Reviewerly/exHarmony huggingface dataset:
Evolving diverse TSP instances by means of novel and creative mutation operators

Evolutionary algorithms have successfully been applied to evolve problem instances that exhibit a significant difference in performance for a given algorithm or a pair of algorithms inter alia for the Traveling Salesperson Problem (TSP). Creating a large variety of instances is crucial for successful applications in the blooming field of algorithm selection. In this paper, we introduce new and creative mutation operators for evolving instances of the TSP. We show that adopting those operators in an evolutionary algorithm allows for the generation of benchmark sets with highly desirable properties: (1) novelty by clear visual distinction to established benchmark sets in the field, (2) visual and quantitative diversity in the space of TSP problem characteristics, and (3) significant performance differences with respect to the restart versions of heuristic state-of-the-art TSP solvers EAX and LKH. The important aspect of diversity is addressed and achieved solely by the proposed mutation operators and not enforced by explicit diversity preservation.
Nonsubsampled Graph Filter Banks: Theory and Distributed Algorithms

In this paper, we consider nonsubsampled graph filter banks (NSGFBs) to process data on a sparse graph. The analysis filter banks of NSGFBs have small bandwidth, pass/block the normalized constant signal, and have stability on ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Given an analysis filter bank with small bandwidth, we introduce algebraic and optimization methods to construct well-localized synthesis filter banks such that the corresponding NSGFBs provide a perfect signal reconstruction in the noiseless setting. We also prove that the proposed NSGFBs can control the resonance effect in the presence of bounded noise and they can limit the influence of shot noise primarily to a small neighborhood near its location on the graph. We later introduce an iterative algorithm to implement the proposed NSGFBs in a distributed manner, and develop an NSGFB-based denoising technique which is demonstrated to have satisfactory performance on noise suppression.
XBFS

Attracted by the enormous potentials of Graphics Processing Units (GPUs), an array of efforts has surged to deploy Breadth-First Search (BFS) on GPUs, which, however, often exploits the static mechanisms to address the challenges that are dynamic in nature. Such a mismatch prevents us from achieving the optimal performance for offloading graph traversal on GPUs. To this end, we propose XBFS that leverages the runtime optimizations atop GPUs to cope with the nondeterministic characteristics of BFS with the following three techniques: First, XBFS adaptively exploits four either new or optimized frontier queue generation designs to accommodate various BFS levels that present dissimilar features. Second, inspired by the observation that the workload associated with each vertex is not proportional to its degree in bottom-up, we design three new strategies to better balance the workload. Third, XBFS introduces the first truly asynchronous bottom-up traversal which allows BFS to visit vertices for multiple levels at a single iteration with both theoretical soundness and practical benefits. Taken together, XBFS is, on average, 3.5×, 4.9×, 11.2× and 6.1× faster than the state-of-the-art Enterprise, Tigr, Gunrock on a Quadro P6000 GPU and Ligra on a 24-core Intel Xeon Platinum 8175M CPU. Note, the CPU used for Ligra is more expensive than the GPU for XBFS.
Cyclic Hybrid Double-Channel Quantum Communication via Bell-State and GHZ-State in Noisy Environments

In this paper, a scheme for cyclic hybrid double-channel quantum communication is proposed by using the product state of three Bell states and three Greenberger-Horne-Zeilinger (GHZ) states as the quantum channel.It shows that Alice teleports a single-qubit state to Bob and prepares a single-qubit state for Charlie, Bob teleports a single-qubit state to Charlie and prepares a single-qubit state for Alice, while Charlie teleports a single-qubit state to Alice and prepares a single-qubit state for Bob.The quantum channel is constructed by using Hadamard (H) and Controlled-NOT (CNOT) operations.Participants reconstruct the desired states by performing Bell-state measurements, single-qubit measurements, and unitary transformations.Compared with existing schemes, this new scheme improves the efficiency and capacity of quantum communication because it constructs a cyclic and bidirectional quantum communication and simultaneously supports two communication protocols, quantum teleportation and remote state preparation.Only single-qubit measurements, two-qubit measurements, and basic unitary transformations are utilized in the scheme, so our operation complexity is lower than others.Thus, the scheme is likely to be implemented through physical experiments in the future.Besides this, we discuss the impact of noisy environments (amplitude-damping, phase-damping noise, bit-flip noise, and phase-flip noise) in the scheme and calculate the fidelities of the output states.It is demonstrated that the fidelities only depend on the coefficients of the initial state and the decoherence rate.
Classifying suspicious content in tor darknet through Semantic Attention Keypoint Filtering

One of the tasks Law Enforcement Agencies are responsible for is to find evidence of criminal activities in the Darknet. However, visiting thousands of domains to locate visual information containing illicit acts manually requires a considerable amount of time and human resources. To support this task, in this paper, we explore the automatic classification of images uploaded to Tor darknet. Unfortunately, the foreground objects on such images are not always presented standalone, without background, due to the environmental conditions. To address this challenge on the digital investigation of Tor darknet visual content, we propose to classify automatically only relevant parts of the image combining saliency maps, i.e. to select the regions with the most salient information, with Bag of Visual Words (BoVW). We introduce Semantic Attention Keypoint Filtering (SAKF), a filtering strategy that removes non-significant features at a pixel level that mainly do not belong to the object of interest or foreground. We assessed SAKF on seven publicly available datasets, obtaining from 1.64 to 15.73 points higher accuracies than the method set as the baseline, i.e. BoVW using dense SIFT (Scale-Invariant Feature Transform) descriptors. We also compared SAKF filtering performance against the deep features extracted from two well-known Convolutional Neural Network (CNN) architectures, namely MobileNet and ResNet50. Experimental results reveal the effectiveness of the proposed approach and highlight that the use of automatic image classification could be advantageous to support daily Law Enforcement Agencies investigations on Tor darknet.
Virtual machine scheduling strategy based on machine learning algorithms for load balancing

With the rapid increase of user access, load balancing in cloud data center has become an important factor affecting cluster stability. From the point of view of green scheduling, this paper proposed a virtual machine intelligent scheduling strategy based on machine learning algorithm to achieve load balancing of cloud data center. Firstly, a load forecasting algorithm based on genetic algorithm (SVR_GA), k-means clustering algorithm based on optimized min-max, and adaptive differential evolution algorithm (ESA_DE) to enhance local search ability are proposed to solve the load imbalance problem in cloud data center. The experimental results showed that compared with other classical algorithms, the proposed virtual machine scheduling strategy reduces the number of virtual machine migration by 94.5% and the energy consumption of cloud data center by 49.13%.
Continual and Multi-Task Architecture Search

Architecture search is the process of automatically learning the neural model or cell structure that best suits the given task.Recently, this approach has shown promising performance improvements (on language modeling and image classification) with reasonable training speed, using a weight sharing strategy called Efficient Neural Architecture Search (ENAS).In our work, we first introduce a novel continual architecture search (CAS) approach, so as to continually evolve the model parameters during the sequential training of several tasks, without losing performance on previously learned tasks (via blocksparsity and orthogonality constraints), thus enabling life-long learning.Next, we explore a multi-task architecture search (MAS) approach over ENAS for finding a unified, single cell structure that performs well across multiple tasks (via joint controller rewards), and hence allows more generalizable transfer of the cell structure knowledge to an unseen new task.We empirically show the effectiveness of our sequential continual learning and parallel multi-task learning based architecture search approaches on diverse sentence-pair classification tasks (GLUE) and multimodal-generation based video captioning tasks.Further, we present several ablations and analyses on the learned cell structures. 1
Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approach

Existing graph layout algorithms are usually not able to optimize all the aesthetic properties desired in a graph layout. To evaluate how well the desired visual features are reflected in a graph layout, many readability metrics have been proposed in the past decades. However, the calculation of these readability metrics often requires access to the node and edge coordinates and is usually computationally inefficient, especially for dense graphs. Importantly, when the node and edge coordinates are not accessible, it becomes impossible to evaluate the graph layouts quantitatively. In this paper, we present a novel deep learning-based approach to evaluate the readability of graph layouts by directly using graph images. A convolutional neural network architecture is proposed and trained on a benchmark dataset of graph images, which is composed of synthetically generated graphs and graphs created by sampling from real large networks. Multiple representative readability metrics (including edge crossing, node spread, and group overlap) are considered in the proposed approach. We quantitatively compare our approach to traditional methods and qualitatively evaluate our approach by showing usage scenarios and visualizing convolutional layers. This paper is a first step towards using deep learning based methods to quantitatively evaluate images from the visualization field.
Ship detection and tracking method for satellite video based on multiscale saliency and surrounding contrast analysis

In port surveillance, monitoring based on satellite video is a valuable supplement to a ground monitoring system because of its wide monitoring range. Therefore, automatic ship detection and tracking based on satellite video is an important research field. However, because of the small size of ships without texture and the interference of sea noise, it is also a challenging subject. An approach of automatic detection and tracking moving ships of different sizes using satellite video is presented. First, motion compensation between two frames is realized. Then, saliency maps of multiscale differential image are combined to create dynamic multiscale saliency map (DMSM), which is more suitable for the detection of ships of different sizes. Third, candidate motion regions are segmented from DMSM, and moving ships can be detected after the false alarms are removed based on the surrounding contrast. Fourth, important elements such as centroid distance, area ratio, and histogram distance from moving ships are used to perform ship matching. Finally, ship association and tracking are realized by using the intermediate frame in every three adjacent frames. Experimental results on satellite sequences indicate that our method can effectively detect and track ships and obtain the target track, which is superior in terms of the defined recall and precision compared with other classical target tracking methods.
Human Behavior Inspired Machine Reading Comprehension

Machine Reading Comprehension (MRC) is one of the most challenging tasks in both NLP and IR researches. Recently, a number of deep neural models have been successfully adopted to some simplified MRC task settings, whose performances were close to or even better than human beings. However, these models still have large performance gaps with human beings in more practical settings, such as MS MARCO and DuReader datasets. Although there are many works studying human reading behavior, the behavior patterns in complex reading comprehension scenarios remain under-investigated. We believe that a better understanding of how human reads and allocates their attention during reading comprehension processes can help improve the performance of MRC tasks. In this paper, we conduct a lab study to investigate human's reading behavior patterns during reading comprehension tasks, where 32 users are recruited to take 60 distinct tasks. By analyzing the collected eye-tracking data and answers from participants, we propose a two-stage reading behavior model, in which the first stage is to search for possible answer candidates and the second stage is to generate the final answer through a comparison and verification process. We also find that human's attention distribution is affected by both question-dependent factors (e.g., answer and soft matching signal with questions) and question-independent factors (e.g., position, IDF and Part-of-Speech tags of words). We extract features derived from the two-stage reading behavior model to predict human's attention signals during reading comprehension, which significantly improves performance in the MRC task. Findings in our work may bring insight into the understanding of human reading and information seeking processes, and help the machine to better meet users' information needs.
Heat-Bath Algorithmic Cooling with optimal thermalization strategies

Heat-Bath Algorithmic Cooling is a set of techniques for producing highly pure quantum systems by utilizing a surrounding heat-bath and unitary interactions. These techniques originally used the thermal environment only to fully thermalize ancillas at the environment temperature. Here we extend HBAC protocols by optimizing over the thermalization strategy. We find, for any <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>d</mml:mi></mml:math>-dimensional system in an arbitrary initial state, provably optimal cooling protocols with surprisingly simple structure and exponential convergence to the ground state. Compared to the standard ones, these schemes can use fewer or no ancillas and exploit memory effects to enhance cooling. We verify that the optimal protocols are robusts to various deviations from the ideal scenario. For a single target qubit, the optimal protocol can be well approximated with a Jaynes-Cummings interaction between the system and a single thermal bosonic mode for a wide range of environmental temperatures. This admits an experimental implementation close to the setup of a micromaser, with a performance competitive with leading proposals in the literature. The proposed protocol provides an experimental setup that illustrates how non-Markovianity can be harnessed to improve cooling. On the technical side we 1. introduce a new class of states called <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>x</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mi>l</mml:mi><mml:mi>y</mml:mi></mml:math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>a</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>v</mml:mi><mml:mi>e</mml:mi></mml:math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>s</mml:mi><mml:mi>t</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi></mml:math> and discuss their thermodynamic significance in terms of optimal unitary control, 2. introduce a new set of thermodynamic processes, called <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mrow><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi>β</mml:mi></mml:mrow><mml:mtext class="MJX-tex-mathit" mathvariant="italic">-permutations</mml:mtext></mml:mrow></mml:mrow></mml:math>, whose access is sufficient to simulate a generic thermalization process, 3. show how to use abstract toolbox developed within the resource theory approach to thermodynamics to perform challenging optimizations, while combining it with open quantum system dynamics tools to approximate optimal solutions within physically realistic setups.
FREDPC: A Feasible Residual Error-Based Density Peak Clustering Algorithm With the Fragment Merging Strategy

The most common issues for many clustering algorithms include the slow convergence, requirement for pre-specification of a number of parameters, and the lack of robustness when dealing with anomalies.Recently, the density peak clustering (DPC) algorithm was proposed to discover the centers of clusters by finding the density peaks in a dataset based on their local densities.The DPC needs neither an iterative process nor a large number of parameters, and it supports a heuristic approach, known as the decision graph, to manually select cluster centroids.However, the selection of the key parameters of the DPC was not systematically investigated.In this paper, we propose the feasible residual error-based density peak clustering algorithm with the fragment merging strategy, where the local density within the neighborhood region is measured through the residual error computation and the resulting residual errors are then used to generate residual fragments for cluster formation.The model parameters are then able to be calculated from the equations with statistical theoretical justification.We also develop a semi-automatic cluster identification method to eliminate the iterative process of manual centroid selection.The robustness and effectiveness of the proposed algorithm compared to the DPC and other clustering algorithms are demonstrated through experiments on standard benchmark datasets.The proposed method named feasible residual error-based density peak clustering (FREDPC) algorithm with the fragment merging strategy only needs to perform in one single step without any iteration and thus it is fast and has a great potential to be applied on a wide range of applications.
Shadow removal based on separated illumination correction for urban aerial remote sensing images

The presence of shadows in urban aerial images degrades the image quality and reduces the application accuracy. Removing shadows and recovering the ground information is therefore a crucial issue. The existing shadow removal methods can correct the shadow information, but the inconsistency between the corrected shadow and non-shadow areas is still obvious. A novel shadow removal method based on separated illumination correction is proposed in this paper, in which the shadow removal is only performed on the shadow-related illumination. A spatially adaptive weighted total variation model is constructed to obtain the shadow-related illumination and the shadow-free reflectance. The objects in the shadows are detected based on the reflectance, and object-oriented illumination correction is then implemented to compensate the shadow regions. The shadow removal results can be obtained by combining the corrected illumination and the reflectance. Three aerial remote sensing images were selected for the experiments, and two quantitative evaluation methods are introduced: the shadow standard deviation index and classification analysis. The results are shown and compared with four existing methods by visual and quantitative assessments, which indicate that the proposed method can yield more visually natural shadow-free images and show a better performance in the quantitative indices.
Modified belief propagation decoders for quantum low-density parity-check codes

Quantum low-density parity-check codes can be decoded using a syndrome based GF(4) belief propagation decoder (where GF denotes Galois field). However, the performance of this decoder is limited both by unavoidable 4-cycles in the code's factor graph and the degenerate nature of quantum errors. For the subclass of CSS codes, the number of 4-cycles can be reduced by breaking an error into an $X$ and $Z$ component and decoding each with an individual $\mathrm{GF}(2)$ based decoder. However, this comes at the expense of ignoring potential correlations between these two error components. We present a number of modified belief propagation decoders that address these issues. We propose a $\mathrm{GF}(2)$ based decoder for CSS codes that reintroduces error correlations by reattempting decoding with adjusted error probabilities. We also propose the use of an augmented decoder, which has previously been suggested for classical binary low-density parity-check codes. This decoder iteratively reattempts decoding on factor graphs that have a subset of their check nodes duplicated. The augmented decoder can be based on a $\mathrm{GF}(4)$ decoder for any code, a $\mathrm{GF}(2)$ decoder for CSS code, or even a supernode decoder for a dual-containing CSS code. For CSS codes, we further propose a $\mathrm{GF}(2)$ based decoder that combines the augmented decoder with error probability adjustment. We demonstrate the performance of these new decoders on a range of different codes, showing that they perform favorably compared to other decoders presented in literature.
Generative Steganography by Sampling

In this paper, a novel data-driven information hiding scheme called ''generative steganography by sampling'' (GSS) is proposed.Unlike in traditional modification-based steganography, in our method the stego image is directly sampled by a powerful generator: no explicit cover is used.Both parties share a secret key used for message embedding and extraction.The Jensen-Shannon divergence is introduced as a new criterion for evaluating the security of generative steganography.Based on these principles, we propose a simple practical generative steganography method that uses semantic image inpainting.The message is written in advance to an uncorrupted region that needs to be retained in the corrupted image.Then, the corrupted image with the secret message is fed into a Generator trained by a generative adversarial network (GAN) for semantic completion.Message loss and prior loss terms are proposed for penalizing message extraction error and unrealistic stego image.In our design, we first train a generator whose training target is the generation of new data samples from the same distribution as that of existing training data.Next, for the trained generator, backpropagation to the message and prior loss are introduced to optimize the coding of the input noise data for the generator.The presented experiments demonstrate the potential of the proposed framework based on both qualitative and quantitative evaluations of the generated stego images.