Reviewerly/exHarmony huggingface dataset:
Skin Color Segmentation Based on Artificial Neural Network Improved by a Modified Grasshopper Optimization Algorithm

One of the applications of image processing and computer vision is to detect the skin regions for a wide range of human–computer interaction and content based utilizations. Detecting nude parts in the movies, face detection, tracking of human body parts, and people recognizing in multimedia databases are a small part of its applications. Therefore, designing an efficient technique for skin area detection can help a lot to the determined applications. The grasshopper optimization algorithm is a new optimization algorithm evolutionary algorithm which is recently introduced to solve optimization problems. The main purpose of this paper is to propose a newly developed version of this algorithm to optimize the weights of the backpropagation neural network to design a good segmentation tool for skin area segmentation. The method has been compared with the traditional multi-layer perception and the ICA-MLP to declare the proposed method’s efficiency.
Towards Detecting Inconsistent Comments in Java Source Code Automatically

A number of tools are available to software developers to check consistency of source code during software evolution. However, none of these tools checks for consistency of the documentation accompanying the code. As a result, code and documentation often diverge, hindering program comprehension. This leads to errors in how developers use source code, especially in the case of APIs of reusable libraries. We propose a technique and a tool, upDoc, to automatically detect code-comment inconsistency during code evolution. Our technique builds a map between the code and its documentation, ensuring that changes in the code match the changes in respective documentation parts. We conduct a preliminary evaluation using inconsistency examples from an existing dataset of Java open source projects, showing that upDoc can successfully detect them. We present a roadmap for the further development of the technique and its evaluation.
Blind MV-based video steganalysis based on joint inter-frame and intra-frame statistics

Abstract Despite all its irrefutable benefits, the development of steganography methods has sparked ever-increasing concerns over steganography abuse in recent decades. To prevent the inimical usage of steganography, steganalysis approaches have been introduced. Since motion vector manipulation leads to random and indirect changes in the statistics of videos, MV-based video steganography has been the center of attention in recent years. In this paper, we propose a 54-dimentional feature set exploiting spatio-temporal features of motion vectors to blindly detect MV-based stego videos. The idea behind the proposed features originates from two facts. First, there are strong dependencies among neighboring MVs due to utilizing rate-distortion optimization techniques and belonging to the same rigid object or static background. Accordingly, MV manipulation can leave important clues on the differences between each MV and the MVs belonging to the neighboring blocks. Second, a majority of MVs in original videos are locally optimal after decoding concerning the Lagrangian multiplier, notwithstanding the information loss during compression. Motion vector alteration during information embedding can affect these statistics that can be utilized for steganalysis. Experimental results have shown that our features’ performance far exceeds that of state-of-the-art steganalysis methods. This outstanding performance lies in the utilization of complementary spatio-temporal statistics affected by MV manipulation as well as feature dimensionality reduction applied to prevent overfitting. Moreover, unlike other existing MV-based steganalysis methods, our proposed features can be adjusted to various settings of the state-of-the-art video codec standards such as sub-pixel motion estimation and variable-block-size motion estimation.
In Search for Design Elements: A New Perspective for Employing Ethnography in Human-Computer Interaction Design Research

Design ethnography has been widely used in Human-Computer Interaction (HCI) to understand people’s everyday behaviors, in order to build technologies capable of meeting users’ needs. Building on top of the recent debate on ethnography within HCI, this article proposes to employ reflexivity and theoretical pluralism to ground a new way of using design ethnography in HCI, directly envisioning novel designs during the fieldwork. Inspired by design practices like biomimicry, I describe the figure of the ethno-designer, a digital design ethnographer who dives into ‘successful’ virtual environments in search for insightful design patterns, with the purpose of creating new designs in other, even distant, contexts. In this perspective, the fieldwork becomes an incessant source of inspiration for identifying effective ‘design elements’, understanding how they work and their ‘experiential effects’, and producing design implications to create novel technologies across multiple application domains.
HGNet: Hybrid Generative Network for Zero-Shot Domain Adaptation

Domain Adaptation as an important tool aims to explore a generalized model trained on well-annotated source knowledge to address learning issue on target domain with insufficient or even no annotation. Current approaches typically incorporate data from source and target domains for training stage to deal with domain shift. However, most domain adaptation tasks generally suffer from the problem that measuring the domain shift tends to be impossible when target data is inaccessible. In this paper, we propose a novel algorithm, Hybrid Generative Network (HGNet) for Zero-shot Domain Adaptation, which embeds an adaptive feature separation (AFS) module into generative architecture. Specifically, AFS module can adaptively distinguish classification-relevant features from classification-irrelevant ones to learn domain-invariant and discriminative representations when task-relevant target instances are invisible. To learn high-quality feature representation, we also develop hybrid generative strategy to ensure the uniqueness of feature separation and completeness of semantic information. Extensive experimental results on several benchmarks illustrate that our method achieves more promising results than state-of-the-art approaches.
A Price-Incentive Resource Auction Mechanism Balancing the Interests Between Users and Cloud Service Provider

For a cloud service provider, it necessitates an emerging cloud ecosystem to consolidate the existing users and attract more potential users, further gaining its market share. Therefore, in this article, we design a price-incentive resource auction mechanism in cloud environment. In response to the cloud resource price, each user synthesizes her bidding budget and QoS requirement, and purchases cloud resources according to her resource demand in a strategic manner. The cloud service provider, meanwhile, can regulate the resource demands of users through conducting a market-based pricing strategy, against too low prices to cover the operational costs (i.e., energy costs) or too high prices resulting in user churn. In virtue of an elaborate market-based pricing strategy, the interests of users and the cloud service provider are balanced. Our price-incentive resource auction mechanism targets to stimulate maximum users willing to purchase resources and perform their applications at the cloud, on the premise of a minimum profit rate guaranteed for the cloud service provider. It is also able to provide budge balance and truthfulness guarantee, and satisfy the envy-freeness. In order to carry out the above objectives, we carefully design the user utility function reflecting the complicated user interest, and formulate our resource pricing and auction problem as a bin packing problem, which has non-polynomial computational complexity. Regarding the NP-hardness of optimization problem and the concavity of user utility, we present a computational-efficient ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1+\epsilon $ </tex-math></inline-formula> )-approximate algorithm namely PIRA. Finally, we conduct simulations based on the real-world dataset to validate the effectiveness of our proposed approach.
Neural Batch Sampling with Reinforcement Learning for Semi-supervised Anomaly Detection

We are interested in the detection and segmentation of anomalies in images where the anomalies are typically small (i.e., a small tear in woven fabric, broken pin of an IC chip). From a statistical learning point of view, anomalies have low occurrence probability and are not from the main modes of a data distribution. Learning a generative model of anomalous data from a natural distribution of data can be difficult because the data distribution is heavily skewed towards a large amount of non-anomalous data. When training a generative model on such imbalanced data using an iterative learning algorithm like stochastic gradient descent (SGD), we observe an expected yet interesting trend in the loss values (a measure of the learned models performance) after each gradient update across data samples. Naturally, as the model sees more non-anomalous data during training, the loss values over a non-anomalous data sample decreases, while the loss values on an anomalous data sample fluctuates. In this work, our key hypothesis is that this change in loss values during training can be used as a feature to identify anomalous data. In particular, we propose a novel semi-supervised learning algorithm for anomaly detection and segmentation using an anomaly classifier that uses as input the loss profile of a data sample processed through an autoencoder. The loss profile is defined as a sequence of reconstruction loss values produced during iterative training. To amplify the difference in loss profiles between anomalous and non-anomalous data, we also introduce a Reinforcement Learning based meta-algorithm, which we call the neural batch sampler, to strategically sample training batches during autoencoder training. Experimental results on multiple datasets with a high diversity of textures and objects, often with multiple modes of defects within them, demonstrate the capabilities and effectiveness of our method when compared with existing state-of-the-art baselines.
Calibration and evaluation of sunshine-based empirical models for estimating daily solar radiation in Iran

This research aimed to find a proper model to estimate daily solar radiation in terms of specific climate conditions of 51 weather stations over Iran. Twenty-one sunshine-based empirical models were calibrated and evaluated. Two different quality control methods were used to reject data error in daily solar radiation dataset collected from 2007 to 2017. The empirical constant coefficients were calculated using linear and nonlinear regression models. Statistical error comparisons between observation and models-based estimates were performed via R2, RMSE, RRMSE, MAPE, MBE and MAE measures. Finally, following the models-based estimates, monthly solar radiation maps were created for the entire country using ordinary Kriging interpolation method. The results revealed that for the largest part of the regions, the highest correlation between Observation and Models-Based Estimates was detected while implementing regression-based models (models 4 and 12). However, the highest correlation between the values of the measured radiation and the models-based estimates over the entire country belonged to the linear model 15. Moreover, there was an opposite relationship between "a" and "b" coefficients in model 15. The results of models 4 and 12 show that these models can perform well for estimating solar radiation in the "C" and "F" climate zones, respectively.
Hacking single-photon avalanche detectors in quantum key distribution via pulse illumination

Quantum key distribution (QKD) has been proved to be information-theoretically secure in theory. Unfortunately, the imperfect devices in practice compromise its security. Thus, to improve the security property of practical QKD systems, a commonly used method is to patch the loopholes in the existing QKD systems. However, in this work, we show an adversary's capability of exploiting the imperfection of the patch itself to bypass the patch. Specifically, we experimentally demonstrate that, in the detector under test, the patch of photocurrent monitor against the detector blinding attack can be defeated by the pulse illumination attack proposed in this paper. We also analyze the secret key rate under the pulse illumination attack, which theoretically confirmed that Eve can conduct the attack to learn the secret key. This work indicates the importance of inspecting the security loopholes in a detection unit to further understand their impacts on a QKD system. The method of pulse illumination attack can be a general testing item in the security evaluation standard of QKD.
Recommendation Systems with Machine Learning

Recommender systems are a subclass of information filtering systems. These systems are specialized software components, which usually make part of a larger software system, but can also be standalone tools. A recommender system's main goal is to provide the user software suggestions for items that can be useful. The suggestions are related to different decision-making mechanisms, different techniques, such as, what product to buy, what movie to watch, or what vacation to reserve. In the context of recommender systems, the general term “item” refers to what the system is actually recommending to its users. The paper presents the development and the comparison of multiple recommendation systems, capable of making item suggestions, based on user, item and user-item interaction data, using different machine learning algorithms. Also, the paper deals with finding different ways of using machine learning models to create recommendation systems, training, evaluating and comparing the different methods in order to provide a general but accurate solution for ranking prediction.
General state transitions with exact resource morphisms: a unified resource-theoretic approach

Given a non-empty closed convex subset $\mathsf{F}$ of density matrices, we formulate conditions that guarantee the existence of an $\mathsf{F}$-morphism (namely, a completely positive trace-preserving linear map that maps $\mathsf{F}$ into itself) between two arbitrarily chosen density matrices. While we allow errors in the transition, the corresponding map is required to be an exact $\mathsf{F}$-morphism. Our findings, though purely geometrical, are formulated in a resource-theoretic language and provide a common framework that comprises various resource theories, including the resource theories of bipartite and multipartite entanglement, coherence, athermality, and asymmetric distinguishability. We show how, when specialized to some situations of physical interest, our general results are able to unify and extend previous analyses. We also study conditions for the existence of maximally resourceful states, defined here as density matrices from which any other one can be obtained by means of a suitable $\mathsf{F}$-morphism. Moreover, we quantitatively characterize the paradigmatic tasks of optimal resource dilution and distillation, as special transitions in which one of the two endpoints is maximally resourceful.
Signature verification using geometrical features and artificial neural network classifier

Signature verification has been one of the major researched areas in the field of computer vision. Many financial and legal organizations use signature verification as an access control and authentication. Signature images are not rich in texture; however, they have much vital geometrical information. Through this work, we have proposed a signature verification methodology that is simple yet effective. The technique presented in this paper harnesses the geometrical features of a signature image like center, isolated points, connected components, etc. and, with the power of artificial neural network classifier, classifies the signature image based on their geometrical features. Publicly available dataset MCYT, BHSig260 (contains the image of two regional languages Bengali and Hindi) has been used in this paper to test the effectiveness of the proposed method. We have received a lower equal error rate on MCYT 100 dataset and higher accuracy on the BHSig260 dataset.
Strong convergence of quantum channels: Continuity of the Stinespring dilation and discontinuity of the unitary dilation

We show that a sequence $\{\Phi_n\}$ of quantum channels strongly converges to a quantum channel $\Phi_0$ if and only if there exist a common environment for all the channels and a corresponding sequence $\{V_n\}$ of Stinespring isometries strongly converging to a Stinespring isometry $V_0$ of the channel $\Phi_0$. We also give a quantitative description of the above characterization of the strong convergence in terms of the appropriate metrics on the sets of quantum channels and Stinespring isometries. As a result, the uniform selective continuity of the complementary operation with respect to the strong convergence is established. We show discontinuity of the unitary dilation by constructing a strongly converging sequence of channels which can not be represented as a reduction of a strongly converging sequence of unitary channels. The Stinespring representation of strongly converging sequences of quantum channels allows to prove the lower semicontinuity of the entropic disturbance as a function of a pair (channel, input ensemble). Some corollaries of this property are considered.
I3D-Shufflenet Based Human Action Recognition

In view of difficulty in application of optical flow based human action recognition due to large amount of calculation, a human action recognition algorithm I3D-shufflenet model is proposed combining the advantages of I3D neural network and lightweight model shufflenet. The 5 × 5 convolution kernel of I3D is replaced by a double 3 × 3 convolution kernels, which reduces the amount of calculations. The shuffle layer is adopted to achieve feature exchange. The recognition and classification of human action is performed based on trained I3D-shufflenet model. The experimental results show that the shuffle layer improves the composition of features in each channel which can promote the utilization of useful information. The Histogram of Oriented Gradients (HOG) spatial-temporal features of the object are extracted for training, which can significantly improve the ability of human action expression and reduce the calculation of feature extraction. The I3D-shufflenet is testified on the UCF101 dataset, and compared with other models. The final result shows that the I3D-shufflenet has higher accuracy than the original I3D with an accuracy of 96.4%.
Neural Feature-aware Recommendation with Signed Hypergraph Convolutional Network

Understanding user preference is of key importance for an effective recommender system. For comprehensive user profiling, many efforts have been devoted to extract user feature-level preference from the review information. Despite effectiveness, existing methods mostly assume linear relationships among the users, items, and features, and the collaborative information is usually utilized in an implicit and insufficient manner, which limits the recommender capacity in modeling users’ diverse preferences. For bridging this gap, in this article, we propose to formulate user feature-level preferences by a neural signed hypergraph and carefully design the information propagation paths for diffusing collaborative filtering signals in a more effective manner. By taking the advantages of the neural model’s powerful expressiveness, the complex relationship patterns among users, items, and features are sufficiently discovered and well utilized. By infusing graph structure information into the embedding process, the collaborative information is harnessed in a more explicit and effective way. We conduct comprehensive experiments on real-world datasets to demonstrate the superiorities of our model.
Online Anomaly Detection Leveraging Stream-Based Clustering and Real-Time Telemetry

Recent technology evolution allows network equipment to continuously stream a wealth of “telemetry” information, which pertains to multiple protocols and layers of the stack, at a very fine spatial-grain and high-frequency. This deluge of telemetry data clearly offers new opportunities for network control and troubleshooting, but also poses a serious challenge for what concerns its real-time processing. We tackle this challenge by applying streaming machine-learning techniques to the continuous flow of control and data-plane telemetry data, with the purpose of real-time detection of anomalies. In particular, we implement an anomaly detection engine that leverages DenStream, an unsupervised clustering technique, and apply it to features collected from a large-scale testbed comprising tens of routers traversed up to 3Terabit/s worth of real application traffic. We contrast DenStream with offline algorithms such as DBScan and Local Outlier Factor (LOF), as well as online algorithms such as the windowed version of DBScan, ExactSTORM, Continuous Outlier Detection (COD) and Robust Random Cut Forest (RRCF). Our experimental campaign compares these seven algorithms under both accuracy and computational complexity viewpoints: results testify that DenStream (i) achieves detection results on par with RRCF, the best performing algorithm and (ii) is significantly faster than other approaches, notably over two orders of magnitude faster than RRCF. In spirit with the recent trend toward reproducibility of results, we make our code available as open source to the scientific community.
Eventually Entanglement Breaking Markovian Dynamics: Structure and Characteristic Times

We investigate entanglement breaking times of Markovian evolutions in discrete and continuous time. In continuous time, we characterize which Markovian evolutions are eventually entanglement breaking, that is, evolutions for which there is a finite time after which any entanglement initially present has been destroyed by the noisy evolution. In the discrete time framework, we consider the entanglement breaking index, that is, the number of times a quantum channel has to be composed with itself before it becomes entanglement breaking. The PPT-square conjecture is that every PPT quantum channel has an entanglement breaking index of at most 2; we prove that every faithful PPT quantum channel has a finite entanglement breaking index, and more generally, any faithful PPT CP map whose Hilbert-Schmidt adjoint is also faithful is eventually entanglement breaking. We also provide a method to obtain concrete bounds on this index for any faithful quantum channel. To obtain these estimates, we use a notion of robustness of separability to obtain bounds on the radius of the largest separable ball around faithful product states. We also extend the framework of Poincar\'e inequalities for nonprimitive semigroups to the discrete setting to quantify the convergence of quantum semigroups in discrete time, which is of independent interest.