Redundancy Elimination Research Articles

BELIEF: A distance-based redundancy-proof feature selection method for Big Data

With the advent of Big Data era, data reduction methods are in highly demand given their ability to simplify huge data, and ease complex learning processes. Concretely, algorithms able to select relevant dimensions from a set of millions are of huge importance. Although effective, these techniques also suffer from the “scalability” curse when they are brought into tackle large-scale problems.In this paper, we propose a distributed feature weighting algorithm which precisely estimates feature importance in large datasets using the well-know algorithm RELIEF in small problems. Our solution, called BELIEF, incorporates a novel redundancy elimination measure that generates similar schemes to those based on entropy, but at a much lower time cost. Furthermore, BELIEF provides a smooth scale-up when more instances are required to increase precision in estimations.Empirical tests performed on our method illustrate the estimation ability of BELIEF in manifold huge sets – both in number of features and instances, as well as its reduced runtime cost as compared to other state-of-the-art methods.

Deduplication-Oriented Mutual-Assisted Cooperative Video Upload for Mobile Crowd Sensing

Deduplication (redundancy elimination) and cooperative video delivery are two effective ways to save the bandwidth and energy consumption and ensure video collection in damaged networks. However, deduplication in mobile crowd sensing (MSC) is primarily performed on texts and images. Furthermore, most of deduplication technologies require global information and are separated from video routing. To solve such problems, this paper propose a cooperative upload method for sensing videos, which performs the local video deduplication without excessive comparison and feature exchange. Also, we combine the content-aware deduplication with the dynamic relay selection to avoid the propagation of redundant items caused by the content-free video routing. Besides, we integrate a novel mutual-assisted mechanism into our method to motivate relay cooperation and load balance. We formulate the deduplication-supported cooperative video upload as a multi-stage decision problem. To solve the uncertainty of destinations in the decision problem, we develop a stepwise Mutual-Assisted Video Upload Algorithm (MAVU) to schedule video chunks and remove duplicates. Extensive experiments are conducted to compare MAVU with the existing algorithms. The numerical results validate that our MAVU has advantages over the other algorithms in collected video size and upload latency

An Efficient WSN Based Solution for Border Surveillance

Since a long time, the integrity of physical borders is seen as a challenging concern. Indeed, governments have to facilitate travelling and trade so that economies continue to grow while preventing the entry of dangerous entities. To this aim, several conventional techniques were enforced to secure borders in the recent past. However, due to the immensity of the area to be monitored, such solutions require an intensive human involvement and high maintenance costs. This advocated to use new technologies, such as Wireless Sensor Networks (WSN), to reduce the costs and improve the efficiency of the border surveillance system. Although using and combining these technologies has been already addressed in different existing solutions, it still some key points to be considered like energy efficiency, load balancing, and redundancy elimination. In this paper, we first propose a multilayer hybrid architecture based on cameras, scalar sensors, radars, and UAVs to design a border surveillance system. Then, a detailed deployment strategy is discussed. Finally, an activation scheduling strategy based on load balancing and energy saving is addressed. The simulation results show that our solution could not only detect the intrusion in border areas, but also outperforms the other solutions by managing efficiently the network and extending its lifetime.

Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms

Due to the limited battery energy of underwater wireless sensor nodes and the difficulty in replacing or recharging the battery underwater, it is of great significance to improve the energy efficiency of underwater wireless sensor networks (UWSNs). We propose a novel energy-efficient clustering routing protocol based on data fusion and genetic algorithms (GAs) for UWSNs. In the clustering routing protocol, the cluster head node (CHN) gathers the data from cluster member nodes (CMNs), aggregates the data through an improved back propagation neural network (BPNN), and transmits the aggregated data to a sink node (SN) through a multi-hop scheme. The effective multi-hop transmission path between the CHN and the SN is determined through the enhanced GA, thereby improving transmission efficiency and reducing energy consumption. This paper presents the GA based on a specific encoding scheme, a particular crossover operation, and an enhanced mutation operation. Additionally, the BPNN employed for data fusion is improved by adopting an optimized momentum method, which can reduce energy consumption through the elimination of data redundancy and the decrease of the amount of transferred data. Moreover, we introduce an optimized CHN selecting scheme considering residual energy and positions of nodes. The experiments demonstrate that our proposed protocol outperforms its competitors in terms of the energy expenditure, the network lifespan, and the packet loss rate.

Indirect Category Data Transfer Learning Algorithm using Regularization Discrimination.

To deal with a large amount of redundant data in the indirect category database and inefficient redundancy elimination of the existing methods, we proposed an indirect category data transfer learning algorithm based on regularization discrimination. First of all, we denoised indirect category data, calculated the objective function of distance between the source domain and the target domain, and established the transfer relationship between indirect category data. Second, we adopted the regularization discriminant technique to divide the transfer network structure of indirect category data into five modules, analyzed the effects and advantages of different modules, and constructed the transfer network structure of indirect category data. Finally, the indirect category data transfer was realized by the design of the indirect category data transfer learning algorithm. The results show that the proposed algorithm can effectively eliminate redundancy of indirect category data, the amplitude of fluctuation of indirect category data is small, the transfer time and energy consumption of the algorithm are low, and the accuracy is as high as about 90%, which indicates that the proposed algorithm is far superior to the traditional method and has high application value.

Reliable spatial and temporal data redundancy reduction approach for WSN

Data generated by sensors are inherently apt to spatial and temporal redundancy owing to the proximity of sensors that could sense the same environment or react to the same event. The massively generated data leads to reducing the life span of the sensors in particular, and the network in general. To minimize the effect of such generated data, we develop an approach to reducing the spatial and temporal data redundancy while maintaining the life of the sensor that results in prolonging the lifetime of the network with balancing data reliability. The proposed approach relies on two levels. The first level represents the end node, and it is responsible for reducing the temporal data redundancy and minimizing the data transmission using the Kalman filter for data estimation. The second level represents the sink or base station, which works in synchronization with the end nodes. This level is responsible for minimizing the spatial data redundancy based on two algorithms, namely Sink Level Grouping Algorithm (SLGA) and Sink Level Aggregation Algorithm (SLAA). The obtained results demonstrate that the proposed approach outperformed Prefix Frequency Filtering (PFF) and Redundancy Elimination Data Aggregation (REDA) algorithms in terms of spatial and temporal data redundancy and accuracy with acceptable results of energy consumption.

Facial Expression Recognition Using Frequency Neural Network.

Facial expression recognition has become a newly-emerging topic in recent decades, which has important value in the field of human-computer interaction. In this paper, we present a deep learning based approach, named frequency neural network (FreNet), for facial expression recognition. Different from convolutional neural network in spatial domain, FreNet inherits the advantages of processing image in frequency domain, such as efficient computation and spatial redundancy elimination. First, we propose the learnable multiplication kernel and construct multiple multiplication layers to learn features in frequency domain. Second, a summarization layer is proposed following multiplication layers to further yield high-level features. Third, based on the property of discrete cosine transform (DCT), we utilize multiplication layers and summarization layer to construct the Basic-FreNet, which can yield high-level features on the widely used DCT feature. Finally, to further achieve better performance on Basic-FreNet, we propose the Block-FreNet in which the weight-shared multiplication kernel is designed for feature learning and the block sub-sampling is designed for dimension reduction. The experimental results show that the Block-FreNet not only achieves superior performance, but also greatly reduces the computational cost. To our best knowledge, the proposed approach is the first attempt to fill in the blank of frequency based deep learning model for facial expression recognition.

REF: A Novel Forwarding Strategy in Wireless NDN

Named Data Networking (NDN), an emerging communication model in the content centric networks, has recently presented a solid framework for the future Internet. The NDN exploits content name instead of host name (IP address) and content caching. These features make NDN particularly efficient in networks with intermittent connectivity and dynamic topologies such as mobile ad hoc networks (MANETs). Due to the structure of NDN nodes, design of forwarding strategies has a vital impact on the network performance. In this paper, we propose a new forwarding strategy: redundancy elimination forwarding (REF). In REF, the NDN node structure and the operation of the data structures are modified. These modifications improve network performance in terms of throughput, overhead and resource requirements. REF strategy is simulated in different scenarios based on ndnSIM and the results demonstrate that it provides robust performance for various environments.

Surgical treatment of vulvar venous malformations without preoperative sclerotherapy: Outcomes of 18 patients

BackgroundOur aim was to evaluate the benefit of surgical resection of the venous malformations (VMs) of the external female genitalia. MethodsOver the period of 2009–2019, 18 consecutive females underwent surgical resection for vulvar VM. Evaluations included preoperative Doppler ultrasound, MRI, and pre-and postoperative photographic imaging. The main outcomes were: residual pain, cosmetic distortion, residual VM, and quality of life. ResultsOver a 10 year periods, 18 females, mean age 35 years (range 9–71) were included in this study. All patients were symptomatic: 16 had intermittent pain or discomfort, 1 had bleeding and 2 requested cosmetic treatment. Of these cases, there were 5 isolated vulvar VM, 12 associated VM: 3 of the clitoral hood, 3 troncular pelvic vein insufficiency and 12 of the lower limb. Eight patients had undergone previous procedures: 2 sclerotherapy treatments (1–3 sessions), 4 partial surgical resections.There were 18 single resections in the vulva (7 focal, 11 complete), 2 partial resections in clitoral hood and 2 had resection of a VM in the perirtoneovaginal canal at the same time. The mean follow-up was 42. 9 months (range 6–120). Two patients were lost to followup at 6 months. For all patients, elimination of pain and soft tissue redundancy was achieved. Two patients had persistent discomfort and 2 requested cosmetic treatment. ConclusionSurgical resection of vulvar VM can be the best approach with few postoperative complications, good functional and cosmetic results. Appropriate preoperative evaluation is required to identify isolated VM or VM associated with ovarian vein or internal iliac vein insufficiency requiring to be treated before surgery.

Semiring optimizations: dynamic elision of expressions with identity and absorbing elements

This paper describes a compiler optimization to eliminates dynamic occurrences of expressions in the format a ← a ⊕ b ⊗ c . The operation ⊕ must admit an identity element z , such that a ⊕ z = a . Also, z must be the absorbing element of ⊗, such that b ⊗ z = z ⊗ c = z . Semirings where ⊕ is the additive operator and ⊗ is the multiplicative operator meet this contract. This pattern is common in high-performance benchmarks—its canonical representative being the multiply-add operation a ← a + b × c . However, several other expressions involving arithmetic and logic operations satisfy the required algebra. We show that the runtime elimination of such assignments can be implemented in a performance-safe way via online profiling. The elimination of dynamic redundancies involving identity and absorbing elements in 35 programs of the LLVM test suite that present semiring patterns brings an average speedup of 1.19x (total optimized time over total unoptimized time) on top of clang -O3. When projected onto the entire test suite (259 programs) the optimization leads to a speedup of 1.025x. Once added onto clang, semiring optimizations approximates it to TACO, a specialized tensor compiler.

Protein Complexes Identification with Family-Wise Error Rate Control.

The detection of protein complexes from protein-protein interaction network is a fundamental issue in bioinformatics and systems biology. To solve this problem, numerous methods have been proposed from different angles in the past decades. However, the study on detecting statistically significant protein complexes still has not received much attention. Although there are a few methods available in the literature for identifying statistically significant protein complexes, none of these methods can provide a more strict control on the error rate of a protein complex in terms of family-wise error rate (FWER). In this paper, we propose a new detection method SSF that is capable of controlling the FWER of each reported protein complex. More precisely, we first present a p-value calculation method based on Fisher's exact test to quantify the association between each protein and a given candidate protein complex. Consequently, we describe the key modules of the SSF algorithm: a seed expansion procedure for significant protein complexes search and a set cover strategy for redundancy elimination. The experimental results on five benchmark data sets show that: (1) our method can achieve the highest precision; (2) it outperforms three competing methods in terms of normalized mutual information (NMI) and F1 score in most cases.

Genetic Algorithm-based Feature Selection Approach for Enhancing the Effectiveness of Similarity Searching in Ligand-based Virtual Screening

Background: In the last years, similarity searching has gained wide popularity as a method for performing Ligand-Based Virtual Screening (LBVS). This screening technique functions by making a comparison of the target compound’s features with that of each compound in the database of compounds. It is well known that none of the individual similarity measures could provide the best performances each time pertaining to an active compound structure, representing all types of activity classes. In the literature, we find several techniques and strategies that have been proposed to improve the overall effectiveness of ligand-based virtual screening approaches. Objective: In this work, our main objective is to propose a features selection approach based on genetic algorithm (FSGASS) to improve similarity searching pertaining to ligand-based virtual screening. Methods: Our contribution allows us to identify the most important and relevant characteristics of chemical compounds and to minimize their number in their representations. This will allow the reduction of features space, the elimination of redundancy, the reduction of training execution time, and the increase of the performance of the screening process. Results: The obtained results demonstrate superiority in the performance compared with these obtained with Tanimoto coefficient, which is considered as the most widely coefficient to quantify the similarity in the domain of LBVS. Conclusion: Our results show that significant improvements can be obtained by using molecular similarity research methods at the basis of features selection.

A task‐based approach to parallel parametric linear programming solving, and application to polyhedral computations

SummaryParametric linear programming is a central operation for polyhedral computations, as well as in certain control applications. Here, we propose a task‐based scheme for parallelizing it, with quasi‐linear speedup over large problems. This type of parallel applications is challenging, because several tasks might be computing the same region. In this article, we are presenting the algorithm itself with a parallel redundancy elimination algorithm, and conducting a thorough performance analysis.

Resilient Financial Systems Can Soften the Next Global Financial Crisis

Is there a way to soften the impact of another financial crisis? As national governments and global institutions grapple with the coronavirus pandemic, popular discussion has turned to how countries might overcome the social, economic, and financial disruption posed by the virus and its multi-system fallout. One lesson learned from the Global Financial Crisis of 2008 is that a drive for efficiency and the elimination of redundancy characterized the global financial system of the mid-2000s, and left much of the world exposed to systemic disruption. An overemphasis upon protecting or hardening core systems of governance and finance may not adequately protect against a range of disruptions in an uncertain future. Instead, resilience-thinking is needed to better position national and global financial institutions towards a recovery-oriented path. We describe how resilience-based policy can help lessen the long-term impacts of financial disruption posed by unpredictable events such as a pandemic.

A Hitchhiker\u2019s guide to the potato wart disease resistance galaxy

Key messageTwo novel major effect loci (Sen4 and Sen5) and several minor effect QTLs for potato wart disease resistance have been mapped. The importance of minor effect loci to bring full resistance to wart disease was investigated. Using the newly identified and known wart disease resistances, a panel of potato breeding germplasm and Solanum wild species was screened. This provided a state-of-the-art “hitch-hikers-guide” of complementary wart disease resistance sources.Potato wart disease, caused by the obligate biotrophic soil-born fungus Synchytrium endobioticum, is the most important quarantine disease of potato. Because of its huge impact on yield, the lack of chemical control and the formation of resting spores with long viability, breeding for resistant varieties combined with strict quarantine measures are the only way to efficiently and durably manage the disease. In this study, we set out to make an inventory of the different resistance sources. Using a Genome-Wide Association Study (GWAS) in the potato breeding genepool, we identified Sen4, associated with pathotypes 2, 6 and 18 resistance. Associated SNPs mapped to the south arm of chromosome 12 and were validated to be linked to resistance in one full-sib population. Also, a bulked segregant analysis combined with a Comparative Subsequence Sets Analysis (CoSSA) resulted in the identification of Sen5, associated with pathotypes 2, 6 and 18 resistance, on the south arm of chromosome 5. In addition to these two major effect loci, the GWAS and CoSSA allowed the identification of several quantitative trait loci necessary to bring full resistance to certain pathotypes. Panels of varieties and Solanum accessions were screened for the presence of Sen1, Sen2, Sen3, Sen4 and Sen5. Combined with pedigree analysis, we could trace back some of these genes to the ancestral resistance donors. This analysis revealed complementary resistance sources and allows elimination of redundancy in wart resistance breeding programs.

Video compression using frame redundancy elimination and discrete cosine transform coefficient reduction

The need of human beings for better social media applications has increased tremendously. This increase has necessitated the need for a digital system with a larger storage capacity and more processing power. However, an increase in multimedia content size reduces the overall processing performance. This occurs because the process of storing and retrieving large files affects the execution time. Therefore, it is extremely important to reduce the multimedia content size. This reduction can be achieved by image and video compression. There are two types of image or video compression: lossy and lossless. In the latter compression, the decompressed image is an exact copy of the original image, while in the former compression, the original and the decompressed image differ from each other. Lossless compression is needed when every pixel matters. This can be found in autoimage processing applications. On the other hand, lossy compression is used in applications that are based on human visual system perception. In these applications, not every single pixel is important; rather, the overall image quality is important. Many video compression algorithms have been proposed. However, the balance between compression rate and video quality still needs further investigation. The algorithm developed in this research focuses on this balance. The proposed algorithm exhibits diversity of compression stages used for each type of information such as elimination of redundant and semi redundant frames, elimination by manipulating consecutive XORed frames, reducing the discrete cosine transform coefficients based on the wanted accuracy and compression ratio. Neural network is used to further reduce the frame size. The proposed method is a lossy compression type, but it can reach the near-lossless type in terms of image quality and compression ratio with comparable execution time.

Video Summarization using Keyframe Extraction Methods

Video summarization plays an important role in too many fields, such as video indexing, video browsing, video compression, video analyzing and so on. One of the fundamental units in the video structure analysis is the keyframe extraction, Keyframe provides meaningful frames from the video. The keyframe consists of the meaningful frame from the videos which help for video summarization. In this proposed model, we presented an approach that is based on Convolutional Neural Network, keyframe extraction from videos and static video summarization. First, the video should be converted to frames. Then we perform redundancy elimination techniques to reduce the redundancy from frames. Then extract the keyframes from video by using the Convolutional Neural Network(CNN) model. From the extracted keyframe, we form a video summarization.

A novel data deduplication scheme for encrypted cloud databases

As the demand for data sharing and complex access-control policies continues to grow, traditional encryption mechanisms, which are generally established using a Public Key Infrastructure, face the problem of massive processing overheads and huge network bandwidth consumption. Attribute-Based Encryption (ABE) schemes have been proposed as a potential means of addressing these issues and have attracted great attention in recent years. Most previous studies on ABE focus on issues such as the revocation mechanism, multi-authority, the access structure design, and traceability. However, very few studies consider the storage efficiency problem and the present study proposes a novel data deduplication scheme based on ciphertext-policy ABE with convergent encryption and block-level data. The scheme can be deployed in third-party semi-trusted environments, and not only provides flexible, fine-grained access control over encrypted data, but also allows for the in-line elimination of redundancies in order to save cloud storage space. The experimental results show that the proposed scheme has an acceptable computational overhead and provides a promising solution for real-world data cloud storage and access scenarios.

Improving Restore Performance of Packed Datasets in Deduplication Systems via Reducing Persistent Fragmented Chunks

Data deduplication, though being efficient for redundancy elimination in storage systems, introduces chunk fragmentation which severely decreases restore performance. Rewriting algorithms are proposed to reduce the chunk fragmentation. Typically, the backup software aggregates files into larger “tar” type files for storage. We observe that, in tar type datasets, a large number of Persistent Fragmented Chunks (PFCs) are repeatedly rewritten by state-of-the-art rewriting algorithms in every backup, which severely impacts restore performance. We found that the existence of PFCs is due to the traditional strategy of storing PFCs along with other chunks in the containers to preserve the stream locality, rendering them always stored in the containers with low utilization. We propose DePFC to reduce PFCs. DePFC identifies and removes PFCs from the containers preserving the stream locality, and groups them together, to increase the utilization of containers holding them for the subsequent backup, thus preventing them from being rewritten again. We further propose an FC Buffer to avoid mistaken rewrites of PFCs and grouping PFCs that cause restore cache thrashing together. Experimental results demonstrate that DePFC improves restore performance of state-of-the-art rewriting algorithms by 44.24-89.42 percent, while attaining comparable deduplication efficiency, and FC Buffer further improves restore performance.

Benefícios e desvantagens da implementação do prontuário eletrônico do paciente para o serviço de saúde

Trata-se de uma pesquisa bibliográfica de abordagem qualitativa e caráter descritivo com o objetivo em identificar os benefícios e desvantagens encontradas na implementação dos Prontuários Eletrônico do Paciente no serviço de saúde. Como metodologia. Os dados foram coletados em Base de Dados Virtuais. Para tal utilizou-se a BVS, nas seguintes Bases de informações LILACS, MEDLINE, BDENF e pelo endereço eletrônico scholar.google.com.br, no período de maio de 2019. Como resultados, foram encontradas vantagens como agilidade no acesso de informações relacionadas ao paciente, uso simultâneo dos prontuários, legibilidade, eliminação da redundância de dados, organização sistematizada e ainda desvantagens como grandes investimentos de hardware, software e treinamento, resistência dos profissionais de saúde e lentidão e inoperância dos sistemas. Conclui-se que devem ser considerados, para implementação do PEP, a padronização dos prontuários, o estabelecimento dos fluxogramas de informação da equipe, o treinamento de todos envolvidos e o controle e avaliação dos resultados.