Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Modified One-bit Transform for Motion Estimation

Motion estimation using the one-bit transform (1BT) was proposed in [1] to achieve large computation reduction. However, it degrades the predicted image by almost 1 dB as compared with full search. In this paper, we propose a modification to the IBT by adding conditional local searches; Simulation results show that the proposed modification improves the peak signal-to-noise ratio (PSNR) significantly at the expense of slightly increased computational complexity. A variant of the proposed modification called M2SSFS is found to be particularly good for high quality, high bit rate video coding. In the MPEG-1 simulation, its PSNR is within 0.1 dB from that of full search at bit rates higher Wan I Mbit/s with a computation reduction factor of ten

Fast sola-based time scale modification using modified envelope matching

Time scale modification (TSM) of speech and audio is useful in many applications. Synchronized Overlap-and-Add (SOLA) is a time-domain TSM algorithm known to achieve good speech and audio quality. One problem of SOLA is that it requires a large amount of computation. In this paper, we propose a technique called Modified Envelope-Matching TSM (MEM-TSM) to simplify the computation. The MEM-TSM improves the previously proposed EM-TSM in quality and speed up factor. The proposed algorithm can reduce computation by 300 times with good perceptual quality of time-scaled speech and audio. Our experimental results show that the quality of MEM-TSM is almost the same as SOLA

Fast SOLA-based time scale modification using envelope matching

Time scale modification (TSM) of speech and audio signals is very useful in many applications such as MPEG-4 and fast/slow browsing of pre-recorded materials. Synchronized Overlap-and-Add (SOLA) is a time-domain TSM algorithm known to achieve good speech and audio quality. One problem of SOLA is that it requires a large amount of computation in the search of the best matching point between the analysis and synthesis frames. In this paper, we propose two algorithms, envelope-matching TSM (EM-TSM) and modified EM-TSM (MEM-TSM), to simplify the computation with negligible perceptual quality degradation. In EM-TSM, 1-bit sign information is used in the search to substitute the full-precision signal samples used in SOLA. Three additional computation reduction measures, namely simplified formulation, recursive computation and search-point reduction, are applied to achieve significant computation reduction. In MEM-TSM, we reduce the computation of EM-TSM further by introducing zero-crossing point reduction and predictive search skipping. We also improve the quality of the time-scaled signals by introducing multiple-candidate re-examination, and frame-size modification. Simulation results show that the proposed MEM-TSM can achieve computational reduction factors as large as 300 with very good perceptual quality of time-scaled speech and audio

Fast browsing of speech material for digital library and distance learning

In digital library and distance learning applications, one usually needs to search through lots of speech materials. While content-based retrieval techniques can help to identify possible matching items, the person would usually need to browse through the items quickly before making decisions on whether the items are useful or not. As a result, fast speech browsing techniques are highly desirable. In this paper; we discuss problems of fast playback of speech materials and overview some existing time scale modification (TSM) techniques. We propose some novel modifications of TSM to make it much mole effective in fast browsing of speech materials, especially those with irregular speech tempo. The proposed algorithm includes silent period removal, gain equalization and locally adaptive TSM. Simulation results show that the proposed algorithm can increase the intelligibility of the fast playbacked speech materials significantly

A blind watewmarking technique in JPEG compressed domain

We proposed a blind watermarking technique to embed the watermark in JPEG compressed domain. Low frequency DCT coefficients are extracted to form an M-dimensional vector. Watermarking is achieved by modifying this vector in order to point to the centroid of a particular cell. This cell is determined according to the extracted vector, private keys and the watermark. A dual-key system is used to reduce the chance of the removal of watermark. An iterative approach is used to prevent the removal of watermark by JPEG re-quantization. Experimental results show that the watermark can be detected when the watermarked image is further compressed using a larger scaling factor

A novel semi-private watermarking technique

A novel watermarking technique is proposed to embed a semi-watermark in digital images. Before embedding the watermark, a M-dimensional vector is extracted from the original data. Watermarking is achieved by modifying this vector to another particular vector according to the private keys and the watermark. A dual-key system is used to reduce the chance for the removal of watermark. The watermark can be detected without the original image. If the original image is available in the detection of watermark, the proposed technique is almost equivalent to the well known spread spectrum technique

Novel fast motion estimation for frame rate/structure conversion

Different multimedia applications and transmission channels require different resolution, frame rates/structures and bitrate, there is often a need to transcode the stored compressed video to suit the needs of these various applications. This paper is concerned about fast motion estimation for frame rate/structure conversion. In this paper. we proposed several novel I algorithms that exploit the correlation of the motion vectors in the original video and those in the transcoded video. We achieve a much higher quality than existing fast search algorithms with much lower complexity

Capacity for JPEG2000-to-JPEG2000 images watermarking

A data capacity estimation method is proposed for image watermarking. The data capacity is maximum number of bits that can be embedded in an image such that the image has no perceptual loss. It is assumed that the input is a JPEG2000 image file and after watermark embedding, the image is JPEG2000-cornpressed using the same quantization factors. This is called JPEG2000-to-JPEG2000 (J2K2J2K) watermarking. A Human Visual Systems (HVS) model is used to estimate the Just Noticeable Difference (JND) of each Discrete Wavelet Transform (DWT) coefficients