Reviewerly/exHarmony huggingface dataset:
Narrow Passage Problem Solution for Motion Planning

The paper introduces a new randomized sampling-based method of motion planning suitable for the problem of narrow passages. The proposed method was inspired by the method of exit points for cavities in protein models and is based on the Rapidly Exploring Random Tree (RRT). Unlike other methods, it can also provide locations of the exact positions of narrow passages. This information is extremely important as it helps to solve this part of space in more detail and even to decide whether a path through this bottleneck exists or not. For data with narrow passages, the proposed method finds more paths in a shorter time, for data without narrow passages, the proposed method is slower but still provides correct paths.
Two-photon phase-sensing with single-photon detection

Path-entangled multi-photon states allow optical phase-sensing beyond the shot-noise limit, provided that an efficient parity measurement can be implemented. Realizing this experimentally is technologically demanding, as it requires coincident single-photon detection proportional to the number of photons involved, which represents a severe challenge for achieving a practical quantum advantage over classical methods. Here, we exploit advanced quantum state engineering based on superposing two photon-pair creation events to realize a new approach that bypasses this issue. In particular, optical phase shifts are probed with a two-photon quantum state whose information is subsequently effectively transferred to a single-photon state. Notably, without any multiphoton detection, we infer phase shifts by measuring the average intensity of the single-photon beam on a photodiode, in analogy to standard classical measurements. Importantly, our approach maintains the quantum advantage: twice as many interference fringes are observed for the same phase shift, corresponding to N = 2 path-entangled photons. Our results demonstrate that the advantages of quantum-enhanced phase sensing can be fully exploited in standard intensity measurements, paving the way toward resource-efficient and practical quantum optical metrology.
Evaluating single-mode nonclassicality

Quantifying nonclassicality of a bosonic mode is an important but challenging task in quantum optics. Recently, a nonclassicality measure based on the concept of operational resource theory has been proposed [W. Ge, K. Jacobs, S. Asiri, M. Foss-Feig, and M. S. Zubairy, Phys. Rev. Res. 2, 023400 (2020)], which shows several crucial properties as a resource for quantum metrology. Here we apply the measure to evaluate and categorize different classes of nonclassical states. We discover a class of states that can achieve the maximum nonclassicality in the asymptotic limit of a large mean number of excitations. These states can provide the same sensitivity in place of a squeezed vacuum in a sensing scheme, e.g., the laser interferometer gravitational-wave observatory (LIGO) experiments. We also discover that the nonclassicality of certain states can be greatly improved with a single-photon addition. In addition, we explore some examples on how to evaluate analytically the nonclassicality of mixed states, which are in general difficult to calculate due to the convex roof nature of the measure. Our results will be useful for preparing and utilizing nonclassical states for applications in precision-sensing tasks, such as quadrature sensing and phase sensing in a Mach-Zehnder interferometer.
Adaptive differential evolution with a new joint parameter adaptation method

Abstract Differential evolution (DE) is a population-based metaheuristic algorithm that has been proved powerful in solving a wide range of real-parameter optimization tasks. However, the selection of the mutation strategy and control parameters in DE is problem dependent, and inappropriate specification of them will lead to poor performance of the algorithm such as slow convergence and early stagnation in a local optimum. This paper proposes a new method termed as Joint Adaptation of Parameters in DE (JAPDE). The key idea lies in dynamically updating the selection probabilities for a complete set of pairs of parameter generating functions based on feedback information acquired during the search by DE. Further, for mutation strategy adaptation, the Rank-Based Adaptation (RAM) method is utilized to facilitate the learning of multiple probability distributions, each of which corresponds to an interval of fitness ranks of individuals in the population. The coupling of RAM with JAPDE results in the new RAM-JAPDE algorithm that enables simultaneous adaptation of the selection probabilities for pairs of control parameters and mutation strategies in DE. The merit of RAM-JAPDE has been evaluated on the benchmark test suit proposed in CEC2014 in comparison to many well-known DE algorithms. The results of experiments demonstrate that the proposed RAM-JAPDE algorithm outperforms or is competitive to the other related DE variants that perform mutation strategy and control parameter adaptation, respectively.
Interaction-based creation and maintenance of continuously usable trace links between requirements and source code

Abstract Trace links between requirements and code are beneficial for many software engineering tasks such as maintenance, program comprehension, and re-engineering. If trace links are created and used continuously during a project, they need to have high precision and recall to be useful. However, manual trace link creation is cumbersome and existing automatic trace link creation methods are typically only applied retrospectively and to structured requirements. Therefore, they focus on recall and accept manual effort to cope with low precision. Such manual effort is not acceptable continuously. Furthermore, the maintenance of existing links along with changing artefacts in a project is neglected in most automatic trace link creation approaches. Therefore, we developed and evaluated an interaction log-based trace link creation approach IL to continuously provide correct trace links during a project. IL links unstructured requirements specified in an issue tracker and source code managed in a version control system. In the latest version, I L C o m , our approach uses the interactions of developers with files in an integrated development environment and issue identifiers provided in commit messages to create trace links continuously after each commit. In this paper, we present I L C o m , its most recent evaluation study, and a systematic literature review (SLR) about trace link maintenance (TM). We also present a TM process for I L C o m based on two approaches from our SLR. In the evaluation study, we show that precision of I L C o m created links is above 90% and recall almost at 80%. In the SLR, we discuss 16 approaches. Our approach is the first trace link creation approach with very good precision and recall and integrated trace maintenance.
Resource theory of superposition: State transformations

A combination of a finite number of linear independent states forms superposition in a way that cannot be conceived classically. Here, using the tools of resource theory of superposition, we give the conditions for a class of superposition state transformations. These conditions strictly depend on the scalar products of the basis states and reduce to the well-known majorization condition for quantum coherence in the limit of orthonormal basis. To further superposition-free transformations of $d$-dimensional systems, we provide superposition-free operators for a deterministic transformation of superposition states. The linear independence of a finite number of basis states requires a relation between the scalar products of these states. With this information in hand, we determine the maximal superposition states which are valid over a certain range of scalar products. Notably, we show that, for $d\ensuremath{\ge}3$, scalar products of the pure superposition-free states have a greater place in seeking maximally resourceful states. Various explicit examples illustrate our findings.
Multiple Feature Dependency Detection for Deep Learning Technology—Smart Pet Surveillance System Implementation

Image identification, machine learning and deep learning technologies have been applied in various fields. However, the application of image identification currently focuses on object detection and identification in order to determine a single momentary picture. This paper not only proposes multiple feature dependency detection to identify key parts of pets (mouth and tail) but also combines the meaning of the pet’s bark (growl and cry) to identify the pet’s mood and state. Therefore, it is necessary to consider changes of pet hair and ages. To this end, we add an automatic optimization identification module subsystem to respond to changes of pet hair and ages in real time. After successfully identifying images of featured parts each time, our system captures images of the identified featured parts and stores them as effective samples for subsequent training and improving the identification ability of the system. When the identification result is transmitted to the owner each time, the owner can get the current mood and state of the pet in real time. According to the experimental results, our system can use a faster R-CNN model to improve 27.47%, 68.17% and 26.23% accuracy of traditional image identification in the mood of happy, angry and sad respectively.
LayerOut: Freezing Layers in Deep Neural Networks

Deep networks involve a huge amount of computation during the training phase and are prone to over-fitting. To ameliorate these, several conventional techniques such as DropOut, DropConnect, Guided Dropout, Stochastic Depth, and BlockDrop have been proposed. These techniques regularize a neural network by dropping nodes, connections, layers, or blocks within the network. However, these conventional regularization techniques suffers from limitation that, they are suited either for fully connected networks or ResNet-based architectures. In this research, we propose a novel regularization technique LayerOut to train deep neural networks which stochastically freeze the trainable parameters of a layer during an epoch of training. This technique can be applied to both fully connected networks and all types of convolutional networks such as VGG-16, ResNet, etc. Experimental evaluation on multiple dataset including MNIST, CIFAR-10, and CIFAR-100 demonstrates that LayerOut generalizes better than the conventional regularization techniques and additionally reduces the computational burden significantly. We have observed up to 70 $$\%$$ reduction in computation per epoch and up to 2 $$\%$$ improvement in classification accuracy as compared to the baseline networks (VGG-16 and ResNet-110) on above datasets. Codes are publically available at https://github.com/Goutam-Kelam/LayerOut .
Controller Design Based on Echo State Network with Delay Output for Nonlinear System

For the nonlinear systems with delay output, the control performance of the system is affected by the previous output of the system, such as crawling robots of photovoltaic panels. In this paper, an improved controller design method based on echo state network with delay output (DO-ESN) is proposed for designing the controller of a class of nonlinear system. According to the internal characteristics of DO-ESN, the DO-ESN can match the system characteristics of nonlinear systems with delay output, such that the proposed controller can quickly meet the control performance of the nonlinear system. In order to ensure the stability of the controller, a sufficient condition is given for the echo state property of DO-ESN. Finally, a simulation example is used to illustrate the effectiveness of the proposed method.
Convolutional Neural Network Training for RGBN Camera Color Restoration Using Generated Image Pairs

RGBN cameras that can capture visible light and near-infrared (NIR) light simultaneously produce better color image quality in low-light-level conditions. However, these RGBN cameras introduce additional color bias caused by the mixing of visible information and NIR information. The color correction matrix model widely used in current commercial color digital cameras cannot handle the complicated mapping function between biased color and ground truth color. Convolutional neural networks (CNNs) are good at fitting such complicated relationships, but they require a large quantity of training image pairs of different scenes. In order to achieve satisfactory training results, large amounts of data must be captured manually, even when data augmentation techniques are applied, requiring significant time and effort. Hence, a data generation method for training pairs that are consistent with target RGBN camera parameters, based on an open access RGB-NIR dataset, is proposed. The proposed method is verified by training an RGBN camera color restoration CNN model with generated data. The results show that the CNN model trained with the generated data can achieve satisfactory RGBN color restoration performance with different RGBN sensors.
A New Family of Optimal Binary Few-Weight Codes From Simplicial Complexes

Trace codes over a cubic ring extension of the binary field are constructed by using a triple of simplicial complexes. A linear Gray map yields several infinite families of binary few-weight codes which can have as many as 9 weights. By equating two complexes in the triple, we obtain binary three-weight codes, which are minimal and distance optimal.
Construction of isodual codes from polycirculant matrices

Double polycirculant codes are introduced here as a generalization of double circulant codes. When the matrix of the polyshift is a companion matrix of a trinomial, we show that such a code is isodual, hence formally self-dual. Numerical examples show that the codes constructed have optimal or quasi-optimal parameters amongst formally self-dual codes. Self-duality, the trivial case of isoduality, can only occur over $$ {\mathbb {F}}_2$$ in the double circulant case. Building on an explicit infinite sequence of irreducible trinomials over $${\mathbb {F}}_2,$$ we show that binary double polycirculant codes are asymptotically good.
Reducing the Gap Between the Conceptual Models of Students and Experts Using Graph-Based Adaptive Instructional Systems

Decision-making in complex environments requires a detailed understanding of causality to avoid unintended consequences and consider multiple scenarios. Concept mapping is of the key tools to support such decision-making activities. A system is abstracted as a ‘map’ or graph, in which relevant factors are represented as nodes and connected through causal links. Although studies have shown that concept maps help students recall the evidence and apply the knowledge gained to new cases, barriers such as the difficulty of assessment have prevented a wide adoption of concept maps by instructors. Previous works have partly addressed this concern by using graph algorithms to automatically assess or score a student’s map, but instructors are often more interested in correcting their students’ misconceptions than in only counting mistakes. In this paper, we design and implement a system to automatically guide students in modifying their conceptual models such that they get increasingly similar to the models produced by experts on the same problem. Although a simple automatic feedback may tell students to add/remove concepts or links based on whether they appear in the expert’s map, our approach combines expertise from systems science and participatory modeling to help students better understand why low-level changes are motivated by higher-level structures such as loops and alternative paths.
An anti-recompression video watermarking algorithm in bitstream domain

Following the popularity of digital video application, video copying and dissemination have become very easy; however, this makes video hacking and piracy a potential threat in video communication. Video watermarking technology can solve the problem of copyright protection, and thus, it has been extensively researched. The robustness of the video watermarking algorithm in the bitstream domain is poor, especially the anti-recompression ability, since the watermarked video may be compressed again before transmitting. Considering this, this paper proposes a video watermarking algorithm in the bitstream domain based on moving object detection. To increase the robustness of the watermarking scheme, the moving macroblocks that belong to the moving object in each P frame can be identified via the moving object detection algorithm. Then, watermark embedding in the moving macroblocks is performed using codeword substitution to ensure the consistency of the bitstream. Moving object detection and watermark embedding are independent and are both carried out in the bitstream domain by partially decoding the bitstream; this avoids the complete decoding and reconstruction of the video, making the method to be highly efficient. The simulation results confirm that the proposed method is robust against recompression and has little impact on the video visual quality and no influence on the bit rate.
GCNEXT: graph convolutional network with expanded balance theory for fraudulent user detection

Abstract Rating platforms provide users with useful information on products or other users. However, fake ratings are sometimes generated by fraudulent users. In this paper, we tackle the task of fraudulent user detection on rating platforms. We propose GCNEXT (Graph Convolutional Network with Expended Balance Theory), an end-to-end framework based on graph convolutional networks (GCNs) and expanded balance theory, which properly incorporates both the signs and directions of edges. The experimental results on seven real-world datasets show that the proposed framework performs better, or even best, in most settings. In particular, this framework shows remarkable stability in inductive settings, which is associated with the detection of new fraudulent users on rating platforms. Furthermore, using expanded balance theory, we provide new insight into the behavior of users in rating networks that fraudulent users form a faction to deal with the negative ratings from other users. The owner of a rating platform can detect fraudulent users earlier and constantly provide users with more credible information by using the proposed framework.
Detection and stage classification of Plasmodium falciparum from images of Giemsa stained thin blood films using random forest classifiers

Abstract Background The conventional method for the diagnosis of malaria parasites is the microscopic examination of stained blood films, which is time consuming and requires expertise. We introduce computer-based image segmentation and life stage classification with a random forest classifier. Segmentation and stage classification are performed on a large dataset of malaria parasites with ground truth labels provided by experts. Methods We made use of Giemsa stained images obtained from the blood of 16 patients infected with Plasmodium falciparum. Experts labeled the parasite types from each of the images. We applied a two-step approach: image segmentation followed by life stage classification. In segmentation, we classified each pixel as a parasite or non-parasite pixel using a random forest classifier. Performance was evaluated with classification accuracy, Dice coefficient and free-response receiver operating characteristic (FROC) analysis. In life stage classification, we classified each of the segmented objects into one of 8 classes: 6 parasite life stages, early ring, late ring or early trophozoite, mid trophozoite, early schizont, late schizont or segmented, and two other classes, white blood cell or debris. Results Our segmentation method gives an average cross-validated Dice coefficient of 0.82 which is a 13% improvement compared to the Otsu method. The Otsu method achieved a True Positive Fraction (TPF) of 0.925 at the expense of a False Positive Rate (FPR) of 2.45. At the same TPF of 0.925, our method achieved an FPR of 0.92, an improvement by more than a factor two. We find that inclusion of average intensity of the whole image as feature for the random forest considerably improves segmentation performance. We obtain an overall accuracy of 58.8 % when classifying all life stages. Stages are mostly confused with their neighboring stages. When we reduce the life stages to ring, trophozoite and schizont only, we obtain an accuracy of 82.7 % . Conclusion Pixel classification gives better segmentation performance than the conventional Otsu method. Effects of staining and background variations can be reduced with the inclusion of average intensity features. The proposed method and data set can be used in the development of automatic tools for the detection and stage classification of malaria parasites. The data set is publicly available as a benchmark for future studies.
A constructive approach to topological invariants for one-dimensional strictly local operators

In this paper we shall focus on one-dimensional strictly local operators, the notion of which naturally arises in the context of discrete-time quantum walks on the one-dimensional integer lattice. In particular, we give an elementary constructive approach to the following two topological invariants associated with such operators: Fredholm index and essential spectrum. As a direct application, we shall explicitly compute and fully classify these topological invariants for a well-known quantum walk model.
MMES: Mixture Model-Based Evolution Strategy for Large-Scale Optimization

This work provides an efficient sampling method for the covariance matrix adaptation evolution strategy (CMA-ES) in large-scale settings. In contract to the Gaussian sampling in CMA-ES, the proposed method generates mutation vectors from a mixture model, which facilitates exploiting the rich variable correlations of the problem landscape within a limited time budget. We analyze the probability distribution of this mixture model and show that it approximates the Gaussian distribution of CMA-ES with a controllable accuracy. We use this sampling method, coupled with a novel method for mutation strength adaptation, to formulate the mixture model based evolution strategy (MMES) -- a CMA-ES variant for large-scale optimization. The numerical simulations show that, while significantly reducing the time complexity of CMA-ES, MMES preserves the rotational invariance, is scalable to high dimensional problems, and is competitive against the state-of-the-arts in performing global optimization.
Performance Analysis of Deep Neural Networks for Object Classification with Edge TPU

Deep learning becomes a more popular, widespread, and common tool in almost any task that requires information extraction from a large dataset. Hence, the data transmission speed between the data-gathering devices and processing units can be crucial in hardware selection depending on the machine learning application. Generally, the processing unit is usually centralized, and the data transferring time will increase when the data-gathering devices were installed further away from the processing unit. The work aims to provide the performance analysis on Google's new machine learning hardware called Edge TPU that was created specifically for edge devices. Furthermore, the work also reviewed the different types of deep neural network models as current benchmarks in deep learning were tested with different hardware used in edge applications. The review also discussed the comparison of the performance of the edge device using the deep neural networks in Tensorflow. From the results obtained, the performance of the edge device with the Edge TPU is faster than the device without it.