Reviewerly/exHarmony huggingface dataset:
Reconstructing a single-head formula to facilitate logical forgetting

Abstract Logical forgetting is removing some variables from a formula while maintaining its consequences on the remaining variables. This removal may require exponential time on arbitrary propositional Horn formulae, but it only takes polynomial time on single-head propositional definite Horn formulae. Single-head means that no variable is the head of multiple clauses. An algorithm to make a formula single-head if possible is shown. It enlarges the set of formulae admitting polynomial-time forgetting by making them single-head if possible and then running the usual algorithm for forgetting.
Solving a Multi-Traveling Salesmen Problem using a Mamdani Fuzzy Inference Engine and Simulated Annealing Search Algorithm

The multi-traveling salesmen problem (MTSP) is an extended situation of the standard traveling salesman problem (TSP), in which there is more than one salesman. In this matter, several salesmen are determined to visit N city with the goal of the shortest route to selling their goods, assuming they have crossed all of them, via just once each. The aim is to minimize the total cost of travel for salesman. Thus, it can be modeled as an optimization problem. Regarding the complexity degree, this problem is well known as a NP-Hard problem. Therefore, several meta-heuristic algorithms have been developed at the frontiers of knowledge to solve this problem. Nevertheless, the computational and time complexities are the most important challenges of such algorithms. In this paper, we first convert the MTSP using a fuzzy approach with a linear complexity level, to several TSPs. Then, we solve each problem using the simulated annealing (SA) optimization algorithm. In this way, the time complexity of the system is significantly reduced using the proposed method as well as the accuracy of the system is satisfactory. To assess the proposed algorithm, this method is implemented in the TSPLIB library dataset.
Acceptance of Virtual Health Avatars

We investigated the acceptance of virtual health avatars in geriatrics and gerontology, prevention and rehabilitation. We produced simulation videos for several scenarios and evaluated the acceptance with focus groups. The following research objectives were addressed: 1) the assessment of the acceptance of virtual health avatars 2) naming of the reasons for approval and rejection of the application of digital health technologies using the example of health avatars.The results obtained on the basis of the focus group discussions reveal a number of positive perspectives. The research results allow the identification of concerns towards the implementation of virtual health avatars and give hints how to improve interfaces for future data driven medicine.
Adaptive Multi-objective Particle Swarm optimization Based on Competitive Learning

In the evolutionary process of multi-objective optimization algorithm, the selection of the guide has a great influence on the flying state of particles. A more accurate guide selection strategy can enhance the convergence faculty and maintain the population's diversity. This article introduces an improved multi-objective optimization algorithm (AMPSO). Its core innovation is the introduction of competition. Firstly, the evolution process is divided into three states according to the different flight conditions of particles: convergence state, exploration state, and stagnation state. At the same time, the traditional g-best algorithm is divided into three kinds: c-gbst, d-gbest, and s-gbest. Then, according to the evolution state of particles, different leaders are selected. When the particles are in convergence state, c-gbest is selected as the leader to improve the convergence ability of the population. When the particles are in exploration state, d-gbest is selected as the leader to improve the diversity of particles. When the particles are in stagnation state, s-gbest is selected as the leader to guide the population out of the local area optimal. Compared with the other four modified multi-objective algorithms, we have carried out a series of experimental simulations of 10 benchmark functions. Experimental results reveal that the algorithm in this paper performs better.
Prosodic Information-Assisted DNN-based Mandarin Spontaneous-Speech Recognition

This paper continues the method proposed in [1] and updates its traditional HMM-based ASR to state-of-the-art DNN-based ASR. Use prosodic information to assist state-of-the-art DNN-based Mandarin spontaneous-speech recognition, especially to alleviate the serious interference of annoying disfluencies and paralinguistic phenomena during decoding. This approach adopts a sophisticated hierarchical prosodic model (HPM) made of several break-syntax, break-acoustic, syllable prosodic and prosodic state models to rescore and improve the TDNN-f+RNNLM-based 1st pass decoding output and generate, at the same time, the word, Part of Speech (POS), Punctuation Mark (PM), tone, break type, and prosodic state tags for further use. Experimental results showed the HPM-based system not only dramatically reduced the word error rate from previous best value: 41.8% [1] to 21.2%. It also detected well the underlying POS, PMs, and tones (10.9%, 12.6%, and 2.3% error rates were achieved, respectively). This confirms that the proposed method is very promising on tackling the task of Mandarin spontaneous-speech recognition.
A Human Behavior Recognition Method for Home Service Robot

In view of the limited computing power of the processors available for service robots and the complex illumination changes and shadows in the home environment, we propose a method of human behavior recognition based on skeleton model and depth image in this paper. We define several key actions for the sequence of actions, then optimizes the actions of each kind of actions, and finally completes the behavior recognition by the similarity between the measured and the template. This experiment is carried out on a general configuration processor to simulate a service robot processor. The experimental results show that the proposed method achieves a high recognition rate for eight kinds of behavior, such as brushing teeth, standing, etc. and a stable recognition effect can be obtained under the condition of changing illumination.
Change detection in streaming data analytics: A comparison of Bayesian online and martingale approaches

On line change detection is a key activity in streaming analytics, which aims to determine whether the current observation in a time series marks a change point in some important characteristic of the data, given the sequence of data observed so far. It can be a challenging task when monitoring complex systems, which are generating streaming data of significant volume and velocity. While applicable to diverse problem domains, it is highly relevant to monitoring high value and critical engineering assets. This paper presents an empirical evaluation of two algorithmic approaches for streaming data change detection. These are a modified martingale and a Bayesian online detection algorithm. Results obtained with both synthetic and real world data sets are presented and relevant advantages and limitations are discussed.
A Noncoherent Incremental Learning Demodulator

Incremental learning after deployment is one of several attractive capabilities that motivate the use of neural network demodulators. This paper presents a complex noncoherent neural network suitable for on-off key (OOK) demodulation. When trained in an AWGN channel, the demodulator learns a solution that outperforms the traditional noncoherent matched filter demodulator. The paper also explores incremental learning techniques that enable continued learning in the field. Training in the field with known labels provides maximum adaptability to new conditions, but the availability of known symbols maybe limited. As an alternative, we considered the effectiveness of entropy regularization and pseudo-labels to adapt a lab-trained reference network to new field conditions. Simulation of these techniques in an example multipath channel demonstrates successful unsupervised adaptation with initial symbol error rates up to 20% and successful semi-supervised adaptation with a small fraction of known symbols per packet and initial symbol error rates as high as 40%. In both cases, symbol error rates after adaptation are below 0.3%.
Automated Cognitive Analyses for Intelligent Tutoring Systems

Designing an Intelligent Tutoring System (ITS) that simulates human learning with regard to different knowledge levels is a challenge as it reflects an accurate way of estimating the students’ performance level. Most developed ITSs typically focus on the normal cognitive factors such as the students’ prior success and failure scores without paying appropriate consideration to the sensitive cognitive factors that have a great impact on the student performance prediction such as the integration of the human current skills and given items skills, particularly when the learning items require multiple skills, which thus reduce student’s learning efficiency due to an incomplete representation of the student’s knowledge. This paper presents a modified student modeling approach, called modified Performance Factor Analysis (ModPFA), based on a previously developed model called Performance Factor Analyses (PFA). ModPFA was developed by adding the hinting parameter to the original PFA formula. This extension has scoring procedure and knowledge level estimation for each student. Results have shown great improvement in terms of performance estimation in the ModPFA compared to PFA.
Central versus Distributed Statistical Computing Algorithms-A Comparison

Distributed statistical learning algorithms are performing many machine learning tasks in a distributed environment. Some scenarios where data sharing is desired among many parties and it may need to increase the efficiency and statistical accuracy of the underlying algorithms. Due to the increase in the size and complexity of today's big data, it is very important to solve problems with a very large number of features, records, and training samples. As a result, it is necessary to deal with the distributed transfer of these datasets as well as their underlying distributed solution methods efficiently and effectively. This paper compares the efficiency and accuracy of a distributed statistical method with a central method with simple regression and classification algorithms.
A Linear Time Pattern Based Algorithm for N-Queens Problem

The n-queens problem is the placing of n number of queens on an nxn chessboard so that no two queens attack each other. This problem is important due to various usage fields such as VLSI testing, traffic control job scheduling, data routing, dead-lock or blockage prevention, digital image processing and parallel memory storage schemes mentioned in the literature. Besides, this problem has been used as a benchmark for developing new Artificial Intelligence search techniques. However, it is known that backtracking algorithms, one of the most frequently used algorithms to solve this problem, cannot produce all solutions in large n values due to the exponentially growing time complexity. Therefore, various methods have been proposed for producing one or more solutions, not all solutions for each n value. In this study, a pattern based approach that produces at least one unique solution for all n values (n&amp;gt;3) was detected. By using this pattern, a new algorithm that produces at least one unique solution for even very large n values in linear time was developed. The developed algorithm with О(n) time complexity produces quite faster solution to n-queens problem and even in some values, this algorithm produces (n-1)/2 unique solutions in linear time.
Unconventionally Designed Tracking Loop Adaptable to Plasma Sheath Channel for Hypersonic Vehicles

An aircraft that moves through the atmosphere at hypersonic speed is covered by plasma sheath, which causes random and fast time-varying amplitude attenuation and phase fluctuation in received signals. This paper comprehensively analyzes the mechanism of the amplitude attenuation effects on a traditional phase-locked loop (PLL), which is always ignored in traditional scenarios (such as satellite telemetry and vehicle communication). Simulation results and theoretical analysis showed that traditional PLL does not work reliably for signal carrier tracking with the severe time-varying amplitude attenuation of the plasma sheath channel. In this paper, an unconventionally designed Kalman filter (KF) tracking loop that is aware of phase dynamics and amplitude attenuation fluctuation for hypersonic vehicles is proposed. To introduce time-varying amplitude attenuation into the proposed KF-based tracking loop, the amplitude attenuation is first modeled with an autoregressive model. The statistical characteristics of the amplitude and phase fluctuation are then incorporated into the state equation and observation equation. Simulation results indicate that the proposed tracking loop is stable when the signal-to-noise ratio is −10 dB with the Ka band, even in the most severe flight environment for hypersonic vehicles.
Personal Identification by Learning-Based Integration of Facial and Clothes Similarities

Face recognition often fails when the target face is hidden or angled. Therefore, entry/exit management systems using face recognition force users to perform some operation or a temporary stop for recognition. In work environments where users frequently enter and leave the room, it is very inconvenient to force such operations and actions. In this research, we develop personal identification methods using facial features and closing features as methods without user operation. We propose several methods that differ in the integration of facial and clothing recognition results. The evaluation results demonstrate the effectiveness of the proposed methods.
Clustering, factor discovery and optimal transport

Abstract The clustering problem, and more generally latent factor discovery or latent space inference, is formulated in terms of the Wasserstein barycenter problem from optimal transport. The objective proposed is the maximization of the variability attributable to class, further characterized as the minimization of the variance of the Wasserstein barycenter. Existing theory, which constrains the transport maps to rigid translations, is extended to affine transformations. The resulting non-parametric clustering algorithms include $k$-means as a special case and exhibit more robust performance. A continuous version of these algorithms discovers continuous latent variables and generalizes principal curves. The strength of these algorithms is demonstrated by tests on both artificial and real-world data sets.
Kinematics and Plane Decomposition Algorithm for Non Linear Path Planning Navigation and Tracking of Unmanned Aerial Vehicles

Abstract Online planned course of the flight and obstacle avoidance are the major issues of exploitation faced in the real time implementation of Unmanned Aerial Vehicles (UAVs). The main application that are used in the UAV are path planning navigation and tracking. Our project uses two algorithms to derive the navigation control laws. The algorithms such as kinematics based algorithm and plane decomposition algorithm. The control laws rely on the two factors namely, proportionality and derivation factors. These factors guide the UAV in reaching the goal in a nonlinear path successfully. The surveillance mode and obstacle avoidance mode are taken as the important consideration in our project. In the surveillance mode UAV reaches the goal in the three dimensional space with the aid of navigation laws. In obstacle avoidance function, the obstacles are avoided in the course of its path of the flight and attains the destination with the navigation laws’ assistance. The kinematics rules leads to the successful application of tracking and navigation of UAV in nonlinear path planning.
A New Method of Improving Performance of Canny Edge Detection

The traditional Canny Edge Detection Method does not perform efficiently when applied to low contrast images. It creates false edges as it cannot isolate the object from the background properly while detecting edges of any image with low contrast. In response to this problem, this paper proposes a solution using which the performance of Canny Edge Detection can be improved significantly. The images subjected to edge detection is pre-processed by stretching the image histogram. Stretching the image histogram using different stretching limits results in processed images with enhanced contrast. We obtained the best result of Canny Edge Detection Method by applying the detection technique on the modified image which has the best image contrast. The edge detected images show visual proof as well as quantitative proof of the improved performance of Canny edge detection using this process.
Gauges, loops, and polynomials for partition functions of graphical models

Abstract Graphical models represent multivariate and generally not normalized probability distributions. Computing the normalization factor, called the partition function, is the main inference challenge relevant to multiple statistical and optimization applications. The problem is #P-hard that is of an exponential complexity with respect to the number of variables. In this manuscript, aimed at approximating the partition function, we consider multi-graph models where binary variables and multivariable factors are associated with edges and nodes, respectively, of an undirected multi-graph. We suggest a new methodology for analysis and computations that combines the Gauge function technique from Chertkov and Chernyak (2006 Phys. Rev. E 73 065102; 2006 J. Stat. Mech. 2006 P06009) with the technique developed in Anari and Oveis Gharan 2017 arXiv: 1702.02937 ; Gurvits 2011 arXiv: 1106.2844 ; Straszak and Vishnoi 2017 55th Annual Allerton Conf. on Communication, Control, and Computing , based on the recent progress in the field of real stable polynomials. We show that the Gauge function, representing a single-out term in a finite sum expression for the partition function which achieves extremum at the so-called belief-propagation gauge, has a natural polynomial representation in terms of gauges/variables associated with edges of the multi-graph. Moreover, Gauge function can be used to recover the partition function through a sequence of transformations allowing appealing algebraic and graphical interpretations. Algebraically, one step in the sequence consists of the application of a differential operator over gauges associated with an edge. Graphically, the sequence is interpreted as a repetitive elimination/contraction of edges resulting in multi-graph models on decreasing in size (number of edges) graphs with the same partition function as in the original multi-graph model. Even though the complexity of computing factors in the sequence of the derived multi-graph models and respective Gauge functions grow exponentially with the number of eliminated edges, polynomials associated with the new factors remain bi-stable if the original factors have this property. Moreover, we show that BP estimations in the sequence do not decrease, each low-bounding the partition function.
Interpretable Underwater Image Enhancement based on Convolutional Neural Network

An underwater image suffers from degradation due to the physical attributes of water. The enhancement of degraded underwater images is an important area of research. Several researchers have been using machine learning-based models for enhancement. But, the network models are solely based on training data and the results are difficult to explain. Here, we present a novel enhancement technique for underwater image utilizing a set of enhancement functions and a Convolutional neural network(CNN). The four functions are blended to create the resultant enhancement function. The proposed network is interpretable in the sense that the work of the four functions are easily understandable and they can efficiently enhance different part of an underwater image. The CNNs are used to tune the parameters of the functions depending on the training data. The performance of the proposed method is quite efficient compared to the recently published methods on standard dataset.
Research Review on Imbalanced Learning Models in Embedded Intelligent Computing

Imbalanced data sets are a common phenomenon in embedded intelligent computing. In other words, the class imbalance occurs when the number of examples representing one class is much lower than the ones of the other classes. Hence, one or more classes may be underrepresented in the dataset. However, standard machine learning algorithms are usually biased toward the majority class, since rules correctly predicting those instances are positively weighted in favor of the accuracy metric or the corresponding cost function. As a consequence, minority class instances are more often misclassified than those from the majority one. One of the main issues in imbalanced problems is that usually, the underrepresented class is the class of interest of the problem from the application point of view. Therefore, there are a large number of researches on imbalanced learning. Compared with the binary class imbalanced problem, the multi-class imbalanced problem faces greater challenges, which is attributed to the diversity of class distribution and the insufficient performance of multi-class classifiers. Therefore, imbalanced problems have received more and more attention in recent years. This paper studies the mainstream imbalance algorithms, classifies the current models and algorithms to solve the imbalance problem, analyzes the advantages and disadvantages of various methods, discusses the performance indicators suitable for imbalance problems and their evaluation bias, and summarizes the research trend of algorithms.
Analysis of Phonetic Dependence of Segmentation Errors in Speaker Diarization

Evaluation of speaker segmentation and diarization normally makes use of forgiveness collars around ground truth speaker segment boundaries such that estimated speaker segment boundaries with such collars are considered completely correct. This paper shows that the popular recent approach of removing forgiveness collars from speaker diarization evaluation tools can unfairly penalize speaker diarization systems that correctly estimate speaker segment boundaries. The uncertainty in identifying the start and/or end of a particular phoneme means that the ground truth segmentation is not perfectly accurate, and even trained human listeners are unable to identify phoneme boundaries with full consistency. This research analyses the phoneme dependence of this uncertainty, and shows that it depends on (i) whether the phoneme being detected is at the start or end of an utterance and (ii) what the phoneme is, so that the use of a uniform forgiveness collar is inadequate. This analysis is expected to point the way towards more indicative and repeatable assessment of the performance of speaker diarization systems.