Reviewerly/exHarmony huggingface dataset:
A local cooperative approach to solve large-scale constrained optimization problems

Cooperative Co-evolutionary algorithms are very popular to solve large-scale problems. A significant part of these algorithms is the decomposition of the problems according to the variables interaction. In this paper, an approach based on a memetic scheme, where its local stage (and not the global stage) is guided by the decomposition method (Local Cooperative Search LoCoS), is presented to solve large-scale constrained optimization problems. Two decomposition methods are tested: the improved version of the Variable Interdependence Identification for Constrained problems and Differential Grouping version 2. A recently-proposed benchmark with eighteen test problems with different features is solved to assess the performance of LoCoS when compared against a similar memetic algorithm but without decomposition and also against a state-of-the-art cooperative co-evolutionary algorithm. The results show a faster convergence, better final results and higher feasibility ratio by LoCosS with respect to the values provided by the compared algorithms.
Exemplar-based image saliency and co-saliency detection

Image saliency and co-saliency detection that aim to detect salient objects in an image or common salient objects in a group of images are import in computer vision. Researchers often treat saliency and co-saliency as two separate problems. In this paper, we show that these two problems can be solved in a single framework, i.e., treating saliency and co-saliency as finding suitable exemplars. Image-level and region-level exemplars are proposed to obtain the similar images and to propagate the saliency values, respectively. Our method only requires a small number of labeled images having similar appearances with a query image. The exemplars help to detect the real salient objects, which is different from the conventional heuristic methods that are fragile for the images with complex scenes. We have conducted abundant experiments on saliency and co-saliency benchmark datasets, which verifies the effectiveness of our method.
A simheuristic approach for evolving agent behaviour in the exploration for novel combat tactics

The automatic generation of behavioural models for intelligent agents in military simulation and experimentation remains a challenge. Genetic Algorithms are a global optimization approach which is suitable for addressing complex problems where locating the global optimum is a difficult task. Unlike traditional optimisation techniques such as hill-climbing or derivatives-based methods, Genetic Algorithms are robust for addressing highly multi-modal and discontinuous search landscapes. In this paper, we outline a simheuristic GA-based approach for automatic generation of finite state machine based behavioural models of intelligent agents, where the aim is the identification of novel combat tactics. Rather than evolving states, the proposed approach evolves a sequence of transitions. We also discuss workable starting points for the use of Genetic Algorithms for such scenarios, shedding some light on the associated design and implementation difficulties.
Research on Privacy Protection in IoT System Based on Blockchain

The Internet of Things (IoT) is an important area of next-generation information technology, and its value and significance are widely recognized. While providing development opportunities, the IoT also presents major challenges. Security and privacy have become severe issues that cannot be ignored in the development of the IoT in this paper, so we will propose an IoT information security protection scheme based on blockchain technology. The scheme utilizes the security features of the blockchain combined with the AES encryption algorithm to encrypt the original IoT information, and the ciphertext distributed storage can effectively solve the IoT data storage problem. Experiments shown in this scheme could reduce the operation and credit cost of centralized network. At the same time, the blockchain-based IoT information security protection scheme combined with cryptography knowledge can effectively solved the big data management and trust faced in the development of the IoT, security and privacy issues.
Contextual, Behavioral, and Biometric Signatures for Continuous Authentication

Continuous authentication in the Mobile Internet of Things should be based as broadly as possible, since a wide range of factors continuously reveal unexpected correlations. Such factors may include captured events (e.g., password, fingerprint, application start and end, network connect, and disconnect), continuous time series (e.g., gesture, typing rate, accelerometer, GPS, ambient sound, light levels, and time-of-day), and derived behavioral features (e.g., user sociability, browser and application menus, application choice). All these factors have been shown to correlate with the actual user identity, often in surprising combinations. More and more sensors are being deployed in autonomous devices, smart environments and vehicles, enabling even further behavioral and contextual data to be analyzed. The pegs of this continuous authentication “big tent” are moving out further than ever before, bringing it closer to practical uses in our everyday lives.
Does Tail Label Help for Large-Scale Multi-Label Learning?

Large-scale multi-label learning (LMLL) annotates relevant labels for unseen data from a huge number of candidate labels. It is perceived that labels exhibit a long tail distribution in which a significant number of labels are tail labels. Most previous studies consider that the performance would benefit from incorporating tail labels. Nonetheless, it is not quantified how tail labels impact the performance. In this article, we disclose that whatever labels are randomly missing or misclassified, the impact of labels on commonly used LMLL evaluation metrics (Propensity Score Precision (PSP)@ k and Propensity Score nDCG (PSnDCG)@ k ) is directly related to the product of the label weights and the label frequencies. In particular, when labels share equal weights, tail labels impact much less than common labels due to the scarcity of relevant examples. Based on such observation, we propose to develop low-complexity LMLL methods with the goal of facilitating fast prediction time and compact model size by restraining less performance-influential labels. With the consideration that discarding labels may cause the loss of predictive capability, we further propose to preserve dominant model parameters for the less performance-influential labels. Experiments clearly justify that both the prediction time and the model size are significantly reduced without sacrificing much predictive performance.
Comparing the Interpretability of Deep Networks via Network Dissection

In this chapter, we introduce Network Dissection (The complete paper and code are available at http://netdissect.csail.mit.edu ), a general framework to quantify the interpretability of the units inside a deep convolutional neural networks (CNNs). We compare the different vocabularies of interpretable units as concept detectors emerged from the networks trained to solve different supervised learning tasks such as object recognition on ImageNet and scene classification on Places. The network dissection is further applied to analyze how the units acting as semantic detectors grow and evolve over the training iterations both in the scenario of the train-from-scratch and in the stage of the fine-tuning between data sources. Our results highlight that interpretability is an important property of deep neural networks that provides new insights into their hierarchical structure.
3D Inception U-Net for Aorta Segmentation using Computed Tomography Cardiac Angiography

Computed Tomography Coronary Angiography (CTCA) is an effective imaging technique used for diagnosis and surgical planning. Segmentation of the aorta from the CTCA can be used clinically for interpretation, aortic valve measurement for intervention and identification of coronary structures. The process of segmentation done manually is tedious and time consuming. In this paper we propose an approach to automatic aorta segmentation from cardiac CTCA scans using deep learning. A dataset of 20 pairs of CT and mask is used, with the manually segmented masks as ground truth. The dataset is split into a training set of 10 pairs, validation set of 3 pairs and test set of 7 pairs. The proposed framework uses two different U-Net models trained for location and refined segmentation tasks. The U-Net architecture and multiple variants of the U-Net are adopted, and their performances are compared. The paper also explores different types of data preprocessing and augmentation that can be performed to improve the performance of the deep learning model. The results show that 3D Inception U-Net performs the best in localization and segmentation tasks. The Localization test DSC is 0.77 and Segmentation test DSC is 0.81. Qualitative testing shows that predicted masks are quite similar to the original.
A Typology of Architectural Strategies for Interoperability

An increasing interest in researching the development, integration, composition, and evolution of large-scale, software-intensive systems (LSSIS) have been observed in the last years. These systems are presented in different domains as connected health, industry 4.0, military, smart cities, smart grids, and smart agriculture. These systems are realized through the cooperation among heterogeneous, independent, highly distributed, individual, and heterogeneous systems to achieve their business goals. For the success of these larger systems, their individual parts must interoperate among them allowing the execution of more complex functionalities. However, there are many concerns that software engineers must overcome during the composition of these systems, that can be related to (i) the misunderstanding of data formats, data semantic, procedures, contracts, standards, quality, and interfaces structures provided by individual systems; and (ii) the absence of high-level architectures to analyze, comprehend, and guide how interoperability can be addressed. The goal of this work is to present a typology of existing and proven strategies for achieving interoperability. Strategies were identified through a systematic search in scientific databases and patterns repositories. The selected strategies were categorized according to the level of interoperability they support, namely, technical, syntactic, semantic, and organizational. In each level, the strategies were divided by the type of solution their propose, i.e., technique, and architectural styles, patterns, and tactics. Moreover, statements explaining how each strategy address interoperability are given. Results of this work can be used by architects to identify and understand solutions for achieving interoperability requirements during the composition of larger systems. The resulting typology in this study is the first step to consolidate a patterns-language for interoperability in software architectures.
Interactive sonification strategies for the motion and emotion of dance performances

Sonification has the potential to communicate a variety of data types to listeners including not just cognitive information, but also emotions and aesthetics. The goal of our dancer sonification project is to “sonify emotions as well as motions” of a dance performance via musical sonification. To this end, we developed and evaluated sonification strategies for adding a layer of emotional mappings to data sonification. Experiment 1 developed and evaluated four musical sonifications (i.e., sin-ification, MIDI-fication, melody module, and melody and arrangement module) to see their emotional effects. Videos were recorded of a professional dancer interacting with each of the four musical sonification strategies. Forty-eight participants provided ratings of musicality, emotional expressivity, and sound-motion/emotion compatibility via an online survey. Results suggest that increasing musical mappings led to higher ratings for each dimension for dance-type gestures. Experiment 2 used the musical sonification framework to develop four sonification scenarios that aimed to communicate a target emotion (happy, sad, angry, and tender). Thirty participants compared four interactive sonification scenarios with four pre-composed dance choreographies featuring the same musical and gestural palettes. Both forced choice and multi-dimensional emotional evaluations were collected, as well as motion/emotion compatibility ratings. Results show that having both music and dance led to higher accuracy scores for most target emotions, compared to music or dance conditions alone. These findings can contribute to the fields of movement sonification, algorithmic music composition, as well as affective computing in general, by describing strategies for conveying emotion through sound.
Improvements on “Secure multi-party quantum summation based on quantum Fourier transform”

Recently, a quantum multi-party summation protocol based on the quantum Fourier transform has been proposed (Yang et al. in Quantum Inf Process 17:129, 2018). The protocol claims to be secure against both outside and participant attacks. However, a closer look reveals that the player in charge of generating the required multi-partite entangled states can launch two kinds of attacks to learn about other parties’ private integer strings without being caught. In this paper, we present these attacks and propose countermeasures to make the protocol secure again. The improved protocol not only can resist these attacks but also remove the need for the quantum Fourier transform and encoding quantum operations by participants.
A Recurrent Attention Network for Judgment Prediction

Judgment prediction is a critical technique in legal field. Judges usually scan both of the fact descriptions and articles repeatedly to select valuables information for a correct match (i.e., determine the correct articles for a given fact description). Previous works only analyze semantics to the corresponding articles, while the repeated semantic interactions between fact descriptions and articles are ignored, thus the performance may be limited. In this paper, we propose a novel Recurrent Attention Network (RAN for short) to address this issue. Specifically, RAN utilizes a LSTM to obtain both fact description and article representations, then a recurrent process is designed to model the iterative interactions between fact descriptions and articles to make a correct match. Experimental results on real-world datasets demonstrate that our proposed model achieves significant improvements over the state-of-the-art methods.
The Leakage-Resilience Dilemma

Many control-flow-hijacking attacks rely on information leakage to disclose the location of gadgets. To address this, several leakage-resilient defenses, have been proposed that fundamentally limit the power of information leakage. Examples of such defenses include address-space re-randomization, destructive code reads, and execute-only code memory. Underlying all of these defenses is some form of code randomization. In this paper, we illustrate that randomization at the granularity of a page or coarser is not secure, and can be exploited by generalizing the idea of partial pointer overwrites, which we call the Relative ROP (RelROP) attack. We then analyzed more that 1,300 common binaries and found that 94% of them contained sufficient gadgets for an attacker to spawn a shell. To demonstrate this concretely, we built a proof-of-concept exploit against PHP 7.0.0. Furthermore, randomization at a granularity finer than a memory page faces practicality challenges when applied to shared libraries. Our findings highlight the dilemma that faces randomization techniques: course-grained techniques are efficient but insecure and fine-grained techniques are secure but impractical.
Reconstructing 3D Lung Shape from a Single 2D Image during the Deaeration Deformation Process using Model-based Data Augmentation

Three-dimensional (3D) shape reconstruction is particularly important for computer assisted medical systems, especially in the case of lung surgeries, where large deaeration deformation occurs.Recently, 3D reconstruction methods based on machine learning techniques have achieved considerable success in computer vision.However, it is difficult to apply these approaches to the medical field, because the collection of a massive amount of clinic data for training is impractical.To solve this problem, this paper proposes a novel 3D shape reconstruction method that adopts both data augmentation techniques and convolutional neural networks.In the proposed method, a deformable statistical model of the 3D lungs is designed to augment various training data.As the experimental results demonstrate, even with a small database, the proposed method can realize 3D shape reconstruction for lungs during a deaeration deformation process from only one captured 2D image.Moreover, the proposed data augmentation technique can also be used in other fields where the training data are insufficient.
Ballroom dance step type recognition by random forest using video and wearable sensor

The paper presents a hybrid ballroom dance step type recognition method using video and wearable sensors. Learning ballroom dance is very difficult for less experienced dancers as it has many complex types of steps. Therefore, our purpose is to recognize the various step types to support step learning. While the major approach to recognize dance performance is to utilize video, we cannot simply adopt it for ballroom dance because the dancers' images overlap each other. To solve the problem, we propose a hybrid step recognition method combining video and wearable sensors for enhancing its accuracy and robustness. We collect seven dancers' video and wearable sensors data including acceleration, angular velocity, and body parts location change. After that, we pre-process them and extract some feature values to recognize the step types. By adopting Random Forest for recognition, we confirmed that our approach achieved fl-score 0.760 for 13 step types recognition. Finally, we will open our dataset of ballroom dance to HASCA community for further research opportunities.
A Comparative Performance Study of Machine Learning Algorithms for Sentiment Analysis of Movie Viewers Using Open Reviews

The Internet facilitated the easy access to public opinions and reviews for any product or services. The collective opinions of people are significantly helpful for making decisions about any product or services. Movies are one of the most captivating pass times of the modern world on which people like to give their opinion/review. Movie reviews are personal opinions or comments shared via social media tool by a common viewer who has watched the movie. It provides an opportunity to know the outreach and response of the viewers to any film. Movie reviews influence the decision of prospective viewers as well as help producers, directors, and other stack holders for improving the quality aspect of the movie. Therefore, movie reviews play an important role in sentiment analysis of target viewers. In this research work, the sentiment analyses of viewers based on movie reviews using machine learning methods are discussed. The raw movie reviews are collected, and after performing preprocessing, features are extracted using bag of words, TF-IDF, bigram methods from text reviews. Various machine learning techniques including Naive Bayes classifier, Support Vector Machine, Decision trees, and ensemble learners are used with different feature extraction schemes to obtain a sentiment analysis model for positive or negative polarity in the movie review data sets. The performance of learners based sentiment analysis model is evaluated using accuracy, precision, recall, and f-measures. The objective of this research is to find the best classifier to test the reviews of movies given out by people so that we would know the overall general opinion of the audience. It is concluded that the set of classifiers can be used collaboratively to get effective results. Changes can be made from the very algorithmic level of the classifiers to gain better performance in the domain of study.
Faster quantum and classical SDP approximations for quadratic binary optimization

We give a quantum speedup for solving the canonical semidefinite programming relaxation for binary quadratic optimization. This class of relaxations for combinatorial optimization has so far eluded quantum speedups. Our methods combine ideas from quantum Gibbs sampling and matrix exponent updates. A de-quantization of the algorithm also leads to a faster classical solver. For generic instances, our quantum solver gives a nearly quadratic speedup over state-of-the-art algorithms. Such instances include approximating the ground state of spin glasses and MaxCut on Erdös-Rényi graphs. We also provide an efficient randomized rounding procedure that converts approximately optimal SDP solutions into approximations of the original quadratic optimization problem.
TEM analysis of nano- or near-nanoparticles in fault gouge from the Kaxiutata iron deposit (CHN) and the implications for ore body exploration

Deep and surface fault gouge samples were collected from the faults that cut across a concealed ore body in the Kaxiutata iron deposit in Inner Mongolia, China. The characteristics (category, size, shape, chemical component and association) of fault gouge nano- or near-nanoparticles were analyzed by transmission electron microscope (TEM). TEM analysis indicated the differences between the particles from the deep and surface fault gouge in terms of both species and composition. The deep particles mainly contain mafic silicates, metal oxides, sulfates, sulfides and carbonates, while the surface particles mainly contain oxides, hydroxides, silicates and other particles, such as Ba-Sr-bearing and Ce-bearing particles. Based on analysis of these particles, numerous ore-bearing particles such as Fe-, Zn-, Co-, Cu-, Bi- and Pt-bearing particles have been identified in the deep fault gouge. Ore-bearing particle anomalies (Fe-, Cu-, Zn- and Co-bearing particles) were observed in the surface fault gouge samples. In addition, numerous of nano- or near-nanoparticles (including ore-bearing particles) with visible crystal structure deformations and jagged edges were also observed in the fault gouge under TEM. This result indicated that ore-bearing particles could be formed through the movement of the fault that cut across a concealed ore body. When these ore-bearing particles transported by ascending gas flow passed through the fault zone, some of these particles could be adsorbed by minerals such as clay. Thus, we observed ore-bearing particle anomalies in the surface fault gouge.