OpenRaiser/Intern-Atlas huggingface dataset:
Convolutional Neural Network-based Topology Optimization (CNN-TO) By Estimating Sensitivity of Compliance from Material Distribution
Venue: arXiv cs.LG 2019
Year: 2019

This paper proposes a new topology optimization method that applies a convolutional neural network (CNN), which is one deep learning technique for topology optimization problems. Using this method, we acquire a structure with a little higher performance that could not be obtained by the previous topology optimization method. In particular, in this paper, we solve a topology optimization problem aimed at maximizing stiffness with a mass constraint, which is a common type of topology optimization. In this paper, we first formulate the conventional topology optimization by the solid isotropic material with penalization method. Next, we formulate the topology optimization using CNN. Finally, we show the effectiveness of the proposed topology optimization method by solving a verification example, namely a topology optimization problem aimed at maximizing stiffness. In this research, as a result of solving the verification example for a small design area of 16x32 element, we obtain the solution different from the previous topology optimization method. This result suggests that stiffness information of structure can be extracted and analyzed for structural design by analyzing the density distribution using CNN like an image. This suggests that CNN technology can be utilized in the structural design and topology optimization.
A Deterministic plus Stochastic Model of the Residual Signal for Improved Parametric Speech Synthesis
Venue: arXiv cs.SD 2019
Year: 2019

Speech generated by parametric synthesizers generally suffers from a typical buzziness, similar to what was encountered in old LPC-like vocoders. In order to alleviate this problem, a more suited modeling of the excitation should be adopted. For this, we hereby propose an adaptation of the Deterministic plus Stochastic Model (DSM) for the residual. In this model, the excitation is divided into two distinct spectral bands delimited by the maximum voiced frequency. The deterministic part concerns the low-frequency contents and consists of a decomposition of pitch-synchronous residual frames on an orthonormal basis obtained by Principal Component Analysis. The stochastic component is a high-pass filtered noise whose time structure is modulated by an energy-envelope, similarly to what is done in the Harmonic plus Noise Model (HNM). The proposed residual model is integrated within a HMM-based speech synthesizer and is compared to the traditional excitation through a subjective test. Results show a significative improvement for both male and female voices. In addition the proposed model requires few computational load and memory, which is essential for its integration in commercial applications.
A Performance Comparison of Data Mining Algorithms Based Intrusion Detection System for Smart Grid
Venue: arXiv cs.CR 2019
Year: 2019

Smart grid is an emerging and promising technology. It uses the power of information technologies to deliver intelligently the electrical power to customers, and it allows the integration of the green technology to meet the environmental requirements. Unfortunately, information technologies have its inherent vulnerabilities and weaknesses that expose the smart grid to a wide variety of security risks. The Intrusion detection system (IDS) plays an important role in securing smart grid networks and detecting malicious activity, yet it suffers from several limitations. Many research papers have been published to address these issues using several algorithms and techniques. Therefore, a detailed comparison between these algorithms is needed. This paper presents an overview of four data mining algorithms used by IDS in Smart Grid. An evaluation of performance of these algorithms is conducted based on several metrics including the probability of detection, probability of false alarm, probability of miss detection, efficiency, and processing time. Results show that Random Forest outperforms the other three algorithms in detecting attacks with higher probability of detection, lower probability of false alarm, lower probability of miss detection, and higher accuracy.
DepthTransfer: Depth Extraction from Video Using Non-parametric Sampling
Venue: arXiv cs.CV 2019
Year: 2019

We describe a technique that automatically generates plausible depth maps from videos using non-parametric depth sampling. We demonstrate our technique in cases where past methods fail (non-translating cameras and dynamic scenes). Our technique is applicable to single images as well as videos. For videos, we use local motion cues to improve the inferred depth maps, while optical flow is used to ensure temporal depth consistency. For training and evaluation, we use a Kinect-based system to collect a large dataset containing stereoscopic videos with known depths. We show that our depth estimation technique outperforms the state-of-the-art on benchmark databases. Our technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and we demonstrate this through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.
Segmentation-Aware and Adaptive Iris Recognition
Venue: arXiv cs.CV 2019
Year: 2019

Iris recognition has emerged as one of the most accurate and convenient biometric for the human identification and has been increasingly employed in a wide range of e-security applications. The quality of iris images acquired at-a-distance or under less constrained imaging environments is known to degrade the iris matching accuracy. The periocular information is inherently embedded in such iris images and can be exploited to assist in the iris recognition under such non-ideal scenarios. Our analysis of such iris templates also indicates significant degradation and reduction in the region of interest, where the iris recognition can benefit from a similarity distance that can consider importance of different binary bits, instead of the direct use of Hamming distance in the literature. Periocular information can be dynamically reinforced, by incorporating the differences in the effective area of available iris regions, for more accurate iris recognition. This paper presents such a segmentation-assisted adaptive framework for more accurate less-constrained iris recognition. The effectiveness of this framework is evaluated on three publicly available iris databases using within-dataset and cross-dataset performance evaluation and validates the merit of the proposed iris recognition framework.
A Network-Level View of Author Influence
Venue: arXiv cs.DL 2019
Year: 2019

I compare several network-level measures of centrality to common measures of author reputation and influence (e.g. hindex, i10index), all taken over the data set of papers published in 2017 at major computer systems conferences and some controls. I hypothesize that centrality measures will correlate strongly with the reputation and influence measures. My results confirm several expected correlations and exhibit a few surprising absences of correlation. In particular, there was an absence of statistically significant correlation between degree centrality and hindex,
On Concept of Petri Nets Receptors and Effectors
Venue: arXiv cs.LO 2019
Year: 2019

New subclasses of Petri nets - Petri nets receptors and Petri nets effectors are introduced. The introduction/exclusion of such substructures in the main Petri net may be fulfilled in accordance with the Fusion/Defusion principles. We propose two pairs of entities: position marking receptor (effector) and transition marking receptor (effector), which allow to observe parameters of the main Petri net and, if necessary, to carry out their regulation.
Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework
Venue: arXiv cs.IR 2019
Year: 2019

Design of recommender systems aimed at achieving high prediction accuracy is a widely researched area. However, several studies have suggested the need for diversified recommendations, with acceptable level of accuracy, to avoid monotony and improve customers experience. However, increasing diversity comes with an associated reduction in recommendation accuracy; thereby necessitating an optimum tradeoff between the two. In this work, we attempt to achieve accuracy vs diversity balance, by exploiting available ratings and item metadata, through a single, joint optimization model built over the matrix completion framework. Most existing works, unlike our formulation, propose a 2 stage model, a heuristic item ranking scheme on top of an existing collaborative filtering technique. Experimental evaluation on a movie recommender system indicates that our model achieves higher diversity for a given drop in accuracy as compared to existing state of the art techniques.
Embedding of FRPN in CNN architecture
Venue: arXiv cs.CV 2019
Year: 2019

This paper extends the fully recursive perceptron network (FRPN) model for vectorial inputs to include deep convolutional neural networks (CNNs) which can accept multi-dimensional inputs. A FRPN consists of a recursive layer, which, given a fixed input, iteratively computes an equilibrium state. The unfolding realized with this kind of iterative mechanism allows to simulate a deep neural network with any number of layers. The extension of the FRPN to CNN results in an architecture, which we call convolutional-FRPN (C-FRPN), where the convolutional layers are recursive. The method is evaluated on several image classification benchmarks. It is shown that the C-FRPN consistently outperforms standard CNNs having the same number of parameters. The gap in performance is particularly large for small networks, showing that the C-FRPN is a very powerful architecture, since it allows to obtain equivalent performance with fewer parameters when compared with deep CNNs.
A Simulation Based Performance Evaluation of Optical Ethernet Switch
Venue: arXiv cs.NI 2019
Year: 2019

With the advent of several new Cloud Radio Access Network (C-RAN) technologies, todays networking environment is dramatically altered and is experiencing a rapid transformation. One of the most important is Ethernet based C-RAN, in support of which many products such as optical Ethernet switches have recently appeared on the market. This paper presents the performance analysis of such switches with respect to Packet Loss Ratio (PLR), Latency and Packet Delay Variation (PDV). We employed the Simula based on Discrete Event Modelling on Simula (DEMOS), a context class for discrete event simulation to simulate a cut-through optical Ethernet switch under two types of traffics: High priority (HP) traffic and Low priority (LP) traffic. In this way, the paper evaluates the optical Ethernet switch performance quantitatively. The results obtained from the simulator showed that the high quality of service was reflected on HP traffic and the low quality of service in LP traffic. Hence, HP traffic can be used for transporting Radio over Ethernet (RoE) traffic while LP traffic can used for transporting time insensitive application. It is also found that HP traffic experiences a PDV equals to the duration of maximum sized LP traffic in Optical Ethernet switch.
Extreme learning machine-based model for Solubility estimation of hydrocarbon gases in electrolyte solutions
Venue: arXiv cs.LG 2019
Year: 2019

Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases including methane, ethane, propane, and butane in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points concluded to R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility led to confirm the ability of the proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of the model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants.
Three complete deterministic polynomial algorithms for 3SAT
Venue: arXiv cs.CC 2019
Year: 2019

Three algorithms are presented that determine the existence of satisfying assignments for 3SAT Boolean satisfiability expressions. One algorithm is presented for determining an instance of a satisfying assignment, where such exists. The algorithms are each deterministic and of polynomial complexity. The algorithms determining existence are complete as each produces a certificate of non-satisfiability, for instances where no satisfying assignment exists, and of satisfiability for such assignment does exist.
StructBoost: Boosting Methods for Predicting Structured Output Variables
Venue: arXiv cs.LG 2020
Year: 2020

Boosting is a method for learning a single accurate predictor by linearly combining a set of less accurate weak learners. Recently, structured learning has found many applications in computer vision. Inspired by structured support vector machines (SSVM), here we propose a new boosting algorithm for structured output prediction, which we refer to as StructBoost. StructBoost supports nonlinear structured learning by combining a set of weak structured learners. As SSVM generalizes SVM, our StructBoost generalizes standard boosting approaches such as AdaBoost, or LPBoost to structured learning. The resulting optimization problem of StructBoost is more challenging than SSVM in the sense that it may involve exponentially many variables and constraints. In contrast, for SSVM one usually has an exponential number of constraints and a cutting-plane method is used. In order to efficiently solve StructBoost, we formulate an equivalent $ 1 $-slack formulation and solve it using a combination of cutting planes and column generation. We show the versatility and usefulness of StructBoost on a range of problems such as optimizing the tree loss for hierarchical multi-class classification, optimizing the Pascal overlap criterion for robust visual tracking and learning conditional random field parameters for image segmentation.
Group ring cryptography: Cryptography, key exchange, public key
Venue: arXiv cs.CR 2020
Year: 2020

General cryptographic schemes are presented where keys can be one-time or ephemeral. Processes for key exchange are derived. Public key cryptographic schemes based on the new systems are easily established. Authentication and signature schemes are implemented. The schemes are an advance on group ring techniques and are easily implemented but highly secure. They may be integrated with error-correcting coding schemes so that encryption/coding and decryption/decoding may be done simultaneously.
SIMD Compression and the Intersection of Sorted Integers
Venue: arXiv cs.IR 2020
Year: 2020

Sorted lists of integers are commonly used in inverted indexes and database systems. They are often compressed in memory. We can use the SIMD instructions available in common processors to boost the speed of integer compression schemes. Our S4-BP128-D4 scheme uses as little as 0.7 CPU cycles per decoded integer while still providing state-of-the-art compression. However, if the subsequent processing of the integers is slow, the effort spent on optimizing decoding speed can be wasted. To show that it does not have to be so, we (1) vectorize and optimize the intersection of posting lists; (2) introduce the SIMD Galloping algorithm. We exploit the fact that one SIMD instruction can compare 4 pairs of integers at once. We experiment with two TREC text collections, GOV2 and ClueWeb09 (Category B), using logs from the TREC million-query track. We show that using only the SIMD instructions ubiquitous in all modern CPUs, our techniques for conjunctive queries can double the speed of a state-of-the-art approach.
Completeness of Kozen's Axiomatization for the Modal mu-Calculus: A Simple Proof
Venue: arXiv cs.LO 2020
Year: 2020

The modal mu-calculus, introduced by Dexter Kozen, is an extension of modal logic with fixpoint operators. Its axiomatization, Koz, was introduced at the same time and is an extension of the minimal modal logic K with the so-called Park fixpoint induction principle. It took more than a decade for the completeness of Koz to be proven, finally achieved by Igor Walukiewicz. However, his proof is fairly involved. In this article, we present an improved proof for the completeness of Koz which, although similar to the original, is simpler and easier to understand. Keywords: The modal mu-calculus, completeness, omega-automata.
Lift & Project Systems Performing on the Partial-Vertex-Cover Polytope
Venue: arXiv cs.DS 2020
Year: 2020

We study integrality gap (IG) lower bounds on strong LP and SDP relaxations derived by the Sherali-Adams (SA), Lovasz-Schrijver-SDP (LS+), and Sherali-Adams-SDP (SA+) lift-and-project (L&P) systems for the t-Partial-Vertex-Cover (t-PVC) problem, a variation of the classic Vertex-Cover problem in which only t edges need to be covered. t-PVC admits a 2-approximation using various algorithmic techniques, all relying on a natural LP relaxation. Starting from this LP relaxation, our main results assert that for every epsilon > 0, level-Theta(n) LPs or SDPs derived by all known L&P systems that have been used for positive algorithmic results (but the Lasserre hierarchy) have IGs at least (1-epsilon)n/t, where n is the number of vertices of the input graph. Our lower bounds are nearly tight. Our results show that restricted yet powerful models of computation derived by many L&P systems fail to witness c-approximate solutions to t-PVC for any constant c, and for t = O(n). This is one of the very few known examples of an intractable combinatorial optimization problem for which LP-based algorithms induce a constant approximation ratio, still lift-and-project LP and SDP tightenings of the same LP have unbounded IGs. We also show that the SDP that has given the best algorithm known for t-PVC has integrality gap n/t on instances that can be solved by the level-1 LP relaxation derived by the LS system. This constitutes another rare phenomenon where (even in specific instances) a static LP outperforms an SDP that has been used for the best approximation guarantee for the problem at hand. Finally, one of our main contributions is that we make explicit of a new and simple methodology of constructing solutions to LP relaxations that almost trivially satisfy constraints derived by all SDP L&P systems known to be useful for algorithmic positive results (except the La system).
Spaced Repetition and Mnemonics Enable Recall of Multiple Strong Passwords
Venue: arXiv cs.CR 2020
Year: 2020

We report on a user study that provides evidence that spaced repetition and a specific mnemonic technique enable users to successfully recall multiple strong passwords over time. Remote research participants were asked to memorize 4 Person-Action-Object (PAO) stories where they chose a famous person from a drop-down list and were given machine-generated random action-object pairs. Users were also shown a photo of a scene and asked to imagine the PAO story taking place in the scene (e.g., Bill Gates---swallowing---bike on a beach). Subsequently, they were asked to recall the action-object pairs when prompted with the associated scene-person pairs following a spaced repetition schedule over a period of 127+ days. While we evaluated several spaced repetition schedules, the best results were obtained when users initially returned after 12 hours and then in $1.5\times$ increasing intervals: 77% of the participants successfully recalled all 4 stories in 10 tests over a period of 158 days. Much of the forgetting happened in the first test period (12 hours): 89% of participants who remembered their stories during the first test period successfully remembered them in every subsequent round. These findings, coupled with recent results on naturally rehearsing password schemes, suggest that 4 PAO stories could be used to create usable and strong passwords for 14 sensitive accounts following this spaced repetition schedule, possibly with a few extra upfront rehearsals. In addition, we find that there is an interference effect across multiple PAO stories: the recall rate of 100% (resp. 90%) for participants who were asked to memorize 1 PAO story (resp. 2 PAO stories) is significantly better than the recall rate for participants who were asked to memorize 4 PAO stories. These findings yield concrete advice for improving constructions of password management schemes and future user studies.
A Topological Approach to Spectral Clustering
Venue: arXiv cs.LG 2020
Year: 2020

We propose two related unsupervised clustering algorithms which, for input, take data assumed to be sampled from a uniform distribution supported on a metric space $X$, and output a clustering of the data based on the selection of a topological model for the connected components of $X$. Both algorithms work by selecting a graph on the samples from a natural one-parameter family of graphs, using a geometric criterion in the first case and an information theoretic criterion in the second. The estimated connected components of $X$ are identified with the kernel of the associated graph Laplacian, which allows the algorithm to work without requiring the number of expected clusters or other auxiliary data as input.
Calibrations Scheduling Problem with Arbitrary Lengths and Activation Length
Venue: arXiv cs.DS 2020
Year: 2020

Bender et al. (SPAA 2013) have proposed a theoretical framework for testing in contexts where safety mistakes must be avoided. Testing in such a context is made by machines that need to be often calibrated. Given that calibration costs, it is important to study policies minimizing the calibration cost while performing all the necessary tests. We focus on the single-machine setting and we extend the model proposed by Bender et al. by considering that the jobs have arbitrary processing times and that the preemption of jobs is allowed. For this case, we propose an optimal polynomial time algorithm. Then, we study the case where there are several types of calibrations with different lengths and costs. We first prove that the problem becomes NP-hard for arbitrary processing times even when the preemption of the jobs is allowed. Finally, we focus on the case of unit-time jobs and we show that a more general problem, where the recalibration of the machine is not instantaneous but takes time, can be solved in polynomial time.
Time-Space Trade-offs for Triangulations and Voronoi Diagrams
Venue: arXiv cs.CG 2020
Year: 2020

Let $S$ be a planar $n$-point set. A triangulation for $S$ is a maximal plane straight-line graph with vertex set $S$. The Voronoi diagram for $S$ is the subdivision of the plane into cells such that all points in a cell have the same nearest neighbor in $S$. Classically, both structures can be computed in $O(n \log n)$ time and $O(n)$ space. We study the situation when the available workspace is limited: given a parameter $s \in \{1, \dots, n\}$, an $s$-workspace algorithm has read-only access to an input array with the points from $S$ in arbitrary order, and it may use only $O(s)$ additional words of $Θ(\log n)$ bits for reading and writing intermediate data. The output should then be written to a write-only structure. We describe a deterministic $s$-workspace algorithm for computing an arbitrary triangulation of $S$ in time $O(n^2/s + n \log n \log s )$ and a randomized $s$-workspace algorithm for finding the Voronoi diagram of $S$ in expected time $O((n^2/s) \log s + n \log s \log^*s)$.