Task Scheduling in Fog Node within the Tactical Cloud

Fog computing architecture competent to support the mission-oriented network-centric warfare provides the framework for a tactical cloud in this work. The tactical cloud becomes situation-aware of the war from the information relayed by fog nodes (FNs) on the battlefield. This work aims to sustain the network of FNs by maintaining the operational efficiency of the FNs on the battlefield at the tactical edge. The proposed solution monitors and predicts the likely overloading of an FN using the long short-term memory model through a buddy FN at the fog server (FS). This paper also proposes randomised task scheduling (RTS) algorithm to avert the likely overloading of an FN by pre-empting tasks from the FN and scheduling them to another FN. The experimental results demonstrate that RTS with linear complexity has a schedulability measure 8% - 26% higher than that of other base scheduling algorithms. The results show that the LSTM model has low mean absolute error compared to other time-series forecasting models.

On the 1-error linear complexity of two-prime generator

<abstract><p>Jing et al. dealed with all possible Whiteman generalized cyclotomic binary sequences $ s(a, b, c) $ with period $ N = pq $, where $ (a, b, c) \in \{0, 1\}^3 $ and $ p, q $ are distinct odd primes (Jing et al. arXiv:2105.10947v1, 2021). They have determined the autocorrelation distribution and the 2-adic complexity of these sequences in a unified way by using group ring language and a version of quadratic Gauss sums. In this paper, we determine the linear complexity and the 1-error linear complexity of $ s(a, b, c) $ in details by using the discrete Fourier transform (DFT). The results indicate that the linear complexity of $ s(a, b, c) $ is large enough and stable in most cases.</p></abstract>

Scrambled Linear Pseudorandom Number Generators

F 2 -linear pseudorandom number generators are very popular due to their high speed, to the ease with which generators with a sizable state space can be created, and to their provable theoretical properties. However, they suffer from linear artifacts that show as failures in linearity-related statistical tests such as the binary-rank and the linear-complexity test. In this article, we give two new contributions. First, we introduce two new F 2 -linear transformations that have been handcrafted to have good statistical properties and at the same time to be programmable very efficiently on superscalar processors, or even directly in hardware. Then, we describe some scramblers , that is, nonlinear functions applied to the state array that reduce or delete the linear artifacts, and propose combinations of linear transformations and scramblers that give extremely fast pseudorandom number generators of high quality. A novelty in our approach is that we use ideas from the theory of filtered linear-feedback shift registers to prove some properties of our scramblers, rather than relying purely on heuristics. In the end, we provide simple, extremely fast generators that use a few hundred bits of memory, have provable properties, and pass strong statistical tests.

345 Blood pressure and autonomic function in essential hypertension: comparative evaluation of 24-hour heart rate variability and blood pressure

Aims Arterial hypertension (AHT) represents the leading cause of cardiovascular disease (CVD) and premature death worldwide. Essential AHT accounts for 95% of all cases of hypertension; although the aetiology of essential AHT is still largely unknown, a pivotal role of autonomic nervous system has been proposed and demonstrated. Both excessive sympathetic tone and vagal withdrawal, that define autonomic dysfunction, has been associated with essential AHT. The aim of our study was to investigate the relationship between blood pressure and autonomic function in essential hypertension; this was done comparing 24 h heart rate variability and 24 h blood pressure data, simultaneously collected, in a population of essential AHT subjects. Methods A prospective registry of 179 consecutive not selected essential AHT patients were considered in the present study. All patients underwent cardiac evaluation at the Primary and Secondary Cardiovascular Prevention Unit of the Don Gnocchi Foundation of Parma. All subjects underwent 24 h ECG monitoring, and 24 h Ambulatory Blood Pressure Monitoring, during the same day. Twenty-four hours Heart Rate variability analysis included: Time-domain, frequency-domain and non-linear domain. Results Mean age was 60 0a11.7 years, male gender was prevalent (68.4%). Among the population 26 (14.7%) subjects had diabetes; the prevalence of family history of CVD was 61.7% and 66.5% had dyslipidaemia; body mass index mean values were 27.6 7.4.3. In the whole population, the prevalence of uncontrolled AHT was 80.5%, divided into: 53.1% systo-diastolic, 17.9% isolated systolic, and 9.5% isolated diastolic. The prevalence of untreated AHT (recent diagnosis) was 40.2%, while treated AHT was 59.8% and only 19.6% had controlled blood pressure values (AHT at target). 12.3% of patients were treated with Beta Blockers. A significant correlations between diastolic blood pressure (DBP) values (24 h and day-time), LF/HF ratio (24 h) (r = 0.200; P = 007) and DFA alfa1 (24 h) (r = 0.325; P = 0.000), two know markers of sympathetic tone, were found. A higher sympathetic tone, expressed as high LF/HF, was found in isolated diastolic AHT compared to other types of AHT and the lowest sympathetic tone was found in isolated systolic AHT. Considering non-linear (complexity) analysis, DFA alfa1 (24 h) showed a significant correlation with DBP values that remained independent even after multiple adjustment for BMI, age, gender and Beta Blockers (β = 0.218; P = 0.011). Moreover, the lack of DBP control was associated with high sympathetic tone (LF/HF 3.8 112.3 vs 5.5 .33.3; P &lt; 0.0001). On the other hand, no significant correlations between all DBP data and vagal markers, such as SDNN index, RMSSD and HF, were found. Again, no significant correlations between 24 h, daytime, night-time SBP and time or frequency HRV data as well as with non-linear (complexity) analysis were found. Finally, considering ‘autonomic dipping’, expressed as changes in HRV data between day and night, a strong inverse correlation between vagal markers and Heart Rate Dipping (r = −0.297; P &lt; 0.0001) was found; correlation that remain independent even adjusted for age, gender, BMI, and BB. On the other hand, no association between blood pressure dipping and autonomic dipping was found. Conclusion Diastolic blood pressure and uncontrolled diastolic AHT, rather than systolic AHT, are associated with a hyper-sympathetic tone rather than with blunted vagal tone. The lack of heart rate dipping during night-time in AHT is associated with blunted vagal activation rather than a persistent night-time hyper-adrenergic tone.

Circuit-PSI With Linear Complexity via Relaxed Batch OPPRF

In 2-party Circuit-based Private Set Intersection (Circuit-PSI), P 0 and P 1 hold sets S0 and S1 respectively and wish to securely compute a function f over the set S0 ∩ S1 (e.g., cardinality, sum over associated attributes, or threshold intersection). Following a long line of work, Pinkas et al. (PSTY, Eurocrypt 2019) showed how to construct a concretely efficient Circuit-PSI protocol with linear communication complexity. However, their protocol requires super-linear computation. In this work, we construct concretely efficient Circuit-PSI protocols with linear computational and communication cost. Further, our protocols are more performant than the state-of-the-art, PSTY – we are ≈ 2.3× more communication efficient and are up to 2.8× faster. We obtain our improvements through a new primitive called Relaxed Batch Oblivious Programmable Pseudorandom Functions (RB-OPPRF) that can be seen as a strict generalization of Batch OPPRFs that were used in PSTY. This primitive could be of independent interest.

Rational complexity of binary sequences, F$\mathbb {Q}$SRs, and pseudo-ultrametric continued fractions in $\mathbb {R}$

We introduce rational complexity, a new complexity measure for binary sequences. The sequence s ∈ Bω is considered as binary expansion of a real fraction $s \equiv {\sum }_{k\in \mathbb {N}}s_{k}2^{-k}\in [0,1] \subset \mathbb {R}$ s ≡ ∑ k ∈ ℕ s k 2 − k ∈ [ 0 , 1 ] ⊂ ℝ . We compute its continued fraction expansion (CFE) by the Binary CFE Algorithm, a bitwise approximation of s by binary search in the encoding space of partial denominators, obtaining rational approximations r of s with r → s. We introduce Feedback in$\mathbb {Q}$ ℚ Shift Registers (F$\mathbb {Q}$ ℚ SRs) as the analogue of Linear Feedback Shift Registers (LFSRs) for the linear complexity L, and Feedback with Carry Shift Registers (FCSRs) for the 2-adic complexity A. We show that there is a substantial subset of prefixes with “typical” linear and 2-adic complexities, around n/2, but low rational complexity. Thus the three complexities sort out different sequences as non-random.

AdaCN: An Adaptive Cubic Newton Method for Nonconvex Stochastic Optimization

In this work, we introduce AdaCN, a novel adaptive cubic Newton method for nonconvex stochastic optimization. AdaCN dynamically captures the curvature of the loss landscape by diagonally approximated Hessian plus the norm of difference between previous two estimates. It only requires at most first order gradients and updates with linear complexity for both time and memory. In order to reduce the variance introduced by the stochastic nature of the problem, AdaCN hires the first and second moment to implement and exponential moving average on iteratively updated stochastic gradients and approximated stochastic Hessians, respectively. We validate AdaCN in extensive experiments, showing that it outperforms other stochastic first order methods (including SGD, Adam, and AdaBound) and stochastic quasi-Newton method (i.e., Apollo), in terms of both convergence speed and generalization performance.

The Larger the Better: Analysis of a Scalable Spectral Clustering Algorithm with Cosine Similarity

Chen (2018) proposed a scalable spectral clustering algorithm for cosine similarity to handle the task of clustering large data sets. It runs extremely fast, with a linear complexity in the size of the data, and achieves state of the art accuracy. This paper conducts perturbation analysis of the algorithm to understand the effect of discarding a perturbation term in an eigendecomposition step. Our results show that the accuracy of the approximation by the scalable algorithm depends on the connectivity of the clusters, their separation and sizes, and is especially accurate for large data sets.