Hybrid features for object detection in RGB-D scenes

Object detection is considered a hot research topic in applications of artificial intel-ligence and computer vision. Historically, object detection was widely used in var-ious fields like surveillance, fine-grained activities and robotics. All studies focus on improving accuracy for object detection using images, whether indoor or outdoor scenes. Therefore, this paper took a shot by improving the doable features extraction and proposing crossed sliding window approach using exiting classifiers for object de-tection. In this paper, the contribution includes two parts: First, improving local depth pattern feature along side SIFT and the second part explains a new technique presented by proposing crossed sliding window approach using two different types of images (colored and depth). Two types of features local depth patterns for detection (LDPD) and scale-invariant feature transform (SIFT) were merged as one feature vector. The RGB-D object dataset has been used and it consists of 300 different objects and in-cludes thousands of scenes. The proposed approach achieved high results comparing to other features or separated features that are used in this paper. All experiments and comparatives were applied on the same dataset for the same objective. Experimental results report a high accuracy in terms of detection rate, recall, precision and F1 scorein RGB-D scenes

Defining Trace Semantics for CSP-Agda

This article is based on the library CSP-Agda, which represents the process algebra CSP coinductively in the interactive theorem prover Agda. The intended application area of CSP-Agda is the proof of properties of safety critical systems (especially the railway domain). In CSP-Agda, CSP processes have been extended to monadic form, allowing the design of processes in a more modular way. In this article we extend the trace semantics of CSP to the monadic setting. We implement this semantics, together with the corresponding refinement and equality relation, formally in CSP-Agda. In order to demonstrate the proof capabilities of CSP-Agda, we prove in CSP-Agda selected algebraic laws of CSP based on the trace semantics. Because of the monadic settings, some adjustments need to be made to these laws. The examples covered in this article are the laws of refinement, commutativity of interleaving and parallel, and the monad laws for the monadic extension of CSP. All proofs and definitions have been type checked in Agda. Further proofs of algebraic laws will be available in the repository of CSP-Agda

Undecidability of Equality for Codata Types

International audienceDecidability of type checking for dependently typed languages usually requires a decidable equality on types. Since bisimilarity on (weakly final) coalgebras such as streams is undecidable, one cannot use it as the equality in type checking. Instead, languages based on dependent types with decidable type checking such as Coq or Agda use intensional equality for type checking. Two streams are definitionally equal if the underlying terms reduce to the same normal form, i.e. if the underlying programs are syntactically equivalent. For reasoning about equality of streams one introduces bisimilarity as a propositional rather than judgemental equality.In this paper we show that it is not possible to strengthen intensional equality in a decidable way while having the property that equality respects one step expansion, which means that a stream with head n and tail s is equal to $\mathrm {cons}(n,s)$ cons(n,s). This property, which would be very useful in type checking, would not necessarily imply that bisimilar streams are equal, and we prove that there exist equalities with this properties which do not coincide with bisimilarity. Whereas a proof that bisimilarity on streams is undecidable is straightforward, proving that respecting one step expansion makes equality undecidable is much more involved and relies on an inseparability result for sets of codes for Turing machines. We prove this theorem both for streams with primitive corecursion and with coiteration as introduction rule.Therefore, pattern matching on streams is, understood literally, not a valid principle, since it assumes that every stream is equal to a stream of the form $\mathrm {cons}(n,s)$ cons(n,s). We relate this problem to the subject reduction problem found when adding pattern matching on coalgebras to Coq and Agda. We discuss how this was solved in Agda by defining coalgebras by their elimination rule and replacing pattern matching on coalgebras by copattern matching, and how this relates to the approach in Agda which uses the type of delayed computations, i.e. the so called “musical notation” for codata types

A Novel Fuzzy Logic-Based Scheme for Malicious Node Eviction in a Vehicular Ad Hoc Network

Securing communication in vehicular ad hoc networks (VANETs) is hampered by numerous constraints, making it more difficult. First, traditional security schemes cannot be directly applied in VANET because they consider fixed topology. Second, VANET enables dynamic spectrum access where nodes constantly change frequencies due to their high degree of mobility, resulting in severe consequences on network performance. Third, an effective security scheme in VANET needs local and continual knowledge of nodes. Last, the presence of malicious nodes and their misbehaving activities impair the safety of the drivers since they might alter the content of the sent safety alerts. With these constraints in mind, this paper presents a unique security strategy that utilizes node behaviour during message exchange as a security metric to address these issues. Through the message alert exchange phase, node behaviour is measured through the fuzzy logic framework to generate a rank for each node called trust level (BL), which describes the node’s reliability in exchanging safety messages correctly. Moreover, all messages in VANET are encrypted using the existing cryptography techniques. The proposed scheme is developed to enhance communication security in VANET, minimize the effects of malicious nodes, and improve resource utilization in VANET. Evaluation of the proposed scheme shows that it improves the performance of VANET in terms of end-to-end delay, packet delivery ratio, and packet loss ratio. According to the results, our scheme improves throughput by up to 23% and reduces end-to-end delay by up to 60%

Hybrid features for object detection in RGB-D scenes

&lt;div&gt;Object detection is considered a hot research topic in applications of artificial intel-ligence and computer vision. Historically, object detection was widely used in var-ious fields like surveillance, fine-grained activities and robotics. All studies focus on improving accuracy for object detection using images, whether indoor or outdoor scenes. Therefore, this paper took a shot by improving the doable features extraction and proposing crossed sliding window approach using exiting classifiers for object de-tection. In this paper, the contribution includes two parts: First, improving local depth pattern feature along side SIFT and the second part explains a new technique presented by proposing crossed sliding window approach using two different types of images (colored and depth). Two types of features local depth patterns for detection (LDPD) and scale-invariant feature transform (SIFT) were merged as one feature vector. The RGB-D object dataset has been used and it consists of 300 different objects and in-cludes thousands of scenes. The proposed approach achieved high results comparing to other features or separated features that are used in this paper. All experiments and comparatives were applied on the same dataset for the same objective. Experimental results report a high accuracy in terms of detection rate, recall, precision and F1 scorein RGB-D scenes.&lt;/div&gt;</jats:p

A New Scheme for Detecting Malicious Nodes in Vehicular Ad Hoc Networks Based on Monitoring Node Behavior

Vehicular ad hoc networks have played a key role in intelligent transportation systems that considerably improve road safety and management. This new technology allows vehicles to communicate and share road information. However, malicious users may inject false emergency alerts into vehicular ad hoc networks, preventing nodes from accessing accurate road information. In order to assure the reliability and trustworthiness of information through the networks, assessing the credibility of nodes has become a critical task in vehicular ad hoc networks. A new scheme for malicious node detection is proposed in this work. Multiple factors are fed into a fuzzy logic model for evaluating the trust for each node. Vehicles are divided into clusters in our approach, and a road side unit manages each cluster. The road side unit assesses the credibility of nodes before accessing vehicular ad hoc networks. The road side unit evicts a malicious node based on trust value. Simulations are used to validate our technique. We demonstrate that our scheme can detect and evict all malicious nodes in the vehicular ad hoc network over time, lowering the ratio of malicious nodes. Furthermore, it has a positive impact on selfish node participation. The scheme increases the success rate of delivered data to the same level as the ideal cases when no selfish node is present