Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Science
Title: An application-specific protocol architecture for wireless microsensor networks
Abstract: Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. We develop and analyze low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality. LEACH includes a new, distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes, and techniques to enable distributed signal processing to save communication resources. Our results show that LEACH can improve system lifetime by an order of magnitude compared with general-purpose multihop approaches.
Extracted statement — Load balancing: Good
Research field: Science
Title: SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks
Abstract: We study the impact of heterogeneity of nodes, in terms of their energy, in wireless sensor networks that are hierarchically clustered. In these networks some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. We assume that a percentage of the population of sensor nodes is equipped with additional energy resources—this is a source of heterogeneity which may result from the initial setting or as the operation of the network evolves. We also assume that the sensors are randomly (uniformly) distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. We show that the behavior of such sensor networks becomes very unstable once the first node dies, especially in the presence of node heterogeneity. Classical clustering protocols assume that all the nodes are equipped with the same amount of energy and as a result, they can not take full advantage of the presence of node heterogeneity. We propose SEP, a heterogeneous-aware protocol to prolong the time interval before the death of the first node (we refer to as stability period), which is crucial for many applications where the feedback from the sensor network must be reliable. SEP is based on weighted election probabilities of each node to become cluster head according to the remaining energy in each node. We show by simulation that SEP always prolongs the stability period compared to (and that the average throughput is greater than) the one obtained using current clustering protocols. We conclude by studying the sensitivity of our SEP protocol to heterogeneity parameters capturing energy imbalance in the network. We found that SEP yields longer stability region for higher values of extra energy brought by more powerful nodes.
Extracted statement — Node type: heterogeneous
Research field: Science
Title: Instance level transfer learning for cross lingual opinion analysis
Abstract: This paper presents two instance-level transfer learning based algorithms for cross lingual opinion analysis by transferring useful translated opinion examples from other languages as the supplementary training data for improving the opinion classifier in target language. Starting from the union of small training data in target language and large translated examples in other languages, the Transfer AdaBoost algorithm is applied to iteratively reduce the influence of low quality translated examples. Alternatively, starting only from the training data in target language, the Transfer Self-training algorithm is designed to iteratively select high quality translated examples to enrich the training data set. These two algorithms are applied to sentence- and document-level cross lingual opinion analysis tasks, respectively. The evaluations show that these algorithms effectively improve the opinion analysis by exploiting small target language training data and large cross lingual training data.
Extracted statement — Computational cost: High
Research field: Science
Title: Adaptive Data Hiding in Edge Areas of Images With Spatial LSB Domain Systems
Abstract: This paper proposes a new adaptive least-significant- bit (LSB) steganographic method using pixel-value differencing (PVD) that provides a larger embedding capacity and imperceptible stegoimages. The method exploits the difference value of two consecutive pixels to estimate how many secret bits will be embedded into the two pixels. Pixels located in the edge areas are embedded by a k-bit LSB substitution method with a larger value of k than that of the pixels located in smooth areas. The range of difference values is adaptively divided into lower level, middle level, and higher level. For any pair of consecutive pixels, both pixels are embedded by the k-bit LSB substitution method. However, the value k is adaptive and is decided by the level which the difference value belongs to. In order to remain at the same level where the difference value of two consecutive pixels belongs, before and after embedding, a delicate readjusting phase is used. When compared to the past study of Wu et al.'s PVD and LSB replacement method, our experimental results show that our proposed approach provides both larger embedding capacity and higher image quality.
Extracted statement — Invisibility: High
Research field: Science
Title: Edge adaptive image steganography based on LSB matching revisited
Abstract: The least-significant-bit (LSB)-based approach is a popular type of steganographic algorithms in the spatial domain. However, we find that in most existing approaches, the choice of embedding positions within a cover image mainly depends on a pseudorandom number generator without considering the relationship between the image content itself and the size of the secret message. Thus the smooth/flat regions in the cover images will inevitably be contaminated after data hiding even at a low embedding rate, and this will lead to poor visual quality and low security based on our analysis and extensive experiments, especially for those images with many smooth regions. In this paper, we expand the LSB matching revisited image steganography and propose an edge adaptive scheme which can select the embedding regions according to the size of secret message and the difference between two consecutive pixels in the cover image. For lower embedding rates, only sharper edge regions are used while keeping the other smoother regions as they are. When the embedding rate increases, more edge regions can be released adaptively for data hiding by adjusting just a few parameters. The experimental results evaluated on 6000 natural images with three specific and four universal steganalytic algorithms show that the new scheme can enhance the security significantly compared with typical LSB-based approaches as well as their edge adaptive ones, such as pixel-value-differencing-based approaches, while preserving higher visual quality of stego images at the same time.
Extracted statement — Invisibility: High
Research field: Science
Title: Adaptive Data Hiding in Edge Areas of Images With Spatial LSB Domain Systems
Abstract: This paper proposes a new adaptive least-significant- bit (LSB) steganographic method using pixel-value differencing (PVD) that provides a larger embedding capacity and imperceptible stegoimages. The method exploits the difference value of two consecutive pixels to estimate how many secret bits will be embedded into the two pixels. Pixels located in the edge areas are embedded by a k-bit LSB substitution method with a larger value of k than that of the pixels located in smooth areas. The range of difference values is adaptively divided into lower level, middle level, and higher level. For any pair of consecutive pixels, both pixels are embedded by the k-bit LSB substitution method. However, the value k is adaptive and is decided by the level which the difference value belongs to. In order to remain at the same level where the difference value of two consecutive pixels belongs, before and after embedding, a delicate readjusting phase is used. When compared to the past study of Wu et al.'s PVD and LSB replacement method, our experimental results show that our proposed approach provides both larger embedding capacity and higher image quality.
Extracted statement — Payload
Capacity: High
Research field: Science
Title: Edge adaptive image steganography based on LSB matching revisited
Abstract: The least-significant-bit (LSB)-based approach is a popular type of steganographic algorithms in the spatial domain. However, we find that in most existing approaches, the choice of embedding positions within a cover image mainly depends on a pseudorandom number generator without considering the relationship between the image content itself and the size of the secret message. Thus the smooth/flat regions in the cover images will inevitably be contaminated after data hiding even at a low embedding rate, and this will lead to poor visual quality and low security based on our analysis and extensive experiments, especially for those images with many smooth regions. In this paper, we expand the LSB matching revisited image steganography and propose an edge adaptive scheme which can select the embedding regions according to the size of secret message and the difference between two consecutive pixels in the cover image. For lower embedding rates, only sharper edge regions are used while keeping the other smoother regions as they are. When the embedding rate increases, more edge regions can be released adaptively for data hiding by adjusting just a few parameters. The experimental results evaluated on 6000 natural images with three specific and four universal steganalytic algorithms show that the new scheme can enhance the security significantly compared with typical LSB-based approaches as well as their edge adaptive ones, such as pixel-value-differencing-based approaches, while preserving higher visual quality of stego images at the same time.
Extracted statement — Payload
Capacity: High
Research field: Science
Title: Reversible data embedding using a difference expansion
Abstract: Reversible data embedding has drawn lots of interest recently. Being reversible, the original digital content can be completely restored. We present a novel reversible data-embedding method for digital images. We explore the redundancy in digital images to achieve very high embedding capacity, and keep the distortion low.
Extracted statement — Robustness against
image
manipulation: High
Research field: Science
Title: Adaptive Data Hiding in Edge Areas of Images With Spatial LSB Domain Systems
Abstract: This paper proposes a new adaptive least-significant- bit (LSB) steganographic method using pixel-value differencing (PVD) that provides a larger embedding capacity and imperceptible stegoimages. The method exploits the difference value of two consecutive pixels to estimate how many secret bits will be embedded into the two pixels. Pixels located in the edge areas are embedded by a k-bit LSB substitution method with a larger value of k than that of the pixels located in smooth areas. The range of difference values is adaptively divided into lower level, middle level, and higher level. For any pair of consecutive pixels, both pixels are embedded by the k-bit LSB substitution method. However, the value k is adaptive and is decided by the level which the difference value belongs to. In order to remain at the same level where the difference value of two consecutive pixels belongs, before and after embedding, a delicate readjusting phase is used. When compared to the past study of Wu et al.'s PVD and LSB replacement method, our experimental results show that our proposed approach provides both larger embedding capacity and higher image quality.
Extracted statement — Robustness against
image
manipulation: High
Research field: Science
Title: Edge adaptive image steganography based on LSB matching revisited
Abstract: The least-significant-bit (LSB)-based approach is a popular type of steganographic algorithms in the spatial domain. However, we find that in most existing approaches, the choice of embedding positions within a cover image mainly depends on a pseudorandom number generator without considering the relationship between the image content itself and the size of the secret message. Thus the smooth/flat regions in the cover images will inevitably be contaminated after data hiding even at a low embedding rate, and this will lead to poor visual quality and low security based on our analysis and extensive experiments, especially for those images with many smooth regions. In this paper, we expand the LSB matching revisited image steganography and propose an edge adaptive scheme which can select the embedding regions according to the size of secret message and the difference between two consecutive pixels in the cover image. For lower embedding rates, only sharper edge regions are used while keeping the other smoother regions as they are. When the embedding rate increases, more edge regions can be released adaptively for data hiding by adjusting just a few parameters. The experimental results evaluated on 6000 natural images with three specific and four universal steganalytic algorithms show that the new scheme can enhance the security significantly compared with typical LSB-based approaches as well as their edge adaptive ones, such as pixel-value-differencing-based approaches, while preserving higher visual quality of stego images at the same time.
Extracted statement — Robustness against
image
manipulation: High
Research field: Science
Title: Reversible data embedding using a difference expansion
Abstract: Reversible data embedding has drawn lots of interest recently. Being reversible, the original digital content can be completely restored. We present a novel reversible data-embedding method for digital images. We explore the redundancy in digital images to achieve very high embedding capacity, and keep the distortion low.
Extracted statement — Robustness against
statistical attacks: High
Research field: Science
Title: Adaptive Data Hiding in Edge Areas of Images With Spatial LSB Domain Systems
Abstract: This paper proposes a new adaptive least-significant- bit (LSB) steganographic method using pixel-value differencing (PVD) that provides a larger embedding capacity and imperceptible stegoimages. The method exploits the difference value of two consecutive pixels to estimate how many secret bits will be embedded into the two pixels. Pixels located in the edge areas are embedded by a k-bit LSB substitution method with a larger value of k than that of the pixels located in smooth areas. The range of difference values is adaptively divided into lower level, middle level, and higher level. For any pair of consecutive pixels, both pixels are embedded by the k-bit LSB substitution method. However, the value k is adaptive and is decided by the level which the difference value belongs to. In order to remain at the same level where the difference value of two consecutive pixels belongs, before and after embedding, a delicate readjusting phase is used. When compared to the past study of Wu et al.'s PVD and LSB replacement method, our experimental results show that our proposed approach provides both larger embedding capacity and higher image quality.
Extracted statement — Robustness against
statistical attacks: High
Research field: Science
Title: Reversible image watermarking using interpolation technique
Abstract: In this paper presents a novel reversible watermarking scheme. The proposed scheme use an interpolation technique to generate residual values named as interpolation error. Additionally apply the additive expansion to these interpolation-errors, this project achieve a highly efficient reversible watermarking scheme which can guarantee high image quality without sacrificing embedding capacity. The experimental results show the proposed reversible scheme provides a higher capacity and achieves better image quality for watermarked images. The computational cost of the proposed scheme is small.
Extracted statement — Robustness against
statistical attacks: High
Research field: Science
Title: Edge adaptive image steganography based on LSB matching revisited
Abstract: The least-significant-bit (LSB)-based approach is a popular type of steganographic algorithms in the spatial domain. However, we find that in most existing approaches, the choice of embedding positions within a cover image mainly depends on a pseudorandom number generator without considering the relationship between the image content itself and the size of the secret message. Thus the smooth/flat regions in the cover images will inevitably be contaminated after data hiding even at a low embedding rate, and this will lead to poor visual quality and low security based on our analysis and extensive experiments, especially for those images with many smooth regions. In this paper, we expand the LSB matching revisited image steganography and propose an edge adaptive scheme which can select the embedding regions according to the size of secret message and the difference between two consecutive pixels in the cover image. For lower embedding rates, only sharper edge regions are used while keeping the other smoother regions as they are. When the embedding rate increases, more edge regions can be released adaptively for data hiding by adjusting just a few parameters. The experimental results evaluated on 6000 natural images with three specific and four universal steganalytic algorithms show that the new scheme can enhance the security significantly compared with typical LSB-based approaches as well as their edge adaptive ones, such as pixel-value-differencing-based approaches, while preserving higher visual quality of stego images at the same time.
Extracted statement — Tolerance to RS
Steganalysis: High
Research field: Science
Title: Edge adaptive image steganography based on LSB matching revisited
Abstract: The least-significant-bit (LSB)-based approach is a popular type of steganographic algorithms in the spatial domain. However, we find that in most existing approaches, the choice of embedding positions within a cover image mainly depends on a pseudorandom number generator without considering the relationship between the image content itself and the size of the secret message. Thus the smooth/flat regions in the cover images will inevitably be contaminated after data hiding even at a low embedding rate, and this will lead to poor visual quality and low security based on our analysis and extensive experiments, especially for those images with many smooth regions. In this paper, we expand the LSB matching revisited image steganography and propose an edge adaptive scheme which can select the embedding regions according to the size of secret message and the difference between two consecutive pixels in the cover image. For lower embedding rates, only sharper edge regions are used while keeping the other smoother regions as they are. When the embedding rate increases, more edge regions can be released adaptively for data hiding by adjusting just a few parameters. The experimental results evaluated on 6000 natural images with three specific and four universal steganalytic algorithms show that the new scheme can enhance the security significantly compared with typical LSB-based approaches as well as their edge adaptive ones, such as pixel-value-differencing-based approaches, while preserving higher visual quality of stego images at the same time.
Extracted statement — Utilization of edge
areas: High
Research field: Science
Title: Individual Skill Progression on a Virtual Reality Simulator for Shoulder Arthroscopy A 3-Year Follow-up Study
Abstract: Background Previous studies have demonstrated a correlation between surgical experience and performance on a virtual reality arthroscopy simulator but only provided single time point evaluations. Additional longitudinal studies are necessary to confirm the validity of virtual reality simulation before these teaching aids can be more fully recommended for surgical education. Hypothesis Subjects will show improved performance on simulator retesting several years after an initial baseline evaluation, commensurate with their advanced surgical experience. Study Design Controlled laboratory study. Methods After gaining further arthroscopic experience, 10 orthopaedic residents underwent retesting 3 years after initial evaluation on a Procedicus virtual reality arthroscopy simulator. Using a paired t test, simulator parameters were compared in each subject before and after additional arthroscopic experience. Subjects were evaluated for time to completion, number of probe collisions with the tissues, average probe velocity, and distance traveled with the tip of the simulated probe compared to an optimal computer-determined distance. In addition, to evaluate consistency of simulator performance, results were compared to historical controls of equal experience. Results Subjects improved significantly ( P &lt; .02 for all) in the 4 simulator parameters: completion time (−51 %), probe collisions (−29%), average velocity (+122%), and distance traveled (−;32%). With the exception of probe velocity, there were no significant differences between the performance of this group and that of a historical group with equal experience, indicating that groups with similar arthroscopic experience consistently demonstrate equivalent scores on the simulator. Conclusion Subjects significantly improved their performance on simulator retesting 3 years after initial evaluation. Additionally, across independent groups with equivalent surgical experience, similar performance can be expected on simulator parameters; thus it may eventually be possible to establish simulator benchmarks to indicate likely arthroscopic skill. Clinical Relevance These results further validate the use of surgical simulation as an important tool for the evaluation of surgical skills.
Extracted statement — Evaluator: Independent
Research field: Science
Title: Mining High Utility Itemsets in Large High Dimensional Data
Abstract: Existing algorithms for utility mining are inadequate on datasets with high dimensions or long patterns. This paper proposes a hybrid method, which is composed of a row enumeration algorithm (i.e., inter-transaction) and a column enumeration algorithm (i.e., two-phase), to discover high utility itemsets from two directions: Two-phase seeks short high utility itemsets from the bottom, while inter-transaction seeks long high utility itemsets from the top. In addition, optimization technique is adopted to improve the performance of computing the intersection of transactions. Experiments on synthetic data show that the hybrid method achieves high performance in large high dimensional datasets.
Extracted statement — Algorithm name: Inter-transaction