Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd the perception of the visual world to read.

The Perception of the Visual World. By James J. Gibson. U.S.A.: Houghton Mifflin Company, 1950 (George Allen & Unwin, Ltd., London). Price 35s.

In this paper, we present new collision search attacks on the hash function SHA-1. We show that collisions of SHA-1 can be found with complexity less than 269 hash operations. This is the first attack on the full 80-step SHA-1 with complexity less than the 280 theoretical bound.

/pdf/finding-collisions-in-the-full-sha-1-ptij2r5weq.pdf

Finding collisions in the full SHA-1

In this paper, we present new techniques for collision search in the hash function SHA-0. Using the new techniques, we can find collisions of the full 80-step SHA-0 with complexity less than 239 hash operations.

/pdf/efficient-collision-search-attacks-on-sha-0-4lhhlgq5lq.pdf

Efficient collision search attacks on SHA-0

Executive Summary Skein is a new family of cryptographic hash functions. Its design combines speed, security, simplicity , and a great deal of flexibility in a modular package that is easy to analyze. Skein is fast. Skein-512—our primary proposal—hashes data at 6.1 clock cycles per byte on a 64-bit CPU. This means that on a 3.1 GHz x64 Core 2 Duo CPU, Skein hashes data at 500 MBytes/second per core—almost twice as fast as SHA-512 and three times faster than SHA-256. An optional hash-tree mode speeds up parallelizable implementations even more. Skein is fast for short messages, too; Skein-512 hashes short messages in about 1000 clock cycles. Skein is secure. Its conservative design is based on the Threefish block cipher. The current best attack on the tweaked Threefish-512 is on 35 of 72 rounds, for a safety factor of just over 2.0. For comparison, at a similar stage in the standardization process, the AES encryption algorithm had an attack on 6 of 10 rounds, for a safety factor of only 1.7. Additionally, Skein has a number of provably secure properties, greatly increasing confidence in the algorithm. Skein is simple. Using only three primitive operations, the Skein compression function can be easily understood and remembered. The rest of the algorithm is a straightforward iteration of this function. Skein is flexible. Skein is defined for three different internal state sizes—256 bits, 512 bits, and 1024 bits—and any output size. This allows Skein to be a drop-in replacement for the entire SHA family of hash functions. A completely optional and extendable argument system makes Skein an efficient tool to use for a very large number of functions: PRNG, stream cipher, key derivation function, authentication without the overhead of HMAC, and personalization capability. All these features can be implemented with very low overhead. Together with the Threefish large-block cipher at Skein's core, this design provides a full set of symmetric cryptographic primitives suitable for most modern applications. Skein is efficient on a variety of platforms, both hardware and software. Skein-512 can be implemented in about 200 bytes of state. Small devices, such as 8-bit smart cards, can implement Skein-256 using about 100 bytes of memory. Larger devices can implement the larger versions of Skein to achieve faster speeds. Skein was designed by a team of highly experienced cryptographic experts from academia and industry , with expertise in cryptography, security analysis, software, chip design, and …

The Skein Hash Function Family

In (Micciancio, FOCS 2002), it was proved that solving the generalized compact knapsack problem on the average is as hard as solving certain worst-case problems for cyclic lattices. This result immediately yielded very efficient one-way functions whose security was based on worst-case hardness assumptions. In this work, we show that, while the function proposed by Micciancio is not collision resistant, it can be easily modified to achieve collision resistance under essentially the same complexity assumptions on cyclic lattices. Our modified function is obtained as a special case of a more general result, which yields efficient collision-resistant hash functions based on the worst-case hardness of various new problems. These include new problems from algebraic number theory as well as classic lattice problems (e.g., the shortest vector problem) over ideal lattices, a class of lattices that includes cyclic lattices as a special case.

Generalized Compact Knapsacks Are Collision Resistant

MD4 is a hash function developed by Rivest in 1990 It serves as the basis for most of the dedicated hash functions such as MD5, SHAx, RIPEMD, and HAVAL In 1996, Dobbertin showed how to find collisions of MD4 with complexity equivalent to 220 MD4 hash computations In this paper, we present a new attack on MD4 which can find a collision with probability 2−2 to 2−6, and the complexity of finding a collision doesn't exceed 28 MD4 hash operations Built upon the collision search attack, we present a chosen-message pre-image attack on MD4 with complexity below 28 Furthermore, we show that for a weak message, we can find another message that produces the same hash value The complexity is only a single MD4 computation, and a random message is a weak message with probability 2−122

The attack on MD4 can be directly applied to RIPEMD which has two parallel copies of MD4, and the complexity of finding a collision is about 218 RIPEMD hash operations

/pdf/cryptanalysis-of-the-hash-functions-md4-and-ripemd-5b3o3tju8p.pdf

Cryptanalysis of the hash functions MD4 and RIPEMD

Detection of road or lane is indispensable for the environmental perception of advanced driver assistance systems. It has been an active field of research with a wide application prospect. However, due to the complex illumination and interferences, such as vehicles and shadows in the real driving environment, lane detection is still a challenging task today. To address these issues, a robust method for road segmentation and lane detection based on a normal map is proposed. The first step of this approach is to generate the normal map by using the depth information and then extract a segmented road pavement without vehicles and buildings based on the normal map. Second, we improve an adaptive threshold segmentation method and denoising operations to enhance the lane markings. Third, the combination of Hough transform and vanishing point makes it more accurate to determine the starting points of host lanes, and then the lanes in the following image sequence can be detected in the adaptive region of interest. Compared with the state-of-the-art methods, the experimental results on the data sets in two countries demonstrate that our approach produces more credible performance under various light conditions or dense traffic.

Robust Lane Detection for Complicated Road Environment Based on Normal Map

Summary

Short-term traffic flow prediction in urban area remains a difficult yet important problem in intelligent transportation systems. Current spatio-temporal-based urban traffic flow prediction techniques trend aims to discover the relationship between adjacent upstream and downstream road segments using specific models, while in this paper, we advocate to exploit the spatial and temporal information from all available road segments in a partial road network. However, the available traffic states can be high dimensional for high-density road networks. Therefore, we propose a spatio-temporal variable selection-based support vector regression (VS-SVR) model fed with the high-dimensional traffic data collected from all available road segments. Our prediction model can be presented as a two-stage framework. In the first stage, we employ the multivariate adaptive regression splines model to select a set of predictors most related to the target one from the high-dimensional spatio-temporal variables, and different weights are assigned to the selected predictors. In the second stage, the kernel learning method, support vector regression, is trained on the weighted variables. The experimental results on the real-world traffic volume collected from a sub-area of Shanghai, China, demonstrate that the proposed spatio-temporal VS-SVR model outperforms the state-of-the-art. Copyright © 2015 John Wiley & Sons, Ltd.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/atr.1356

Urban traffic flow prediction: a spatio-temporal variable selection-based approach

In this paper, we describe the design of a mosaicing technique for images from a microscope system with automatically controlled object stage and image capture unit. Due to the limited field of view in microscope imagery, larger objects are split up into many adjacent, but slightly overlapping frames. In many fields, such as medicine or biology, it is vastly beneficial that these image patches are recomposed to a single (panoramic) image. We propose a feature matching and registration method based on SURF (Speeded-Up Robust Features). This method is most accurate for microscopy images, which usually have repetitive, blob-like structures. Further steps in our algorithm are estimation of transformation parameters for image warping and blending for elimination of color and luminance differences between images. For feature matching, we propose a new method of dividing descriptor windows. This increases matching speed considerably. The experimental results provided demonstrate the performance of our method.

Mosaicing of microscope images based on SURF

Disparity map generation is a significant component of vision-based driver assistance systems. This paper describes an efficient implementation of a belief propagation algorithm on a graphics card (GPU) using CUDA (Compute Uniform Device Architecture) that can be used to speed up stereo image processing by between 30 and 250 times. For evaluation purposes, different kinds of images have been used: reference images from the Middlebury stereo website, and real-world stereo sequences, self-recorded with the research vehicle of the .enpeda.. project at The University of Auckland. This paper provides implementation details, primarily concerned with the inequality constraints, involving the threads and shared memory, required for efficient programming on a GPU.

Hui Chen

Papers

Cryptanalysis of the hash functions MD4 and RIPEMD

Robust Lane Detection for Complicated Road Environment Based on Normal Map

Urban traffic flow prediction: a spatio-temporal variable selection-based approach

Mosaicing of microscope images based on SURF

Belief Propagation Implementation Using CUDA on an NVIDIA GTX 280