Single Image Reflection Separation via Component Synergy

The reflection superposition phenomenon is complex and widely distributed in the real world, which derives various simplified linear and nonlinear formulations of the problem. In this paper, based on the investigation of the weaknesses of existing models, we propose a more general form of the superposition model by introducing a learnable residue term, which can effectively capture residual information during decomposition, guiding the separated layers to be complete. In order to fully capitalize on its advantages, we further design the network structure elaborately, including a novel dual-stream interaction mechanism and a powerful decomposition network with a semantic pyramid encoder. Extensive experiments and ablation studies are conducted to verify our superiority over state-of-the-art approaches on multiple real-world benchmark datasets. Our code is publicly available at https://github.com/mingcv/DSRNet.Comment: Accepted to ICCV 202

Dynamic MDS Matrices for Substantial Cryptographic Strength

Ciphers get their strength from the mathematical functions of confusion and diffusion, also known as substitution and permutation. These were the basics of classical cryptography and they are still the basic part of modern ciphers. In block ciphers diffusion is achieved by the use of Maximum Distance Separable (MDS) matrices. In this paper we present some methods for constructing dynamic (and random) MDS matrices.Comment: Short paper at WISA'10, 201

International standards for stream ciphers: a progress report

The main objective of this paper is to review the current status of stream cipher standardisation. The hope is that, by doing so, the algorithms and techniques that are likely to be standardised at some point during the next year or so will be subjected to rigorous scrutiny by the crytopgraphic community

19th Annual Andrew B. Conteh Student Academic Conference

Minnesota State University Moorhead Student Academic Conference abstract book.https://red.mnstate.edu/sac-book/1018/thumbnail.jp

Cryptoraptor : high throughput reconfigurable cryptographic processor for symmetric key encryption and cryptographic hash functions

textIn cryptographic processor design, the selection of functional primitives and connection structures between these primitives are extremely crucial to maximize throughput and flexibility. Hence, detailed analysis on the specifications and requirements of existing crypto-systems plays a crucial role in cryptographic processor design. This thesis provides the most comprehensive literature review that we are aware of on the widest range of existing cryptographic algorithms, their specifications, requirements, and hardware structures. In the light of this analysis, it also describes a high performance, low power, and highly flexible cryptographic processor, Cryptoraptor, that is designed to support both today's and tomorrow's encryption standards. To the best of our knowledge, the proposed cryptographic processor supports the widest range of cryptographic algorithms compared to other solutions in the literature and is the only crypto-specific processor targeting the future standards as well. Unlike previous work, we aim for maximum throughput for all known encryption standards, and to support future standards as well. Our 1GHz design achieves a peak throughput of 128Gbps for AES-128 which is competitive with ASIC designs and has 25X and 160X higher throughput per area than CPU and GPU solutions, respectively.Electrical and Computer Engineerin

Modified Bistable Modules for Bias Deployable Structures

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Bias deployable grids are meshes with two directions of rotation on the ground plan with respect to the edges. They offer benefits such as three-dimensional resistance with supports around the entire perimeter of a rectangular layout, and consist exclusively of load-bearing scissors as opposed to the usual combinations of load-bearing scissors and bracing scissors. However, their resistance to angular distortion is limited, and they require auxiliary elements to maintain the fully deployed position. Nevertheless, they are very promising solutions for medium-span emergency buildings. This paper proposes a bistable module adapted to bias deployable structures. The geometrical incompatibilities of several modules are analysed together with their behaviour based on the kinematic models that were built, which alternate different types of nodes and different geometries of the perimeter scissors, making it possible to calibrate the level of incompatibility introduced. The dimensions of the nodes are also taken into account. The tests are checked against the results of several series of dynamic calculations.This research was carried out as a part of the Spanish Research Project on Deployable and Modular Constructions for Situations of Humanitarian Catastrophe, CODEMOSCH (Reference BIA2016-79459-R), funded by the Spanish Ministry of Industry, Energy, and Competitiveness (MINECO). Financing of the open access fee: Universidade da Coruña / CISU

Coping with Data Scarcity: First Steps towards Word Expansion for a Chatbot in the Urban transportation Domain

Hizkuntzaren Prozesamenduan (HP) zenbait arlotan hitzak erabili izan dira tradizionalki zabaltze-tekniken garapenean, hala nola Informazioaren Berreskurapenean (IB) edota Galdera-Erantzun (GE) sistemetan. Master tesi honek bi hurbilpen aurkezten ditu Elkarrizketa-Sistemen (ES) arloan zabaltze-teknikak garatze aldera, zehazkiago Donostiako (Gipuzkoa) hiri-garraiorako chatbot baten ulertze-modulua garatzera zuzendurik. Lehenengo hurbilpenak hitz-bektoreak erabiltzen ditu semantikoki antzekoak diren terminoak erauzteko, kasu honetan FastText-eko aurre-entreinaturiko embedding sorta espainieraz eta bigarren hurbiltzeak hitzen adiera-desanbiguazioa erabiltzen du sinonimoak datu-base lexiko baten bidez erauzteko, kasu honetan espainierazko WordNet-a. Horretarako, ataza kolaboratibo bat diseinatu da, non corpusa osatuko baitugu balizko-egoera erreal baten sarrerak jasoz. Bestalde, domeinuz kanpo dauden sarrerak identi katze aldera, bi esperimentu sorta garatu dira. Lehenengo fasean kali katze sistema bat garatu da, non corpuseko terminoak Term Frequency-Inverse Document Frequency (TF-IDF) erabiliz ordenatzen baitiren eta ondoren kali katze-sistema kosinu-antzekotasunaren bidez osatzen da. Bigarren faseak aurreko kali katze-sistema formalizatuko da, hiru datu-multzo prestatuz eta estrati katuz. Datu-multzo hauek erregresore lineal bat eta Kernel linealarekin euskarri bektoredun makina bat entreinatzeko erabili dira. Emaitzen arabera, aurre-entreinaturiko bektoreek leialtasun handiagoa daukate input errealari dagokionez. Hala ere, datu-base lexikoek estaldura linguistiko zabalagoa gehituko diote zabalduriko corpus hipotetikoari. Azkenik, domeinuaren diskriminazioari dagokionez, emaitzek TF-IDF-tik erauzitako termino gehienen zeukan datu-multzoa hobesten dute.Text expansion techniques have been used in some sub elds of Natural Language Processing (NLP) such as Information Retrieval or Question-Answering Systems. This Master's Thesis presents two approaches for expansion within the context of Dialogue Systems (DS), more precisely for the Natural Language Understanding (NLU) module of a chatbot for the urban transportation domain in San Sebastian (Gipuzkoa). The rst approach uses word vectors to obtain semantically similar terms while the second one involves synonym extraction from a lexical database. For this purpose, a corpus composed of real case scenario inputs has been exploited. Furthermore, the qualitative analysis of the implemented expansion techniques revealed a need to lter out-of-domain inputs. In relation to this problem, two di erent sets of experiments have been carried out. First, the feasibility of using Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity as discrimination features was explored. Then, linear regression and Support Vector Machine (SVM) classi ers were trained and tested. Results show that pre-trained word embedding expansion constitutes a more loyal representation of real case scenario inputs, whereas lexical database expansion adds a wider linguistic coverage to a hypothetically expanded version of the corpus. For out-of-domain detection, increasing the number of features improves both, linear regression and SVM classi cation results

Golden Fish: An Intelligent Stream Cipher Fuse Memory Modules

In this paper, we use a high-order iterated function generated by block cipher as the nonlinear filter to improve the security of stream cipher. Moreover, by combining the published rounds function in block cipher and OFB as the nonlinear functional mode with an extra memory module, we enable to control the nonlinear complexity of the design. This new approach fuses the block cipher operation mode with two memory modules in one stream cipher. The security of this design is proven by the both periodic and nonlinear evaluation. The periods of this structure is guaranteed by the traditional Linear Feedback Shift Register design and the security of nonlinear characteristic is demonstrated by block cipher algorithm design itself, which is remarkably safer than the previous designs of stream cipher. We also can find such design style at SHA3