Intelligent In-Vehicle Safety System Based on Yolov5

In order to reduce the occurrence of traffic accidents and assist drivers to avoid dangerous driving. This paper presents a smart in-vehicle safety system that utilises the Yolov5 algorithm. Yolov5 algorithm is used to anticipate driver fatigue and distraction behaviours, and remind drivers to pay attention to safe driving in time. The system continuously splits the frames and analyses the frame content through the video feedback from the front camera, compared to the traditional machine learning, Yolov5’s mosaic data is enhanced, resulting in a batch size enhancement of 92.3%, and it also uses the Drop Block mechanism to prevent overfitting. The hardware of this system uses STM32 microcontroller and uses system DMA interrupt control and buzzer alarm device to warn about dangerous driving behaviour.

Semantics Analytics of Origin-Destination Flows from Crowd Sensed Big Data

Monitoring,understanding and predicting Origin-destination(OD)flows in a city is an important problem for city planning and human activity.Taxi-GPS traces,acted as one kind of typical crowd sensed data,it can be used to mine the semantics of OD flows.In this paper,we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in China.The spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD flows.Then based on a novel complex network model,a semantics mining method of OD flows is proposed through compounding Points of Interests(POI)network and public transport network to the OD flows network.The propose method would offer a novel way to predict the location characteristic and future traffic conditions accurately.

Sub-pixel edge detection method for miniature parts in microscopic field of view

In order to improve the edge detection precision of miniature parts in microscopic field of viewa sub-pixel edge detectionalgorithm combining non-orthogonal quadratic B-spline wavelet transform algorithm and Zernike moment algorithm is proposed.Non-orthogonal quadratic B-spline wavelet transform algorithm is adopted to get the pixel edge of miniature parts?andthe moment invariant of Zernike moment algorithm is used for refining the pixel edge to get sub-pixel edges.A real-time detectionsystem based on the proposed algorithm for miniature parts is established.The general system structure and operational principle are given,the real-time image acquisition and detection are completed,the results of edge detection are analyzed and the detection precision is evaluated.The results show that parts size can be0.01-10mm and the detection precision reaches0.01%-0.1%.Therefore,the edge of the miniature parts can be accurately identified and the detection precision can be improved to sub-pixel level,which meets the requirements of miniature parts precision detection.

Efficient and Provably Secure Multi-Recipient Signcryption from Bilinear Pairings

Signcryption is a cryptographic primitive that performs signature and encryption simultaneously, at lower computational costs and communication overheads than the signature-then- encryption approach. In this paper, we propose an efficient multi-recipient signcryption scheme based on the bilinear pairings, which broadcasts a message to multiple users in a secure and authenticated manner. We prove its semantic security and unforgeability under the Gap Diffie-Hellman problem assumption in the random oracle model. The proposed scheme is more efficient than re-signcrypting a message n times using a signcryption scheme in terms of computational costs and communication overheads.

An Improved Simple Power Attack against Camellia's Key Schedule

This paper presents an improved simple power attack against the key schedule of Camellia. While the original attack required an exact determination of the Hamming weight of intermediate data values based on power measurements, in this paper, two types of the simple power attack are presented and shown to be tolerant of errors that might occur in the Hamming weight determinations. In practical applications of the attack, such errors are likely to occur due to noise and distortion in the power measurements and their mapping to the Hamming weights of the data. To resist these attacks, the required design rationale of key schedules and several practical countermeasures are suggested.

New Weak Keys in RSA

The security of the RSA system with the prime pairs of some special form is investigated. A new special-purpose algorithm for factoring RSA numbers is proposed. The basic idea of the method is to factor RSA numbers by factoring a well-chosen quadratic polynomial with integral coefficients. When viewed as a general-purpose algorithm, the new algorithm has a high computational complexity. It is shown thai the RSA number n = pq can be easily factored if p and q have the special form of p = as＋b, q=cs＋d, where a, b, c, d are relatively small numbers. Such prime pairs （p, q） are the weak keys of RSA, so when we generate RSA modulus, we should avoid using such prime pairs （p, q）.

An Efficient Method against Side-Channel Attacks on ECC

Side-channel attacks （SCA） may exploit leakage information to break cryptosystems. In this paper we present a new SCA resistant Elliptic Curve scalar multiplication algorithm. The proposed algorithm, builds a sequence of bit-strings representing the scalar k, characterized by the fact that all bit-strings are different from zero; this property will ensure a uniform computation behavior for the algorithm, and thus will make it secure against simple power analysis attacks （SPA）. With other randomization techniques, the proposed countermeasures do not penalize the computation time. The proposed scheme is more efficient than MOEller＇s one, its cost being about 5% to 10% smaller than MOEller＇s one.

Approach of generating parallel programs from parallelized algorithm design strategies

Today, parallel programming is dominated by message passing libraries, such as message passing interface （MPI）. This article intends to simplify parallel programming by generating parallel programs from parallelized algorithm design strategies. It uses skeletons to abstract parallelized algorithm design strategies, as well as parallel architectures. Starting from problem specification, an abstract parallel abstract programming language＋ （Apla＋） program is generated from parallelized algorithm design strategies and problem-specific function definitions. By combining with parallel architectures, implicity of parallelism inside the parallelized algorithm design strategies is exploited. With implementation and transformation, C＋＋ and parallel virtual machine （CPPVM） parallel program is finally generated. Parallelized branch and bound （B＆B） algorithm design strategy and paraUelized divide and conquer （D ＆ C） algorithm design strategy are studied in this article as examples. And it also illustrates the approach with a case study.

Nonlinear Program Construction and Verification Method Based on Partition Recursion and Morgan's Refinement Rules

The traditional program refinement strategy cannot be refined to an executable program,and there are issues such as low verification reliability and automation.To solve the above problems,this paper proposes a nonlinear program construction and verification method based on partition recursion and Morgan’s refinement rules.First,we use recursive definition technique to characterize the initial specification.The specification is then transformed into GCL(Guarded Command Language)programs using loop invariant derivation and Morgan’s refinement rules.Furthermore,VCG(Verification Condition Generator)is used in the GCL program to generate the verification condition automatically.The Isabelle theorem prover then validates the GCL program’s correctness.Finally,the GCL code generates a C++executable program automatically via the conversion system.The effectiveness of this method is demonstrated using binary tree preorder traversal program construction and verification as an example.This method addresses the problem that the construction process’s loop invariant is difficult to obtain and the refinement process is insufficiently detailed.At the same time,the method improves verification process automation and reduces the manual verification workload.

Improved Hybrid Collaborative Fitering Algorithm Based on Spark Platform

An improved Hybrid Collaborative Filtering algorithm(H-CF)is proposed,addressing the issues of data sparsity,low recommendation accuracy,and poor scalability present in traditional collaborative filtering algorithms.The core of H-CF is a linear weighted hybrid algorithm based on the Latent Factor Model(LFM)and the Improved Item Clustering and Similarity Calculation Collaborative Filtering Algorithm(ITCSCF).To begin with,the items are clustered based on their attribute dimension,which accelerates the computation of the nearest neighbor set.Subsequently,H-CF enhances the formula for scoring similarity by penalizing popular items and optimizing unpopular items.This improvement enhances the rationality of scoring similarity and reduces the impact of data sparseness.Furthermore,a weighting function is employed to combine the various improved algorithms.The balance factor of the weighting function is dynamically adjusted to attain the optimal recommendation list.To address the real-time and scalability concerns,the algorithm leverages the Spark big data distributed cluster computing framework.Experiments were conducted using the public dataset Movie Lens,where the improved algorithm’s performance was compared against the algorithm before enhancement and the algorithm running on a single machine.The experimental results demonstrate that the improved algorithm outperforms in terms of data sparsity,recommendation personalization,accuracy,recall,and efficiency.

Program Construction Method for Sequential Statistics Class Algorithm Based on Bidirectional Scanning Induction

The program construction process is based on rigorous mathematical reasoning,which leads to a fully correct algorithmic program via step-by-step refinement of the program specifications.The existing program construction methods'refinement process is partly based on individual subjective speculation and analysis,which lacks a precise guidance method.Meanwhile,efficiency factors have usually been ignored in the construction process,and most of the constructed abstract programs cannot be run directly by machines.In order to solve these problems,a novel program construction method for the sequence statistical class algorithms based on bidirectional scan induction is proposed in this paper.The method takes into account the efficiency factor and thus improves the Morgan's refinement calculus.Furthermore,this paper validates the method's feasibility using an efficiency-sensitive sequential statistics class algorithm as a program construction example.The method proposed in this paper realizes the correctness construction process from program specifications to efficient executable programs.

Light bottle transformer based large scale point cloud classification

With the rapid development of computer vision,point clouds technique was widely used in practical applications,such as obstacle detection,roadside detection,smart city construction,etc.However,how to efficiently identify the large scale point clouds is still an open challenge.For relieving the large computation consumption and low accuracy problem in point cloud classification,a large scale point cloud classification framework based on light bottle transformer(light-Bot Net)is proposed.Firstly,the two-dimensional(2D)and three-dimensional(3D)feature values of large scale point cloud were extracted for constructing point cloud feature images,which employed the prior knowledge to normalize the point cloud features.Then,the feature images are input to the classification network,and the light-Bot Net network is applied for point cloud classification.It is an interesting attempt to combine the traditional image features with the transformer network.For proving the performance of the proposed method,the large scale point cloud benchmark Oakland 3D is utilized.In the experiments,the proposed method achieved 98.1%accuracy on the Oakland 3D dataset.Compared with the other methods,it can both reduce the memory consumption and improve the classification accuracy in large scale point cloud classification.

EXTRAPUSH FOR CONVEX SMOOTH DECENTRALIZED OPTIMIZATION OVER DIRECTED NETWORKS

In this note, we extend the algorithms Extra [13] and subgradient-push [I0] to a new algorithm ExtraPush for consensus optimization with convex differentiable objective functions over a directed network. When the stationary distribution of the network can be computed in advance, we propose a simplified algorithm called Normalized ExtraPush. Just like Extra, both ExtraPush and Normalized ExtraPush can iterate with a fixed step size. But unlike Extra, they can take a column-stochastic mixing matrix, which is not necessarily doubly stochastic. Therefore, they remove the undirected-network restriction of Extra. Subgradient-push, while also works for directed networks, is slower on the same type of problem because it must use a sequence of diminishing step sizes. We present preliminary analysis for ExtraPush under a bounded sequence assumption. For Normalized ExtraPush, we show that it naturally produces a bounded, linearly convergent sequence provided that the objective function is strongly convex. In our numerical experiments, ExtraPush and Normalized ExtraPush performed similarly well. They are significantly faster than subgradient-push, even when we hand-optimize the step sizes for the latter.

Floor positioning method indoors with smartphone’s barometer

This paper presents an indoor floor positioning method with the smartphone’s barometer for the purpose of solving the problem of low availability and high environmental dependence of the traditional floor positioning technology.First,an initial floor position algorithm with the“entering”detection algorithm has been obtained.Second,the user’s going upstairs or downstairs activities are identified by the characteristics of the air pressure fluctuation.Third,the moving distance in the vertical direction and the floor change during going upstairs or downstairs are estimated to obtain the accurate floor position.In order to solve the problem of the floor misjudgment from different mobile phone’s barometers,this paper calculates the pressure data from the different cell phones,and effectively reduce the errors of the air pressure estimating the elevation which is caused by the heterogeneity of the mobile phones.The experiment results show that the average correct rate of the floor identification is more than 85%for three types of the cell phones while reducing environmental dependence and improving availability.Further,this paper compares and analyzes the three common floor location methods–the WLAN Floor Location(WFL)method based on the fingerprint,the Neural Network Floor Location(NFL)methods,and the Magnetic Floor Location(MFL)method with our method.The experiment results achieve 94.2%correct rate of the floor identification with Huawei mate10 Pro mobile phone.

A Formal Method for Developing Algebraic and Numerical Algorithms

The development of algebraic and numerical algorithms is a kind of complicated creative work and it is difficult to guarantee the correctness of the algorithms. This paper introduces a systematic and unified formal development method of algebraic and numerical algorithms. The method implements the complete refinement process from abstract specifications to a concrete executable program. It uses the core idea of partition and recursion for formal derivation and combines the mathematical induction based on strict mathematical logic with Hoare axiom for correctness verification. This development method converts creative work into non-creative work as much as possible while ensuring the correctness of the algorithm, which can not only verify the correctness of the existing algebraic and numerical algorithms but also guide the development of efficient unknown algorithms for such problems. This paper takes the non-recursive implementation of the Extended Euclidean Algorithm and Horner's method as examples. Therefore, the effectiveness and feasibility of this method are further verified.

A Method to Deduce and Synthesize the Dafny Programs

We propose a systematic method to deduce and synthesize the Dafny programs.First,the specification of problem is described in strict mathematical language.Then,the derivation process uses program specification transformation technology to perform equivalent transformation.Furthermore,Dafny program is synthesized through the obtained recursive relationship and loop invariants.Finally,the functional correctness of Dafny program is automatically verified by Dafny verifier or online tool.Through this method,we deduce and synthesize Dafny programs for many typical problems such as the cube sum problem,the minimum(or maximum)contiguous subarray problems,several searching problems,several sorting problems,and so on.Due to space limitation,we only illustrate the development process of Dafny programs for two typical problems:the minimum contiguous subarray problem and the new local bubble sorting problem.It proves that our method can effectively improve the correctness and reliability of Dafny program developed.What’s more,we demonstrate the potential of the deductive synthesis method by developing a new local bubble Sorting program.

A Semantic-Aware Method for Polymorphic Signature Generation

It is difficult for security experts to generate polymorphic signatures by using traditional string mining and matching techniques.A semantic-aware method is presented to generate a kind of two-level signature that includes both polymorphic semantics and string patterns.It first analyzes the characteristics of polymorphic engines and categorizes the data flows into different clusters and then uses static data flow methods to extract invariable semantic instructions.And then,it combines traditional string methods to generate the signature.In comparison with other methods,experimental results show that it may effectively reduce false positives and false negatives.