scholarly journals Rotational Cryptanalysis of MORUS

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2426
Author(s):  
Iftekhar Salam
Keyword(s):  
The State ◽  
Experimental Results ◽  
Word Size ◽  
Full Version ◽  
Caesar Competition ◽  
One Step ◽  
Rotational Invariant

MORUS is one of the finalists of the CAESAR competition. This is an ARX construction that required investigation against rotational cryptanalysis. We investigated the power of rotational cryptanalysis against MORUS. We show that all the operations in the state update function of MORUS maintain the rotational pairs when the rotation distance is set to a multiple of the sub-word size. Our investigation also confirms that the rotational pairs can be used as distinguishers for the full version of MORUS if the constants used in MORUS are rotational-invariant. However, the actual constants used in MORUS are not rotational-invariant. The introduction of such constants in the state update function breaks the symmetry of the rotational pairs. Experimental results show that rotational pairs can be used as distinguishers for only one step of the initialization phase of MORUS. For more than one step, there are not enough known differences in the rotational pairs of MORUS to provide an effective distinguisher. This is due to the XOR-ing of the constants that are not rotational-invariant. Therefore, it is unlikely for an adversary to construct a distinguisher for the full version of MORUS by observing the rotational pairs.

Atomization of Liquids by Means of a Rotating Cup

1950 ◽  
Vol 17 (2) ◽  
pp. 145-153 ◽  
Author(s):  
J. O. Hinze ◽  
H. Milborn
Keyword(s):  
Working Conditions ◽  
Drop Size ◽  
Film Formation ◽  
Angular Speed ◽  
The State ◽  
Experimental Results ◽  
Drop Formation ◽  
Dimensionless Groups ◽  
Different Types ◽  
Centrifugal Action

Abstract Liquid, supplied through a stationary tube to the inner part of a rotating cup widening toward a brim, flows viscously in a thin layer toward this brim and is then flung off, all by centrifugal action. The flow within this layer and the disintegration phenomena occurring beyond the brim have been studied, experimentally as well as theoretically. A formula has been derived for the thickness and for the radial velocity of the liquid layer within the cup, which proved to agree reasonably well with experimental results. Three essentially different types of disintegration may take place around and beyond the edge of the cup designated, respectively, by: (a) the state of direct drop formation; (b) the state of ligament formation; and (c) the state of film formation. Which one of these is realized depends upon working conditions. Transition from state (a) into (b), or of state (b) into state (c) is promoted by an increased quantity of supply, an increased angular speed, a decreased diameter of the cup, an increased density, an increased viscosity, and a decreased surface tension of the liquid. The experimental results have been expressed in relationships between relevant dimensionless groups. For the state of ligament formation a semiempirical relationship has been derived between the number of ligaments and dimensionless groups determining the working conditions of the cup. Results of drop-size measurements made for the state of ligament formation as well as for the state of film formation show that atomization by mere rotation of the cup is much more uniform than commonly achieved with pressure atomizers.

scholarly journals A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis

2021 ◽  
Vol 11 (23) ◽  
pp. 11344
Author(s):  
Wei Ke ◽  
Ka-Hou Chan
Keyword(s):  
Probability Distribution ◽  
Information Extraction ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Final Analysis ◽  
The State ◽  
Experimental Results ◽  
Content Adaptive

Paragraph-based datasets are hard to analyze by a simple RNN, because a long sequence always contains lengthy problems of long-term dependencies. In this work, we propose a Multilayer Content-Adaptive Recurrent Unit (CARU) network for paragraph information extraction. In addition, we present a type of CNN-based model as an extractor to explore and capture useful features in the hidden state, which represent the content of the entire paragraph. In particular, we introduce the Chebyshev pooling to connect to the end of the CNN-based extractor instead of using the maximum pooling. This can project the features into a probability distribution so as to provide an interpretable evaluation for the final analysis. Experimental results demonstrate the superiority of the proposed approach, being compared to the state-of-the-art models.

scholarly journals Price Forecasting of Mango in Varanasi Market of Uttar Pradesh

2018 ◽  
Vol 6 (2) ◽  
pp. 218-224 ◽  
Author(s):  
Ravishankar Pardhi ◽  
Rakesh Singh ◽  
Ranjit Kumar Paul
Keyword(s):  
Arima Model ◽  
Uttar Pradesh ◽  
The State ◽  
Percentage Error ◽  
Price Forecasting ◽  
Harvest Management ◽  
Alternate Bearing ◽  
Agricultural Produce ◽  
One Step ◽  
Forecasting Analysis

The study had been made to forecast the price of mango using ARIMA model in one of the major markets of Uttar Pradesh as the state ranks first position in production of mango in India. Varanasi market was selected purposively on the basis of second highest arrival market of mango in the state. Using ARIMA methodology on the monthly prices of mango collected from the Agricultural Produce Market Committee (APMC), Varanasi for the year 1993 to 2015. As the mango fruit having property of alternate bearing, only six month data from March to August was available in the market and accordingly had been used for forecasting analysis using E-views 7 software. The results revealed that the price in selected market was found to be highest during the start of the season using ARIMA (1,0,6) model, confirming the validity of model through Mean Absolute Percentage Error (MAPE). The MAPE was found to be less than 10 per cent for one step ahead forecast of year 2015. Forecasted price for the month of March was almost double than the price of other months. It indicates the necessity of adopting pre and post harvest management technologies for getting the benefit over increase in prices.

scholarly journals RDFuzz: Accelerating Directed Fuzzing with Intertwined Schedule and Optimized Mutation

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jiaxi Ye ◽  
Ruilin Li ◽  
Bin Zhang
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Exploration And Exploitation ◽  
Balance Problem ◽  
Evaluation Strategy ◽  
Testing Schedule ◽  
Available Resources

Directed fuzzing is a practical technique, which concentrates its testing energy on the process toward the target code areas, while costing little on other unconcerned components. It is a promising way to make better use of available resources, especially in testing large-scale programs. However, by observing the state-of-the-art-directed fuzzing engine (AFLGo), we argue that there are two universal limitations, the balance problem between the exploration and the exploitation and the blindness in mutation toward the target code areas. In this paper, we present a new prototype RDFuzz to address these two limitations. In RDFuzz, we first introduce the frequency-guided strategy in the exploration and improve its accuracy by adopting the branch-level instead of the path-level frequency. Then, we introduce the input-distance-based evaluation strategy in the exploitation stage and present an optimized mutation to distinguish and protect the distance sensitive input content. Moreover, an intertwined testing schedule is leveraged to perform the exploration and exploitation in turn. We test RDFuzz on 7 benchmarks, and the experimental results demonstrate that RDFuzz is skilled at driving the program toward the target code areas, and it is not easily stuck by the balance problem of the exploration and the exploitation.

scholarly journals FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning

2020 ◽  
Vol 10 (8) ◽  
pp. 2864 ◽  
Author(s):  
Muhammad Asad ◽  
Ahmed Moustafa ◽  
Takayuki Ito
Keyword(s):  
Privacy Preservation ◽  
State Of The Art ◽  
Homomorphic Encryption ◽  
Evaluation Criteria ◽  
The State ◽  
Compression Algorithm ◽  
Experimental Results ◽  
Local Learning ◽  
Learning Models ◽  
Communication Efficiency

Artificial Intelligence (AI) has been applied to solve various challenges of real-world problems in recent years. However, the emergence of new AI technologies has brought several problems, especially with regard to communication efficiency, security threats and privacy violations. Towards this end, Federated Learning (FL) has received widespread attention due to its ability to facilitate the collaborative training of local learning models without compromising the privacy of data. However, recent studies have shown that FL still consumes considerable amounts of communication resources. These communication resources are vital for updating the learning models. In addition, the privacy of data could still be compromised once sharing the parameters of the local learning models in order to update the global model. Towards this end, we propose a new approach, namely, Federated Optimisation (FedOpt) in order to promote communication efficiency and privacy preservation in FL. In order to implement FedOpt, we design a novel compression algorithm, namely, Sparse Compression Algorithm (SCA) for efficient communication, and then integrate the additively homomorphic encryption with differential privacy to prevent data from being leaked. Thus, the proposed FedOpt smoothly trade-offs communication efficiency and privacy preservation in order to adopt the learning task. The experimental results demonstrate that FedOpt outperforms the state-of-the-art FL approaches. In particular, we consider three different evaluation criteria; model accuracy, communication efficiency and computation overhead. Then, we compare the proposed FedOpt with the baseline configurations and the state-of-the-art approaches, i.e., Federated Averaging (FedAvg) and the paillier-encryption based privacy-preserving deep learning (PPDL) on all these three evaluation criteria. The experimental results show that FedOpt is able to converge within fewer training epochs and a smaller privacy budget.

scholarly journals Saga of Superfluid Solids

Physics ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 49-66 ◽  
Author(s):  
Vyacheslav I. Yukalov
Keyword(s):  
Numerical Simulations ◽  
State Of The Art ◽  
Theoretical Models ◽  
The State ◽  
Experimental Results ◽  
Standing Problem

The article presents the state of the art and reviews the literature on the long-standing problem of the possibility for a sample to be at the same time solid and superfluid. Theoretical models, numerical simulations, and experimental results are discussed.

scholarly journals Evolutionary Manytasking Optimization Based on Symbiosis in Biocoenosis

2019 ◽  
Vol 33 ◽  
pp. 4295-4303 ◽  
Author(s):  
Rung-Tzuo Liaw ◽  
Chuan-Kang Ting
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Benchmark Problems ◽  
Good Estimator ◽  
Multiple Tasks ◽  
Effectiveness And Efficiency ◽  
Evolutionary Multitasking

Evolutionary multitasking is a significant emerging search paradigm that utilizes evolutionary algorithms to concurrently optimize multiple tasks. The multi-factorial evolutionary algorithm renders an effectual realization of evolutionary multitasking on two or three tasks. However, there remains room for improvement on the performance and capability of evolutionary multitasking. Beyond three tasks, this paper proposes a novel framework, called the symbiosis in biocoenosis optimization (SBO), to address evolutionary many-tasking optimization. The SBO leverages the notion of symbiosis in biocoenosis for transferring information and knowledge among different tasks through three major components: 1) transferring information through inter-task individual replacement, 2) measuring symbiosis through intertask paired evaluations, and 3) coordinating the frequency and quantity of transfer based on symbiosis in biocoenosis. The inter-task individual replacement with paired evaluations caters for estimation of symbiosis, while the symbiosis in biocoenosis provides a good estimator of transfer. This study examines the effectiveness and efficiency of the SBO on a suite of many-tasking benchmark problems, designed to deal with 30 tasks simultaneously. The experimental results show that SBO leads to better solutions and faster convergence than the state-of-the-art evolutionary multitasking algorithms. Moreover, the results indicate that SBO is highly capable of identifying the similarity between problems and transferring information appropriately.

scholarly journals Input-Aware Implication Selection Scheme Utilizing ATPG for Efficient Concurrent Error Detection

Electronics ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 258 ◽  
Author(s):  
Abdus Hassan ◽  
Umar Afzaal ◽  
Tooba Arifeen ◽  
Jeong Lee
Keyword(s):  
Error Detection ◽  
High Probability ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Concurrent Error Detection ◽  
Selection Algorithm ◽  
Probability Of Error ◽  
Selection Scheme ◽  
Selection Strategies

Recently, concurrent error detection enabled through invariant relationships between different wires in a circuit has been proposed. Because there are many such implications in a circuit, selection strategies have been developed to select the most valuable implications for inclusion in the checker hardware such that a sufficiently high probability of error detection ( P d e t e c t i o n ) is achieved. These algorithms, however, due to their heuristic nature cannot guarantee a lossless P d e t e c t i o n . In this paper, we develop a new input-aware implication selection algorithm with the help of ATPG which minimizes loss on P d e t e c t i o n . In our algorithm, the detectability of errors for each candidate implication is carefully evaluated using error prone vectors. The evaluation results are then utilized to select the most efficient candidates for achieving optimal P d e t e c t i o n . The experimental results on 15 representative combinatorial benchmark circuits from the MCNC benchmarks suite show that the implications selected from our algorithm achieve better P d e t e c t i o n in comparison to the state of the art. The proposed method also offers better performance, up to 41.10%, in terms of the proposed impact-level metric, which is the ratio of achieved P d e t e c t i o n to the implication count.

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