Reviewerly/exHarmony huggingface dataset:
Semicentralized Deep Deterministic Policy Gradient in Cooperative StarCraft Games

In this article, we propose a novel semicentralized deep deterministic policy gradient (SCDDPG) algorithm for cooperative multiagent games. Specifically, we design a two-level actor-critic structure to help the agents with interactions and cooperation in the StarCraft combat. The local actor-critic structure is established for each kind of agents with partially observable information received from the environment. Then, the global actor-critic structure is built to provide the local design an overall view of the combat based on the limited centralized information, such as the health value. These two structures work together to generate the optimal control action for each agent and to achieve better cooperation in the games. Comparing with the fully centralized methods, this design can reduce the communication burden by only sending limited information to the global level during the learning process. Furthermore, the reward functions are also designed for both local and global structures based on the agents' attributes to further improve the learning performance in the stochastic environment. The developed method has been demonstrated on several scenarios in a real-time strategy game, i.e., StarCraft. The simulation results show that the agents can effectively cooperate with their teammates and defeat the enemies in various StarCraft scenarios.
Additive DenseNet: Dense connections based on simple addition operations

The Densely Connected Network (DenseNet) has been widely recognized as a highly competitive architecture in Deep Neural Networks. And its most outstanding property is called Dense Connections, which represent each layer’s input by concatenating all the preceding layers’ outputs and thus improve the performance by encouraging feature reuse to the extreme. However, it is Dense Connections that cause the challenge of dimension-enlarging, making DenseNet very resource-intensive and low efficiency. In the light of this, inspired by the Residual Network (ResNet), we propose an improved DenseNet named Additive DenseNet, which features replacing concatenation operations (used in Dense Connections) with addition operations (used in ResNet), and in terms of feature reuse, it upgrades addition operations to accumulating operations (namely ∑ (·)), thus enables each layer’s input to be the summation of all the preceding layers’ outputs. Consequently, Additive DenseNet can not only preserve the dimension of input from enlarging, but also retain the effect of Dense Connections. In this paper, Additive DenseNet is applied to text classification task. The experimental results reveal that compared to DenseNet, our Additive DenseNet can reduce the model complexity by a large margin, such as GPU memory usage and quantity of parameters. And despite its high resource economy, Additive DenseNet can still outperform DenseNet on 6 text classification datasets in terms of accuracy and show competitive performance for model training.
Introducing Sentiment Analysis of Textual Reviews in a Multi-Criteria Decision Aid System

Nowadays, most decision processes rely not only on the preferences of the decision maker but also on the public opinions about the possible alternatives. The user preferences have been heavily taken into account in the multi-criteria decision making field. On the other hand, sentiment analysis is the field of natural language processing devoted to the development of systems that are capable of analysing reviews to obtain their polarity. However, there have not been many works up to now that integrate the results of this process with the analysis of the alternatives in a decision support system. SentiRank is a novel system that takes into account both the preferences of the decision maker and the public online reviews about the alternatives to be ranked. A new mechanism to integrate both aspects into the ranking process is proposed in this paper. The sentiments of the reviews with respect to different aspects are added to the decision support system as a set of additional criteria, and the ELECTRE methodology is used to rank the alternatives. The system has been implemented and tested with a restaurant data set. The experimental results confirm the appeal of adding the sentiment information from the reviews to the ranking process.
A Real Pilot-Platform Implementation for Blockchain-Based Peer-to-Peer Energy Trading

The ever growing energy demand due to population growth, higher penetration of electric vehicles and smart appliances, as well as superior living standards, is a demanding incentive to the better utilization of conventional and renewable energy systems. Moreover, to facilitate the emerging requirements of prosumers to participate in the electricity market and monetise their efforts towards distributed energy deployment, traditional centralised energy trading architectures are no longer viable. In this context, blockchain-based ledger technology emerges as the most feasible solution which offers a peer to peer (P2P) energy trading platform providing a unique distributed local energy market model for beneficial energy exchanges among participants. This will represent a significant evolution for future smart grids. In this regard, this work provides a ground understanding as well as all the necessary technical details and procedures required to implement a pilot-platform P2P energy trading system based on blockchain technology. All the source codes have been uploaded and socialized. This may support academics and entrepreneurs at the initial development stage of these kind of initiatives.
The Utilization of Padding Scheme on Convolutional Neural Network for Cervical Cell Images Classification

Cervical cancer identification through pap-smear images analysis is a challenge for medicians, especially in distinguishing cells, between the normal and abnormal one. This study aims to create the classification model of Cervical Cell Images using the Convolutional Neural Network (CNN) algorithm. The dataset used is the image dataset SIPaKMeD. The CNN algorithm was implemented using the AlexNet architecture with and non-padding scheme. Padding is included in the experiments by adding the pixel 0 on the original images to improve the accuracy of the model. The experimental results show that using the utilization padding scheme on the AlexNet architecture can increase the accuracy of the model slightly significantly from 84.88% to 87.32%.
Quantum Resource Estimates of Grover’s Key Search on ARIA

Grover's algorithm provides a quantum attack against block ciphers by searching for a k-bit key using $$O(\sqrt{2^k})$$ calls to the cipher, when given a small number of plaintext-ciphertext pairs. Recent works by Grassl et al. in PQCrypto'16 and Almazrooie et al. in QIP'18 have estimated the cost of this attack against AES by analyzing the quantum circuits of the cipher. We present a quantum reversible circuit of ARIA, a Korean standardized block cipher that is widely deployed in government-to-public services. Firstly, we design quantum circuits for the main components of ARIA, and then combine them to construct the complete circuit of ARIA. We implement Grover's algorithm-based exhaustive key-search attack on ARIA. For all three variants of ARIA-{128, 192, 256}, we establish precise bounds for the number of qubits and the number of Clifford $$+T$$ gates that are required to implement Grover's algorithm. We also estimate the G-cost as the total number of gates, and DW-cost as the product of circuit depth and width. To find the circuit depth of various circuits such as squaring, multiplier, and permutation layer, we implement them in an open-source quantum computing platform QISKIT developed by IBM.
A Critical review of Credit Card Fraud Detection Techniques

Credit card fraud is one of the most important threats that affect people as well as companies across the world, particularly with the growing volume of financial transactions using credit cards every day. This puts the security of financial transactions at serious risk and calls for a fundamental solution. In this paper, we discuss various techniques of credit card fraud detection techniques that provide enhanced protection for credit card systems against a variety of frauds. We also compare these techniques in terms of accuracy, time, and cost, and outlined potential strengths and weaknesses to provide a guideline to choose the right technique.
Training DNN Model with Secret Key for Model Protection

In this paper, we propose a model protection method by using block-wise pixel shuffling with a secret key as a preprocessing technique to input images for the first time. The protected model is built by training with such preprocessed images. Experiment results show that the performance of the protected model is close to that of non-protected models when the key is correct, while the accuracy is severely dropped when an incorrect key is given, and the proposed model protection has enough robustness against fine-tuning attacks, while maintaining almost the same performance accuracy as that of using a nonprotected model.
Counterspeech and Ordinary Citizens: How? When?

Central to the still-nascent normative literature on counterspeech is the widespread belief that citizens should engage discursively with haters and the effects of hate speech. It is also increasingly clear that discursive engagement with intolerant members of society should be understood as a continuous and extended series of different and connected actions. Much less has been said about the ways that attempts in persuasion and direct responses to hate speech relate to one another and about when specific counterspeech actions should happen. This essay advances a more expansive and refined account of counterspeech, which is understood as a combination of continuous discursive engagement with intolerant members of society and acts of distancing from haters (shaming, correcting falsehoods, “Not in my name” campaigns, protests, and forms of discursive exit). After reconsidering discursive agency distribution (that is, who is an active participant, how, and when) around public hate speech, I show that continuous discursive engagement with intolerant members of society should be interrupted by visible acts of distancing when haters make hateful representative claims.
Detecting Fake Reviews Using Multidimensional Representations With Fine-Grained Aspects Plan

Due to the rapid growth of network data, the authenticity and reliability of network information have become increasingly important and have presented challenges. Most of the methods for fake review detection start with textual features and behavioral features. However, they are time-consuming and easily detected by fraudulent users. Although most of the existing neural network-based methods address the problems presented by the complex semantics of reviews, they do not account for the implicit patterns among users, reviews, and products; additionally, they do not consider the usefulness of information regarding fine-grained aspects in identifying fake reviews. In this paper, we propose an attention-based multilevel interactive neural network model with aspect constraints that mines the multilevel implicit expression mode of reviews and integrates four dimensions, namely, users, review texts, products and fine-grained aspects, into review representations. We model the relationships between users and products and use these relationships as a regularization term to redefine the model’s objective function. The experimental results from three public datasets show that the model that we propose is superior to the state-of-the-art methods; thus showing the effectiveness and portability of our model.
Neural network forecasting of news feeds

The paper considers a problem of forecasting of news feeds content. Analysis of existing approaches to this problem solution reveals the need for development of methods with enhanced forecasting capabilities. A method is proposed with expanded accounting for space and time relationships of the processed data. The method is revealed through an example of a neural network forecasting system that implements it. Implementation includes data retrieval from news feeds, their special preprocessing, coding and forecasting of words sets and their interconnections, followed by highlighting news topics and describing the of news feeds content. Some variants of stream recurrent neural networks with spiral layer structures were investigated with due regard to their forecasting capabilities under direction and strength control of the associative call of signals from the network memory. The paper also presents and discusses experimental results, a description of the methodological contribution and recommendations on the method practical application.
Federated Learning with Gaussian Differential Privacy

In recent years, federated learning has rapidly become a new research hotspot in the field of secure machine learning. However, unprotected traditional federated learning can easily leak information, which unable to meet users' anonymization needs. Differential privacy is widely used in privacy protection due to its excellent privacy quantification properties, but differential privacy as a lossy protection will reduce the accuracy of the machine learning model. This paper proposes a federated learning algorithm based on Gaussian differential privacy, Noisy-FL, which can more accurately track the changes in privacy loss during model training. Noisy-FL can achieve user-level privacy protection while increasing the number of communication rounds compared to the previous algorithm. Experimental results show that the new algorithm can increase the number of communication rounds by 3 times, and when the total number of clients is small, the model accuracy of the Noisy-FL algorithm is increased by 10% compared to the previous algorithm.
Lattice-Based IBE with Equality Test Supporting Flexible Authorization in the Standard Model

Identity-based encryption with equality test supporting flexible authorization (IBEET-FA) allows the equality test of underlying messages of two ciphertexts while strengthens privacy protection by allowing users (identities) to control the comparison of their ciphertexts with others. IBEET by itself has a wide range of useful applicable domain such as keyword search on encrypted data, database partitioning for efficient encrypted data management, personal health record systems, and spam filtering in encrypted email systems. The flexible authorization will enhance privacy protection of IBEET. In this paper, we propose an efficient construction of IBEET-FA system based on the hardness of learning with error (LWE) problem. Our security proof holds in the standard model.
Robust Semantic Segmentation in Adverse Weather Conditions by means of Fast Video-Sequence Segmentation

Computer vision tasks such as semantic segmentation perform very well in good weather conditions, but if the weather turns bad, they have problems to achieve this performance in these conditions. One possibility to obtain more robust and reliable results in adverse weather conditions is to use video-segmentation approaches instead of commonly used single-image segmentation methods. Video-segmentation approaches capture temporal information of the previous video-frames in addition to current image information, and hence, they are more robust against disturbances, especially if they occur in only a few frames of the video-sequence. However, video-segmentation approaches, which are often based on recurrent neural networks, cannot be applied in real-time applications anymore, since their recurrent structures in the network are computational expensive. For instance, the inference time of the LSTM-ICNet, in which recurrent units are placed at proper positions in the single-segmentation approach ICNet, increases up to 61 percent compared to the basic ICNet. Hence, in this work, the LSTM-ICNet is sped up by modifying the recurrent units of the network so that it becomes real-time capable again. Experiments on different datasets and various weather conditions show that the inference time can be decreased by about 23 percent by these modifications, while they achieve similar performance than the LSTM-ICNet and outperform the single-segmentation approach enormously in adverse weather conditions.
Classical Machine Learning Classification for Javanese Traditional Food Image

Indonesia is a culturally rich nation with more than three hundred ethnic groups. This sheer number of ethnic groups reflects the country's diverse culture. One of the identities that could be associated with a group of people is its cuisine. As with the high number of ethnic groups, the diversity of Indonesian traditional food is also very high. However, the diversity of food is threatened by the current food systems, which could endanger food security of a population. To prevent this issue, a traditional food database system is created to monitor the food systems of each area in Indonesia. In this research, automatic traditional food classification is developed as one of the main features of this system. There were 17 Indonesian traditional foods from the Java area that were acquired and used as a dataset for this research. Several key features of the food dataset were extracted using various methods. The data were then classified using various machine learning algorithms. From the experiment, Random Forest classifier achieved the highest accuracy compared to other classical machine learning methods.
An Improved MAHAKIL Oversampling Method for Imbalanced Dataset Classification

In the process of imbalanced dataset classification, traditional oversampling technologies do not take into the account of the distribution characteristics of data sets, which easily results in low classification accuracy of positive samples. In order to improve the recognition rate of positive samples, the algorithm combining the K-means clustering with the MAHAKIL oversampling is proposed in this article. Firstly, the K-means clustering algorithm is used to divide the positive samples into K clusters. The samples in each cluster are then divided into two initial populations according to the magnitude of the Mahalanobis distance. Finally, the crossover operator of the genetic algorithm is used to continuously synthesize new data samples to balance the data set. The experimental results show that when K-means MAHAKIL oversampling algorithm is combined with different classification algorithms, it can achieve better classification performance, which verifies the effectiveness of the algorithm.
Tailoring Asymmetric Lossy Channels to Test the Robustness of Mesoscopic Quantum States of Light

In the past twenty years many experiments have demonstrated that quantum states of light can be used for secure data transfer, despite the presence of many noise sources. In this paper we investigate, both theoretically and experimentally, the role played by a statistically-distributed asymmetric amount of loss in the degradation of nonclassical photon-number correlations between the two parties of multimode twin-beam states in the mesoscopic intensity regime. To be as close as possible to realistic scenarios, we consider two different statistical distributions of such a loss, a Gaussian distribution and a log-normal one. The results achieved in the two cases show to what extent the involved parameters, both those connected to loss and those describing the employed states of light, preserve nonclassicality.
Preserving Population Diversity Based on Transformed Semantics in Genetic Programming for Symbolic Regression

Population diversity plays an important role in avoiding premature convergence in evolutionary techniques including genetic programming (GP). Obtaining an adequate level of diversity during the evolutionary process has became a concern of many previous researches in GP. This work proposes a new novelty metric for entropy-based diversity measure for GP. The new novelty metric is based on the transformed semantics of models in GP, where the semantics are the set of outputs of a model on the training data and principal component analysis is used for a transformation of the semantics. Based on the new novelty metric, a new diversity preserving framework, which incorporates a new fitness function and a new selection operator, is proposed to help GP achieve a good balance between the exploration and the exploitation, thus enhancing its learning and generalization performance. Compared with two stat-of-the-art diversity preserving methods, the new method can generalize better and reduce the overfitting trend more effectively in most cases. Further examinations on the properties of the search process confirm that the new framework notably enhances the evolvability and locality of GP.
Quantum Zeno effect with partial measurement and noisy dynamics

We study the quantum Zeno effect (QZE) induced by continuous partial measurement in the presence of short-correlated noise in the system Hamiltonian. We study the survival probability and the onset of the QZE as a function of the measurement strength and find that, depending on the noise parameters, the quantum Zeno effect can be enhanced or suppressed by the noise in different regions of parameter space. Notably, the conditions for the enhancement of the QZE are different when determined by the short-time or long-time behavior of the survival probability, or by the measurement strength marking the onset of the quantum Zeno regime.Received 26 June 2020Accepted 10 December 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.043420Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum Zeno dynamicsQuantum information processingQuantum InformationGeneral Physics
An efficient outlier detection method for data streams based on closed frequent patterns by considering anti-monotonic constraints

In some practical applications, users pay more attention to a small part of the data that they are interested in, rather than full sets of the data elements, so it is extremely important to improve the security of this small part of the data. For the existing association-based outlier detection methods, they detect the outliers from full set of the data streams, which results in having users to wait for a long period to obtain the detection results. In contrast, most distributions of the outliers are meaningless for the users. To solve this problem, by considering user-specified anti-monotonic constraints, this paper proposes an efficient outlier detection method based on closed frequent patterns for data streams. Also, four deviation indices are designed to accurately calculate the outlier score of each transaction in the sliding window by considering more influencing factors. Then, the top k transactions with the largest outlier score are returned as outliers. Extensive experimental results show that the proposed method can accurately find the outliers from the data streams that satisfy the anti-monotonic constraints with less time cost.