Search

A precoding technique based on polar codes and probabilistic shaping is introduced for dirty paper coding. Two variants of the precoding use multi-level shaping and sign-bit shaping in one dimension. The decoder uses multi-stage successive-cancellation list decoding with list-passing across the bit levels. The approach achieves approximately the same frame error rates as polar codes with multi-level shaping over standard additive white Gaussian noise channels at a block length of 256 symbols and with different amplitude shift keying (ASK) constellations.

In this paper, the implementation of artificial neural networks (multilayer perceptron [MLP] and radial base functions [RBF]) and the upgraded Markov chain model have been studied and performed to identify the human behavior patterns during rock, paper, and scissors game. The main motivation of this research is the design and construction of an intelligent robot with the ability to defeat a human opponent. MATLAB software has been used to implement intelligent algorithms. After implementing the algorithms, their effectiveness in detecting human behavior pattern has been investigated. To ensure the ideal performance of the implemented model, each player played with the desired algorithms in three different stages. The results showed that the percentage of winning computer with MLP and RBF neural networks and upgraded Markov model, on average in men and women is 59%, 76.66%, and 75%, respectively. Obtained results clearly indicate a very good performance of the RBF neural network and the upgraded Markov model in the mental modeling of the human opponent in the game of rock, paper, and scissors. In the end, the designed game has been employed in both hardware and software which include the Zana intelligent robot and a digital version with a graphical user interface design on the stand. To the best knowledge of the authors, the precision of novel presented method for determining human behavior patterns was the highest precision among all of the previous studies.

Oil-paper insulation is the key insulation structure of the transformers, whose aging condition is closely related to the operations of the equipment. The degree of polymerization (DP) is the direct parameter characterizing the aging condition of oil-paper insulation. Recently, the near infrared spectroscopy (NIRS) measurement powered by quantitative analysis is used to evaluate DP of the insulating papers mainly. While the applications of NIRS for DP evaluation is constrained because the present spectral quantitative analysis method is not accurate and stable enough, especially for onsite tests. In this paper, we propose a Gaussian process regression (GPR) method to predict DP of the oil- paper insulation in laboratory as well as in field. Firstly, the basic principles of GPR algorithm are illustrated. A GPR model for DP prediction is established based on the spectra of differently aged insulating paper samples which are prepared in laboratory. The GPR model show a high prediction accuracy both for training set and testing set. The established GPR model is finally applied on the DP prediction of insulating papers originating from a de-tanked transformer. The accurate predicted DP and the reliable aging assessment results indicate that the established model can be implemented on site.

Dirty paper coding is an interference pre-cancellation method for known interference at the transmitter and serves as a basic building block in the digital watermarking system. In this letter, we investigate the dirty paper model in the simplest digital communication system, where both the codeword and the interference are binary. For watermark embedment, we derive the relevant coding, the constant coding, and the symmetric relevant coding when the encoder focuses on the binary codeword and interference. The channel capacity is analyzed and the optimal parameter is discussed in the case.

This article considers the severe co-channel interference caused by the efficient use of spectrum by multiple beams combined with full-frequency multiplexing. After establishing a forward link model that considers severe rainfall attenuation in higher frequency bands such as Ka, the classic low-complexity precoding algorithm for zero-forcing is improved, and a regularized zero-forcing precoding algorithm considering the influence of system noise is proposed. Based on the dirty paper coding idea, a low-complexity dirty paper regularization zero-forcing precoding algorithm is proposed, which maximizes the signal-to-interference and noise ratio, thereby increasing throughput.

Traction transformer is vital equipment in high-speed railway. Insulation status decides its safety and reliability, and the frequency-domain dielectric spectrum (FDS) test is one of the most effective methods reflecting the changing of insulation status. For field applications, the following problems should be addressed: 1) how to obtain the result of paper insulation from the combined result of the oil–paper insulation system and 2) how to discriminate the defects of insulation paper between aging and damp. In this article, the first problem was transferred to a nonlinear equation set, and a cuckoo search algorithm optimized by the differential evolution algorithm and the quadratic interpolation (QI) method (CS-DQ algorithm) was proposed to solve it. Then, the insulation states were discriminated by establishing a multiclass least-squares support vector machine (LS-SVM) model, in which the CS-DQ algorithm was also used. Finally, a diagnostic approach for the insulation paper in the traction transformer was proposed. The results in the laboratory show that the pure result of insulation paper can be obtained, and the insulation states can be discriminated effectively by using the proposed approach. Meanwhile, the proposed CS-DQ algorithm has a better performance than the conventional CS algorithm. The results of field testing also verify the proposed approach.

Machine learning, a branch of artificial intelligence, is increasingly used in health research, including nursing and maternal outcomes research. Machine learning algorithms are complex and involve statistics and terminology that are not common in health research. The purpose of this methods paper is to describe three machine learning algorithms in detail and provide an example of their use in maternal outcomes research. The three algorithms, classification and regression trees, least absolute shrinkage and selection operator, and random forest, may be used to understand risk groups, select variables for a model, and rank variables’ contribution to an outcome, respectively. While machine learning has plenty to contribute to health research, it also has some drawbacks, and these are discussed as well. In order to provide an example of the different algorithms’ function, they were used on a completed cross-sectional study examining the association of oxytocin total dose exposure with primary cesarean section. The results of the algorithms are compared to what was done or found using more traditional methods.

Dirty paper coding (DPC) offers an information-theoretic result for pre-cancellation of known interference at the transmitter. In this letter, we propose practical DPC schemes that use only one error correction code. Our designs focus on practical use from the viewpoint of complexity. For fair comparison with previous schemes, we compute the complexity of proposed schemes by the number of operations used. Simulation results show that compared to previous DPC schemes, the proposed schemes require lower transmission power to maintain the bit error rate to be within $10^{-5}$ .

The assignment of reviewers to papers is one of the most important and challenging tasks in organizing scientific conferences and a peer review process in general. It is a typical example of an optimization task where limited resources (reviewers) should be assigned to a number of consumers (papers), so that every paper should be evaluated by highly competent, in its subject domain, reviewers while maintaining a workload balancing of the reviewers. This article suggests a heuristic algorithm for automatic assignment of reviewers to papers that achieves accuracy of about 98–99% in comparison to the maximum-weighted matching (the most accurate) algorithms, but has better time complexity of Θ(n2). The algorithm provides an uniform distribution of papers to reviewers (i.e. all reviewers evaluate roughly the same number of papers); guarantees that if there is at least one reviewer competent to evaluate a paper, then the paper will have a reviewer assigned to it; and allows iterative and interactive execution that could further increase accuracy and enables subsequent reassignments. Both accuracy and time complexity are experimentally confirmed by performing a large number of experiments and proper statistical analyses. Although it is initially designed to assign reviewers to papers, the algorithm is universal and could be successfully implemented in other subject domains, where assignment or matching is necessary. For example: assigning resources to consumers, tasks to persons, matching men and women on dating web sites, grouping documents in digital libraries and others.

In this paper, we propose a novel paper currency defect detection algorithm using quaternion uniform strength. We first build paper currency image preprocessing integration framework which includes intensity balancing, paper currency location, and geometric correction. We then propose a global–local paper currency image registration algorithm by moving key areas within certain range which can eliminate the false difference effectively. Finally, the quaternion uniform strength is calculated by using quaternion convolution edge detector. The defect degree of paper currency is determined by using the quaternion uniform color difference. The proposed algorithm is tested using different datasets from five countries: CNY, USD, EUR, VND, and RUB. The experimental results demonstrate that the proposed algorithm yields better results than the existing state-of-the-art paper currency defect detection techniques. The demo of the proposed paper currency defect detection algorithm will be publicly available.

Over the last decade, huge number of time series stream data are continuously being produced in diverse fields, including finance, signal processing, industry, astronomy and so on. Since time series data has high-dimensional, real-valued, continuous and other related properties, it is of great importance to do dimensionality reduction as a preliminary step. In this paper, we propose a novel online segmentation algorithm based on the importance of TPs to represent the time series into some continuous subsequences and maintain the corresponding local temporal features of the raw time series data. To demonstrate the advantage of our proposed algorithm, we provide extensive experimental results on different kinds of time series datasets for validating our algorithm and comparing it with other baseline methods of online segmentation.

At ASIACRYPT 2018, Castryck, Lange, Martindale, Panny and Renes proposed CSIDH, which is a key-exchange protocol based on isogenies between elliptic curves, and a candidate for post-quantum cryptography. However, the implementation by Castryck et al. is not constant-time. Specifically, a part of the secret key could be recovered by the side-channel attacks. Recently, Meyer, Campos, and Reith proposed a constant-time implementation of CSIDH by introducing dummy isogenies and taking secret exponents only from intervals of non-negative integers. Their non-negative intervals make the calculation cost of their implementation of CSIDH twice that of the worst case of the standard (variable-time) implementation of CSIDH. In this paper, we propose a more efficient constant-time algorithm that takes secret exponents from intervals symmetric with respect to the zero. For using these intervals, we need to keep two torsion points on an elliptic curve and calculation for these points. We implemented our algorithm by extending the implementation in C of Meyer et al. (originally from Castryck et al.). Then our implementation achieved 152.8 million clock cycles, which is about 29.03% faster than that of Meyer et al.

This companion paper supports the experimental replication of the paper "Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment'' presented at ACM Multimedia 2020. We provide the software package for replicating the implementation of the "Norm-in-Norm'' loss and the corresponding "LinearityIQA'' model used in the original paper. This paper contains the guidelines to reproduce all the experimental results of the original paper.

The present work presents a hybrid identification of recursive least squares with a metaheuristic called radial movement optimization (RMO) applied to the joints of a cylindrical robotic manipulator. The main contribution of the research is to consider the covariance matrix with RMO. At the end a comparative analysis will be made with some classic methods. The results of the identifications were tested with the RMSE. A covariance matrix has also been generated from manipulator identifications.

Background Electrocardiograms (ECGs) are routinely recorded and stored in a variety of paper or scanned image format. Current ECG recording machines record ECG on graph paper, also it provides digitized ECG signal along with automated cardiovascular diagnosis (CVD). However, such recording machines cannot analyse preserved paper ECG records as it requires input in terms of digitized signal. Therefore, it is important to extract ECG signal from these preserved paper ECG records using digitization method. There are different paper degradations that adversely affect digitization process. The purpose of this work is to perform an image enhancement and digitization of the degraded ECG images to extract continuous ECG signal. Methods In this paper, we propose entropy-based bit plane slicing (EBPS) algorithm in which pre-processing is done using dominant color detection and local bit plane slicing. Maximum entropy based adaptive bit plane selection is applied to the pre-processed image. Discontinuous ECG correction (DECGC) is then done to produce continuous ECG signal. Results The algorithm is tested on 836 different degraded paper ECG records obtained from various diagnostic centers. After analysis with 101 known ground truth ECG signals the accuracy, sensitivity, specificity and overall F-measure of ECG is 99.42%, 99.69%, 99.81% and 99.26% respectively. The RMS error and correlation between the extracted digitized signal and ground truth for 101 cases is 0.040 and 99.89% respectively. Conclusions The EBPS method is able to remove all types of degradation in paper ECG records to generate a uniform digitized signal. Instead of manual measurement and prediction from archived paper ECG records, automated prediction (using already existing cardiovascular diagnosis software) is possible with the help of extracted digitized signal obtained using proposed digitization method, which will also help retrospective cardiovascular analysis.

This article centers on the specific algorithm, devised to help students select the theme of the qualifying paper. The article also reviews the contents of the customs affairs specialists qualifying paper. The method of selecting the theme of the qualifying paper using the mind-map is offered. The Mind-map offers a convenient scheme for a student to select his or her own research advisor, work experience placement, object and subject of the qualifying paper. This article represents an analysis of example of student's qualifying paper on currency control theme choice, lays out the contents of theoretical and practical parts of the qualifying paper named “Institutional analysis of currency control on the territory of the Chelyabinsk region”. Methods of currency control scientific analysis with the aid of statistical methods and graphic techniques are offered.

DNA computer based algorithm is developed and evaluated for recyclable waste paper segregation.The concepts of replication and massive parallelism operations are used.The matching stage consists of Copy, Extract, Detect and Merge or Union operations.Gel electrophoresis operation is used to identify the candidate paper object grade.Success rate are 92% for WP, 90% for ONP and 93% for OCC with template size 5i?5. This article explores the application of DNA computing in recyclable waste paper sorting. The primary challenge in paper recycling is to obtain raw materials with the highest purity. In recycling, waste papers are segregated according to their various grades, and these are subjected to different recycling processes. Highly sorted paper streams facilitate high quality end products, while saving on processing chemicals and energy. In the industry, different sensors are used in paper sorting systems, namely, ultrasonic, lignin, gloss, stiffness, infra-red, mid-infra red, and color sensors. Different mechanical and optical paper sorting systems have been developed based on the different sensors. However, due to inadequate throughput and some major drawbacks related to mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. The automated paper sorting systems offer significant advantages over the manual systems in terms of human fatigue, throughput, speed, and accuracy. This research has two objectives: (1) to use a web camera as an image sensor for the vision system in lieu of different sensors; and (2) to develop a new DNA computing algorithm based on the theme of template matching techniques for segregating recyclable waste papers according to paper grades. Using the concepts of replication and massive parallelism operations, the DNA computing algorithm can efficiently reduce the computational time of the template matching method. This is the main strength of the DNA computing algorithm in actual inspections. The algorithm is implemented by using a silicon-based computer to verify the success rate in paper grade identification.

Rapid identification of aging state of oil-paper insulation is of great significance to the operation safety of power transformers. Raman spectroscopy can rapidly analyze the aging characteristic information dissolved in oil, and it is an effective means for the aging diagnosis of oil-paper insulation. In this paper, Multiple screening KNN Algorithms for Raman spectroscopy analysis of aging oil-paper insulation samples is presented. A large number of aging oil samples were obtained by accelerated thermal aging test. According to the aging days, the samples were divided into 12 categories, and 230 Raman spectra were obtained by Raman spectroscopy. The KNN algorithm is used for classification and regression of Raman Spectra of test samples by Pearson correlation coefficient. Then, based on the traditional KNN algorithm, a multi-screening KNN is proposed according to the actual situation of the aging process of insulating oil Raman spectrum. The prediction of Multiple screening KNN Algorithms accuracy of classification reaches 87.92%, and the RMSE of regression reached 54.28.

This paper investigates the reconfigurable intelligent surface (RlS)-aided multiple-input-single-output (MISO) systems with imperfect channel state information (CSI), where RIS-related channels are modeled by Rician fading. Considering the overhead and complexity in practical systems, we employ the low-complexity maximum ratio combining (MRC) at the base station (BS), and configure the phase shifts of the RIS based on long-term statistical CSI. Specifically, we first estimate the overall channel matrix based on the linear minimum mean square error (LMMSE) estimator, and evaluate the performance of mean square error (MSE) and normalized MSE (NMSE). Then, with the estimated channel, we derive the closed-form expressions of the ergodic rate. The derived expressions show that with Rician RIS-related channels, the rate can maintain at a non-zero value when the transmit power is scaled down proportionally to 1 /M or 1 /N2, where M and N are the number of antennas and reflecting elements, respectively. However, if all the RIS-related channels are purely Rayleigh, the transmit power of each user can only be scaled down proportionally to $1/\sqrt M $ or 1/N. Finally, numerical results verify the promising benefits from the RIS to traditional MISO systems.

Fault Tree Analysis is widely used in the reliability evaluation of critical systems, such as railway and automotive systems, power grids, and nuclear power plants. While there are efficient algorithms for calculating the probability of failure in static fault trees, Mean Time to First Failure (MTFF) evaluation remains challenging due to state space explosion. Recently, structural and symmetry reduction methods were proposed to counteract this phenomenon. However, systems with a large number of different and highly interconnected components preclude the use of reduction techniques. Their MTFF analysis requires the solution of a system of linear equations whose size is exponential in the number of components in the system. In this paper, we propose a solution leveraging Tensor Trains as a compressed vector and matrix representation. We build upon Binary Decision Diagram-based techniques to avoid explicit state space enumeration and use linear equation solvers developed specifically for Tensor Trains to efficiently solve the arising linear systems. As a result, our novel approach complements the existing reduction-based techniques and makes some previously intractable models possible to analyze. We evaluate our approach on an industrial case study adapted from a railway system, and other openly available benchmark models.

This paper proposes a novel three-dimensional (3D) cluster-based sixth-generation (6G) model for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) channels. In the proposed model, a novel method based on visibility region (VR) is developed, where the smooth and consistent birth and death of clusters in both the time domain and space domain are successfully captured. By using the developed method, the space-time non-stationarity and time-space consistency of channels can be modeled for the first time. Based on the proposed model, important channel statistical properties are derived and thoroughly investigated. Simulation results demonstrate that the smooth birth and death of clusters, time-space consistency, and space-time non-stationarity are modeled sufficiently.

The current paper provides preliminary statements of the panelists ahead of a panel discussion at the ACM SPAA 2021 conference on the topic: algorithm-friendly architecture versus architecture-friendly algorithms.

Brain-inspired hyperdimensional computing (HDC) is an emerging computational paradigm that has achieved success in various domains. HDC mimics brain cognition and lever-ages hyperdimensional vectors with fully distributed holographic representation and (pseudo)randomness. Compared to the traditional machine learning methods, HDC offers several critical advantages, including smaller model size, less computation cost, and one-shot learning capability, making it a promising candidate in low-power platforms. Despite the growing popularity of HDC, the robustness of HDC models has not been systematically explored. This paper presents a study on the robustness of HDC to errors in associative memory—the key component storing the class representations in HDC. We perform extensive error injection experiments to the associative memory in a number of HDC models (and datasets), sweeping the error rates and varying HDC configurations (i.e., dimension and data width). Empirically, we observe that HDC is considerably robust to errors in the associative memory, opening up opportunities for further optimizations. Further, results show that HDC robustness varies significantly with different HDC configurations such as data width. Moreover, we explore a low-cost error masking mechanism in the associative memory to enhance its robustness.

Optimal linear transmitter beamformers in multi-antenna multi-user systems are of the Minimum Mean Squared Error (MMSE) type (dual uplink MMSE receivers). MMSE designs make an optimal compromise between noise enhancement and interference suppression and reduce to matched filters at low SNR and zero-forcing at high SNR. We consider a realistic scenario of user channels of varying attenuation and constrain the beamformers to either zero-force or ignore each interference term. This leads to a reduced-order zero-forcing (RO-ZF) design in which the number of interference sources being zero-forced increases with SNR. We apply a simple large systems analysis (applicable to Massive MIMO) to determine the asymptotic performance of RO-ZF designs, determine the optimal ZF orders, and compare to optimal and ZF linear and Dirty Paper Coding (DPC) designs. RO-ZF designs lead to variable reductions of computational complexity and channel state information (CSI) requirements (esp. in future multi-cell extensions), both important considerations in Massive MIMO systems.

This paper studies the second-order coding rates for memoryless channels with a state sequence known non-causally at the encoder. In the case of finite alphabets, an achievability result is obtained using constant-composition random coding, and by using a small fraction of the block to transmit the empirical distribution of the state sequence. For error probabilities less than 0.5, it is shown that the second-order rate improves on an existing one based on independent and identically distributed random coding. In the Gaussian case (dirty paper coding) with an almost-sure power constraint, an achievability result is obtained using random coding over the surface of a sphere, and using a small fraction of the block to transmit a quantized description of the state power. It is shown that the second-order asymptotics are identical to the single-user Gaussian channel of the same input power without a state.

This article describes how with the advent of computer-based technology, there is movement from manual to automated systems for different aspects of the education system. Testing is an essential part of teaching process that helps academics in classifying the level of students and evaluating the outcomes of their teaching process. The testing process requires a large amount of attention and professionalism. Automated Test Paper Generation is a system applying the shuffling algorithm in designing different sets of questions without repetition and duplication. It helps the faculty in developing and designing exams with a particular level of difficulty required in evaluating the students by using the utility-based agent. The system includes a knowledge base of many questions' types that are linked to a test engine where the faculty can specify the type and the difficulty level of the exam and then the system will assemble the exam and produce the output as electronic or paper-based. Questions will be picked randomly from the knowledge database. This automated system provides cost saving and time efficient solutions.

Analysis of images with deep learning and machine learning has recently show more accuracy in medical image processing. The paper review the major machine learning methods is used for the extraction of retinal fundus image. It describes how hardware has been modified according to time for retinal eye segmentation that device is helpful for the environment and also useful for humans The algorithms used for image extraction, clear and noise-free Images of different color scales are used to improve image quality. The paper gives a review of how researchers and scientists have been trying different methods to improve vessel segmentation in the future.

One of the most essential investigation demands in the computational evaluation of scientific publications is whether the immense collections of scientific papers hold significant indication about the dynamics included in the development of science; signs that may help predict the growth and decline of scientific methods, ideas, and even fields. The research presented in this paper focuses on a general approach to analyze and predict the thematic evolution of a given research field by pointing out uptrend keywords. In particular, we propose a dataflow of method and a paper ranking algorithm. This results in ranking papers, from there we select the best 20 papers and extract from them meaningful keywords (concerned with research field/subfield, algorithms, methods, etc.). After that, we formulate the final score for keywords by summing up scores of papers containing them and then group the obtained results by year. Therefore, we can demonstrate scores of keywords through years in time series and observe which keywords are displaying an upward tendency. As a case study, the proposed approach is applied to analyze the thematic evolution of the Artificial Intelligence research field in the period 2005–2016 from the Web of Science database. Ultimately the method is evaluated by checking occurrences of predicted keywords in true prominent keywords in timeframe 2017–2019 and provides precision 73.33%.

Neural networks (NNs) are a driving force behind the ongoing information revolution, with a broad spectrum of applications affecting most aspects of science and technology. The interest in robust neural computation under adversarial noise has increased lately, due applications in sensitive tasks ranging from healthcare to finance and autonomous vehicles. This has ignited an influx of research on the topic, which for the most part focuses on obtaining robustness by altering the training process. In contrast, this paper surveys and develops a recently proposed novel approach to obtain robustness after training, by adding redundancy to the network and to the data in the form of error correcting codes.Since neural networks are essentially a concatenation of linear classifiers, we focus on obtaining robustness for a single linear classifier by coding the input and the classifier, and then apply the results on the network. We address two different types of adversaries, a worst-case one and an average-case one. For a worst-case adversary, that can choose the input to be attacked, we focus on binarized classifiers and show that the problem is related to construction of certain linear codes with restricted weight patterns. As a result, it is shown that the parity code can obtain robustness against any 1-erasure in any binarized NN, and no decoding is required. For an average-case adversary, that is given a uniformly random input to be attacked, it is shown that the optimal weights for any classifier and any code are given by the Fourier coefficients of that classifier. We demonstrate the latter experimentally, exposing improved accuracy-robustness tradeoff in neural classification of several popular datasets under state-of-the-art attacks.

International audience; Reconfigurable intelligent surfaces (RISs) represent a new technology that can shape the radio wave propagation and thus offers a great variety of possible performance and implementation gains. Motivated by this, we investigate the achievable sum-rate optimization in a broadcast channel (BC) that is equipped with an RIS. We exploit the well-known duality between the Gaussian multiple-input multiple-output (MIMO) BC and multiple-access channel (MAC) to derive an alternating optimization (AO) algorithm which jointly optimizes the users’ covariance matrices and the RIS phase shifts in the dual MAC. The optimal users’ covariance matrices are obtained by a dual decomposition method in which each iteration is solved in closed-form. The optimal RIS phase shifts are also computed using a derived closed-form expression. Furthermore, we present a computational complexity analysis for the proposed AO algorithm. Simulation results show that the proposed AO algorithm can provide significant achievable sum-rate gains in a BC.

The popular technique of secure signaling over Gaussian MIMO wiretap channels, which makes use of artificial noise (AN) to increase secrecy rates, is considered. First, we briefly review the current state of affairs in this area and then provide new analytical results and insights on the usefulness of AN (jamming) to boost secrecy rates. The settings considered here go beyond the total transmit power constraint and include a number of additional constraints, such as interference (cognitive radio) and energy-harvesting constraints, for which the feasible set is not isotropic anymore so that the standard tools of the analysis cannot be used. By closely examining optimal precoding for the information-bearing and AN signals, we identify a number of cases where it is optimal to transmit no artificial noise at all (so that all the transmit power goes to the information-bearing signal). These cases include a fixed (no fading) MIMO WTC with single eavesdropper (for which we give a direct matrix-theoretic proof using novel matrix inequalities), multi-eavesdropper (com-pound) degraded and reversely-degraded channels, and multi-eavesdropper channels where there exists a dominant eavesdropper (for which we give a precise definition) or when the eavesdroppers collude. To improve secrecy rates by using AN, one has to look elsewhere.

The idea of this study is to validate a list of keywords derived from a scientific article by a domain expert from years of knowledge with prominent document similarity algorithms. For this study, a list of handcrafted keywords generated by Electric Double Layer Capacitor (EDLC) experts are chosen, and relevant documents to EDLC are considered for the comparison. Then, different similarity calculation algorithms were employed in different settings on the documents such as using the whole texts of the documents, selecting the positive sentences of the documents, and generating similarity score with automatically extracted keywords from the documents. The experiment’s outcome provides us with findings that the machine-generated keywords are mostly similar to the curated list by the domain experts. This study also suggests the preferable algorithms for similarity calculation and automated key-phrase extraction for the EDLC domain.

A new algorithm for intelligent test based on genetic algorithm is proposed in this paper. Firstly, this algorithm improves the basic genetic algorithm for two times, furthermore, the cross-probability and variation-probability of the genetic algorithm can be adjusted according to the degree of individual adaption. In this way, the individual structure of high fitness is not destroyed, and the phenomenon of slow search speed is overcome, thereby it effectively improves the speed and quality of the test paper. The experimental results show that the improved algorithm significantly improves the global optimization ability and speed, it has high robustness.

In this paper (This is a short paper accepted in the new ideas and work-in-progress section of SEFM 2017.) we introduce a technique to improve the efficiency of SAT calls in Bounded Model Checking (BMC) problems. The proposed technique is based on exploiting interpolation-based invariants as redundant constraints for BMC.

The uniformity of paper sheets is a key factor which not only affects almost all physical, optical, and printing properties of paper sheets, but also influences the end users’ experience and perception. The uniformity of paper sheets is a great concern for paper manufacturers. However, the methods utilized to measure the uniformity of paper sheets are either performed offline in the quality control laboratory, or performed on line but not effective and fast enough considering the high production rates of paper making. It is imperative to explore effective ways which can be applied on line to achieve real time measurement of paper formation. In this paper, the uniformity of paper sheets was evaluated using K-means algorithm which is one of fastest clustering methods. A paper sample subjected to transmitted light was inspected to identify its uniformity according to the gray level of each pixel. The pixels were allocated into four clusters using K-means. The distribution of pixels of each cluster was analyzed, and the dispersion index was calculated to indicate the uniformity of the paper sample.

The condition of the transformer insulation has an impact on the transformer’s performance during the operation. The aging of the oil-impregnated cellulose insulation and the associated loss of mechanical strength are the important factors that limit the life of expectancy of a transformer. To determine the condition of the oil-impregnated cellulose insulation, the Degree of Polymerization (DP) parameter is commonly used. An Adaptive Neuro-Fuzzy Inference System (ANFIS) has been developed to predict the DP Value by the chemical characteristics and dissolved gas parameters (acidity, interfacial tension, CO, CO 2 , breakdown voltage, and water content of the oil). This paper generates some algorithms which are based on the input space partitioning method to generate rules (grid partition or subtractive clustering) and data is normalized or not. The estimation result has been observed and evaluated to provide that the ANFIS algorithm is suitable to estimate insulation condition on field operating transformers.

Hyperproperties generalize trace properties by expressing relations between multiple computations. Hyperpropertes include policies from information-flow security, like observational determinism or non-interference, and many other system properties including promptness and knowledge. In this paper, we give an overview on the model checking problem for temporal hyperlogics. Our starting point is the model checking algorithm for HyperLTL, a reduction to Buchi automata emptiness. This basic construction can be extended with propositional quantification, resulting in an algorithm for HyperQPTL. It can also be extended with branching time, resulting in an algorithm for HyperCTL*. However, it is not possible to have both extensions at the same time: the model checking problem of HyperQCTL* is undecidable. An attractive compromise is offered by MPL[E], i.e., monadic path logic extended with the equal-level predicate. The expressiveness of MPL[E] falls strictly between that of HyperCTL* and HyperQCTL*. MPL[E] subsumes both HyperCTL* and HyperKCTL*, the extension of HyperCTL* with the knowledge operator. We show that the model checking problem for MPL[E] is still decidable.

Multivariate cryptography plays an important role in post-quantum cryptography. Many signature schemes, such as Rainbow remain secure despite the development of several attempted attack algorithms. However, most multivariate signature schemes use relatively large public keys compared with those of other post-quantum signature schemes. In this paper, we present an approach for constructing a multivariate signature scheme based on matrix multiplication. At the same security level, our proposed signature scheme has smaller public key and signature sizes compared with the Rainbow signature scheme.

K-limited path-sensitive interval domain is an abstract domain that has been proposed for precise and scalable analysis of large software systems. The domain maintains variables’ value ranges in the form of intervals along a configurable K subsets of paths at each program point, which implicitly provides co-relation among variables. When the number of paths at the join point exceeds K, the set of paths are partitioned into K subsets, arbitrarily, which results in loss of precision required to verify program properties. To address this problem, we propose selective merging of paths - identify and merge paths in such a way that the intervals computed help verifying more properties. Our selective path-sensitive approach is based on the knowledge of variables whose values influence the verification outcomes of program properties. We evaluated our approach on industrial automotive applications as well as academic benchmarks. We show benefits of selective path merging over arbitrary path selection by verifying 40% more properties.

Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.