D)-assisted edge computing system with hybrid energy harvesting is investigated in this paper. By employing magnetic induction-based wireless reverse charging technology, mobile user can supplement extra energy from nearby users when the energy harvested from ambient radio frequency sources is about to be exhausted. Moreover, mobile user can not only perform local computation, but also offload computing tasks to nearby users for auxiliary computation through D2D communication links or mobile edge computing (MEC) server under base station (BS) for edge computation. Due to the limited computing resources of MEC server, when the computing capability of the MEC server reaches the maximum value, an adjacent user under another nearby BS can be considered as a relay node. The computing tasks of the remaining users under the previous BS can be transferred to the MEC server with sufficient resources under another nearby BS by establishing D2D relay links. The objective of the resource allocation strategy is to maximize the energy efficiency under the constraints of computation delay and energy harvesting. The resource allocation problem is formulated as a mixed-integer nonlinear programming problem, which is not easy to obtain the optimal solution at low computational complexity. A suboptimal solution is obtained by adopting the quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results show that the performance of the proposed strategy is superior to other benchmark strategies, and QPSO algorithm can achieve higher energy efficiency than the standard particle swarm optimization algorithm.

Research on primary school Chinese character recommendation algorithm based on collaborative filtering

Collaborative filtering is to calculate the similarity between users by taking all the preferences of a user as a reference value. After finding adjacent users, the information mining of relevant recommendations can be realized through similar behavior history. This paper proposes a Chinese character recommendation algorithm based on user collaborative filtering technology by digging deeply into the recorded information of students’ Chinese character learning.

A hybrid technique for joint PAPR reduction and sidelobe suppression in NC-OFDM based cognitive radio systems

Non-Contiguous Orthogonal Frequency Division Multiplexing (NC-OFDM) based Cognitive Radio (CR) system automatically identifies available frequency gaps in spectrum and utilize them to meet the requirement of higher data rates in 5G technology. It allocates some unauthorized users to access those frequency gaps without causing interference to the adjacent users. Besides the advantages of NC-OFDM based CR system, it suffers from two main drawbacks which are high Peak-to-Average Power Ratio (PAPR) and large spectrum sidelobe power. In order to overcome these drawbacks, we propose a hybrid technique that jointly reduces PAPR and sidelobe power. This technique involves a two level or modified clipping scheme for PAPR reduction. For sidelobe power suppression, signal cancellation symbols are generated by calculating Error Vector Magnitude (EVM) that are added to the original data bits. Here, the problem of sidelobe suppression can be formulated as a quadratically constrained quadratic program (QCQP), and the optimal solution can be obtained by the standard interior-point method. By combining two techniques we can solve these two issues in NC-OFDM based CR system simultaneously. From the simulation results, it is inferred that our proposed method outperforms interms of both PAPR, sidelobe power suppression over other existing methods and also improves the BER performance of the system.

An integrated approach of the analytic network process and fuzzy model mapping of evaluation of urban vulnerability against earthquake

The earthquake is one of the most devastating natural hazards that most of the Iranian cities. District 9 of Tehran will be vulnerable due to old residential texture, proximity to North Tehran fault and industrial land use in case of earthquake. Therefore, reducing the vulnerability to this natural disaster is one of the main goals of this study. In the present research, to evaluate vulnerability due to earthquake hazard, 10 indices are studied as effective factors in cities buildings vulnerability. These factors are selected on the basis of indices in previous studies. Indices involving materials type, type of facade building density, the age of building, the number of floors, ground area of buildings, compatibility of adjacent users, distance from the fault, geology formation and width of passageways are analyzed by using computational method of ANP scored by experts. The criteria weight is applied in effective layers in vulnerability. At last, by integrating the layers in GIS environments, general vulnerability map is extracted in the area. In order to evaluate the buildings vulnerability against earthquake, data obtained and from ANP analysis were made fuzzy. Finally, vulnerability map in district 9 of Tehran against earthquake was extracted. The research results show that if earthquake occurs, in district 9 of Tehran, 19.46% and 10.69% of buildings respectively have trivial and low vulnerability, while 50.29 percent of buildings have medium vulnerability. Also, 17.62% and 1.91% of building have high and very high vulnerability respectively, or they are completely damaged.

A Spatially Consistent MIMO Channel Model With Adjustable K Factor

In the area of research on massive multiple-input multiple-output (MIMO), two assumptions on the wireless channel dominate channel modeling. Either, a rich scattering environment is assumed and the channel is modeled as i.i.d. Rayleigh fading, or, a line of sight (LOS) channel is assumed, enabling geometric channel modeling under a farfield assumption. However, either of these assumptions represents an extreme case that is unlikely to be observed in practice. While there is a variety of MIMO channel models in literature, most of them, and even very popular geometry based stochastic channel models, are not spatially consistent. This is especially problematic for technologies in which channel correlation of adjacent users is an important factor, such as massive MIMO. In this work, we introduce a simple but spatially consistent MIMO channel model based on multiple scattering theory. Our proposed channel model allows to adjust the Rician $K$ factor by controlling the number and strength of scattering elements. This allows to perform spatially consistent simulations of wireless communications systems for a large range of scattering environments in between an i.i.d. Rayleigh fading assumption and pure LOS channels. A statistical analysis in terms of the Rician $K$ factor for the introduced model is provided and verified by simulations. By comparison to other channel models, we show that non spatially consistent channel models lead to an underestimation of inter-user correlation and therefore to an overestimation of achievable sum rate.

Research of Compatibility and Systemity of Land Management Terminology

The analysis results of common terms in land management and their interpretations in normative-legal acts of Ukraine are presented. It have been established collisions and significant differences in the wording of even such common terms as a land parcel, a boundary of land parcel, a size of land parcel, etc. The purpose of the study is to identify collisions and contradictions of some key terms used in land surveying and geodetic works, as well as development of concrete proposals for improving and perfection the relevant terms. For this purpose, the following tasks were set and solved: analysis of regulatory legal acts of Ukraine concerning the conceptual apparatus of land management, systematization and generalization of substantiation results of the relevant terms correction need, development of proposals taking into account works content on land management and geodetic works for information provision of the State Land Cadastre. In the course of the study, the comparative method of analysis and the systematization of inconsistencies in the current normative legal acts of Ukraine regarding the terms and their interpretation are used. During the proposals development, known approaches "from general to partial" and vice versa were used. The established disadvantages do not allow unambiguously interpreting and performing such important work as geodetic setting or restoration of the land parcels boundaries, resolution of land disputes between adjacent users of land parcels. Completed summaries of content and wording of the relevant terms justify a need for correction of the terms and additions to their definitions. It was set the features of each term. As a result of integrated approach to solution of the existing problem and systematization of established differences, the general wording and detailed specification of the analyzed terms interpretations are proposed. Knowledge of technological stages for geodetic and land management works provided an opportunity to improve the geodetic information support of the State Land Cadastre. The proposals for correction of the studied terms eliminate existing contradictions, provide an unambiguous interpretation of land management terminology, which leads to the exact execution of geodetic and land management works, and, accordingly, obtaining reliable information for the State Land Cadastre.

A Personalized Hybrid Recommendation Algorithm for Location-Based Service on Smart Campus

In order to provide location-based service for teachers and students, this paper proposes a personalized hybrid recommendation algorithm (GTRA) based on user’s geographical locations and tags. First, by analyzing the user's historical stay points and the length of stay time in different feature areas, the user's geographical adjacent user set is identified, which well solves scoring matrix sparse problem; then, we propose a tag growth strategy in which club administrators participate to give an effective solution to the problem of cold start; Finally, personalized push service was offered according to the user’s identity and the current location. Through the construction of personalized recommendation system and execution of the experiment, the algorithm proposed in this paper is superior to the traditional collaborative filtering algorithm, and it is suitable for campus application scenarios, which has some reference to the construction of smart campus in which personalized services for teachers and students are emphasized.

Game theory-based device-to-device network access algorithm for heterogeneous networks

The device-to-device (D2D) communication, as a high-speed communication from device to device, has attracted large researchers’ attention recently. Namely, by using the D2D communication in the cellular networks, mobile users can transmit data to the adjacent users at a faster rate. As a result, a large part of cellular data traffic can be diverted, reducing the overhead of data transmission from the base station and saving the energy as well as improving the spectrum efficiency. However, in the heterogeneous networks where cellular and D2D communication coexists when the D2D devices are connected to the network, the network performance is not only affected by the slow and fast fading factors, but also by the access network selection strategy and network switching factors. With the aim to improve the network performance, this paper proposes a game theory-based D2D network access algorithm for heterogeneous networks. The proposed algorithm can adapt to the dynamic changes of the network environment while avoiding unnecessary switching by considering the properties of the user equipment and network utilization, and thus improving the network throughput and reducing the delay. The experimental results demonstrate that the proposed algorithm has better performance than the flow algorithm.

User-centric content sharing via cache-enabled device-to-device communication

Cache-enabled device-to-device (D2D) communication has emerged as a promising paradigm for caching popular contents at user devices and using D2D communication among adjacent users to relieve cellular traffic overload. To maximize traffic offloading, optimal content-selection for caching and helper-receiver matching are two key concerns. In this paper, we deal with the two key issues by leveraging user characteristics, which are crucial to D2D content sharing but often omitted by existing work in this field. We first propose a comprehensive model that profiles user characteristics including user preference, user activity degree, and user relationship. To provide an accurate estimation of user relationship, a new metric named expected correlation coefficient (ECC) is further proposed, which can reveal the potential offloading opportunities behind the contacts between users. We then design a novel user-centric caching policy that integrates the above-mentioned models into content caching policy to optimize the caching efficiency. Furthermore, we propose an online learning algorithm by leveraging the Combinatorial Multi-Armed Bandits (CMAB) method to achieve efficient helper-receiver matching among users. Experiments based on real-world traces and content requesting records were conducted. The results demonstrate that when compared with existing algorithms, our proposed algorithm improves the offloading ratio by nearly 10% and the system utility approximately by 20%.