Reviewerly/exHarmony huggingface dataset:
Cooperative concurrent games

In rational verification, the aim is to verify which temporal logic properties will obtain in a multi-agent system, under the assumption that agents (“players”) in the system choose strategies for acting that form a game theoretic equilibrium. Preferences are typically defined by assuming that agents act in pursuit of individual goals, specified as temporal logic formulae. To date, rational verification has been studied using non-cooperative solution concepts—Nash equilibrium and refinements thereof. Such non-cooperative solution concepts assume that there is no possibility of agents forming binding agreements to cooperate, and as such they are restricted in their applicability. In this article, we extend rational verification to cooperative solution concepts, as studied in the field of cooperative game theory. We focus on the core, as this is the most fundamental (and most widely studied) cooperative solution concept. We begin by presenting a variant of the core that seems well-suited to the concurrent game setting, and we show that this version of the core can be characterised using ATL⁎. We then study the computational complexity of key decision problems associated with the core, which range from problems in PSpace to problems in 3ExpTime. We also investigate conditions that are sufficient to ensure that the core is non-empty, and explore when it is invariant under bisimilarity. We then introduce and study a number of variants of the main definition of the core, leading to the issue of credible deviations, and to stronger notions of collective stable behaviour. Finally, we study cooperative rational verification using an alternative model of preferences, in which players seek to maximise the mean-payoff they obtain over an infinite play in games where quantitative information is allowed.
A semi-blind online dictionary learning approach for fMRI data

Online dictionary learning (ODL) has been applied to extract brain networks from functional magnetic resonance imaging (fMRI) data in recent year. Moreover, the supervised dictionary learning (SDL) that fixed the task stimulus curves as predefined atoms was proposed to improve ODL for functional networks separation. However, SDL cannot estimate the real time courses underlying the brain networks and cannot be applied to the inter-network connectivity analysis. This study aimed at investigating how to add the temporal prior information to ODL to extract the accurate task-related brain networks and the corresponding time courses. To improve the performance of ODL, we propose a semi-blind ODL (semi-ODL) method that incorporates temporal prior information of the task paradigm into the dictionary updating process and optimizes the direction of one or more specific atoms "close" to the task time courses. Results of the simulated and real fMRI experiment revealed that semi-ODL extracted more accurate task-related component and time courses than ODL and SDL. For one-task fMRI data, semi-ODL and Infomax-ICA showed similar detection power in most cases. The semi-ODL outperformed ODL, SDL in robustness to noise, spatial detection power and time course estimation. Moreover, semi-ODL showed comparable performance to Infomax-ICA for one-task fMRI data and outperformed Infomax-ICA in extracting the components related to each task from multi-task fMRI data. The semi-ODL method is potentially useful to reveal brain networks underlying various cognitive tasks and the interactions between task-related brain networks.
A Chinese acoustic model based on convolutional neural network

Speech recognition has always been one of the research focuses in the field of human-computer communication and interaction. The main purpose of automatic speech recognition (ASR) is to convert speech waveform signals into text. Acoustic model is the main component of ASR, which is used to connect the observation features of speech signals with the speech modeling units. In recent years, deep learning has become the mainstream technology in the field of speech recognition. In this paper, a convolutional neural network architecture composed of VGG and Connectionist Temporal Classification (CTC) loss function was proposed for speech recognition acoustic model. Traditional acoustic model training is based on frame-level labels with cross-entropy criterion, which requires a tedious label alignment procedure. The CTC loss was adopted to automatically learn the alignments between speech frames and label sequences, such that the training process is end-to-end. The architecture can exploit temporal and spectral structures of speech signals simultaneously. Batch normalization (BN) technique was used for normalizing each layers input to reduce internal covariance shift. To prevent overfitting, dropout technique was used during training to improve network generalization ability. The speech signal was transformed into a spectral image through a series of processing to be the input of the neural network. The input feature is 200 dimensions, and output labels of acoustic mode is 415 Chinese pronunciation without pitch. The experimental results demonstrated that the proposed model achieves the Character error rate (CER) of 17.97% and 23.86% on public Mandarin speech corpus, AISHELL-1 and ST-CMDS-20170001_1, respectively.
Deterministic transformations of three-qubit entangled pure states

The states of three-qubit systems split into two inequivalent types of genuine tripartite entanglement, namely the Greenberger-Horne-Zeilinger (GHZ) type and the $W$ type. A state belonging to one of these classes can be stochastically transformed only into a state within the same class by local operations and classical communications. We provide local quantum operations, consisting of the most general two-outcome measurement operators, for the deterministic transformations of three-qubit pure states in which the initial and the target states are in the same class. We explore these transformations, originally having standard GHZ and standard $W$ states, under the local measurement operations carried out by a single party and $p$ ($p=2,3$) parties (successively). We find a notable result that the standard GHZ state cannot be deterministically transformed to a GHZ-type state in which all its bipartite entanglements are nonzero, i.e., a transformation can be achieved with unit probability when the target state has at least one vanishing bipartite concurrence.
Improvement of Face Recognition with Gabor, PCA, and SVM Under Complex Illumination Conditions

Complex illumination condition is one of the most critical challenging problems for practical face recognition. However, numerous studies have had no effective solutions reported for full illumination variation of face images in the facial recognition research field. In order to effectively solve full illumination variation problem, we propose a novel approach for illumination normalization for facial images based on the enhanced contrast method of histogram equalization (HE) and fusion of illumination estimations (FOIE). Then, feature extraction is applied with consideration of both Gabor wavelet and principal component analysis methods to process illumination normalization. Next, a support vector machine classifier (SVM) is used for face classification. Experimental results show that superior performance can be obtained in the developed approach by comparisons with some state-of-the-arts.
An intuitionistic logic for preference relations

Abstract We investigate in intuitionistic first-order logic various principles of preference relations alternative to the standard ones based on the transitivity and completeness of weak preference. In particular, we suggest two ways in which completeness can be formulated while remaining faithful to the spirit of constructive reasoning, and we prove that the cotransitivity of the strict preference relation is a valid intuitionistic alternative to the transitivity of weak preference. Along the way, we also show that the acyclicity axiom is not finitely axiomatizable in first-order logic.
Spatial Positioning Token (SPToken) for Smart Mobility

We introduce a permissioned distributed ledger technology (DLT) design for crowdsourced smart mobility applications. This architecture is based on a directed acyclic graph architecture (similar to the IOTA tangle) and uses both Proof-of-Work and Proof-of-Position mechanisms to provide protection against spam attacks and malevolent actors. In addition to enabling individuals to retain ownership of their data and to monetize it, the architecture also is suitable for distributed privacy-preserving machine learning algorithms, is lightweight, and can be implemented in simple internet-of-things (IoT) devices. To demonstrate its efficacy, we apply this framework to reinforcement learning settings where a third party is interested in acquiring information from agents. In particular, one may be interested in sampling an unknown vehicular traffic flow in a city, using a DLT-type architecture and without perturbing the density, with the idea of realizing a set of virtual tokens as surrogates of real vehicles to explore geographical areas of interest. These tokens, whose authenticated position determines write access to the ledger, are thus used to emulate the probing actions of commanded (real) vehicles on a given planned route by "jumping" from a passing-by vehicle to another to complete the planned trajectory. Consequently, the environment stays unaffected (i.e., the autonomy of participating vehicles is not influenced by the algorithm), regardless of the number of emitted tokens. The design of such a DLT architecture is presented, and numerical results from large-scale simulations are provided to validate the proposed approach.
High-Fidelity Reversible Data Hiding in JPEG Images Based on Two-Dimensional Histogram

Recently, a series of excellent reversible data hiding (RDH) algorithms based on two-dimensional histogram modification have been proposed. However, the coefficient-pairs of these algorithms are obtained via fixed combination method, which limits the further improvement of algorithm performance. Based on this consideration, a new combination method with pairwise coefficients is proposed in this paper. Firstly, the coefficients are divided into 32 positions according to Zigzag scanning sequence. Then, we pair the coefficients in the same position, and combination mode of the coefficients is determined by the number of expandable pairs. Unlike the fixed combination method, the coefficients of proposed method are adaptively combined into pairs according to image features. Moreover, we propose a coefficient-pairs selection strategy. The coefficient-pairs which have little effect on image quality are selected for payload embedding, thereby distortion of the mark image is limited. The obtained algorithm has better peak signal to noise ratio (PSNR) and embedding capacity over some state-of-the-art algorithms, showing the superiority of our method.
Behavior Matching between Different Domains based on Canonical Correlation Analysis

With the recent proliferation of e-commerce services, online shopping has become more and more popular among customers. Because it is necessary to recommend proper items to customers, to improve the accuracy of recommendation, high-performance recommender systems are required. However, current recommender systems are mainly based on information of their own domain, resulting in low accurate recommendation for customers with limited purchasing histories. The accuracy may suffer due to a lack of information. In order to use information from other domains, it is necessary to associate behaviors in different domains of the behaviorally related users. This paper presents a preliminary analysis of matching behaviors of the behaviorally related users in different domains. The result shows that we got a better prediction rate than linear regression.
The attitude of Jordanian customers towards virtual stores

The present study aims to explore the attitude of Jordanian customers towards virtual stores. The study was conducted during October, 2018. The population of the study involved all Jordanian customers. A questionnaire was developed to collect the data. The study sample consisted of 319 customers who received the questionnaires which were all retrieved. Thus, the response rate was 100%. However, 32 questionnaires were excluded because they were not fully filled. Thus, the sample consisted of 287 Jordanian customers. The findings reveal that the majority of the Jordanian customers have positive attitude towards virtual stores as they believe that such shops save their time and money and provide excellent customer service. Thus, the researchers recommend enforcing legislations that protect e-customers in Jordan.
The Unlearnable Checkerboard Pattern

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms. Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.
Research and Practice on Hierarchical Supervisory Strategy of Mixed Teaching

The core of mixed teaching is to emphasize the integration of teacher's dominant role and student's principal position. Teacher leads the whole learning process in the mixed teaching process. According to studentsâ€™ learning conditions, teacher provides individualized guidance, and urge students to accomplish the learning content as required. In this paper, the learning features are analyzed, and a hierarchical supervision model is designed according to the learning data in mixed teaching. The hierarchical supervision model provides a basis for teachers to guide students in individualized learning. The hierarchical supervision strategy has been applied in the course of â€œUniversity Computer Foundationâ€ in Jianghan University, which has achieved good results and can be extended to other courses.
Optimizing community detection in social networks using antlion and K-median

Antlion Optimization (ALO) is one of the latest population based optimization methods that proved its good performance in a variety of applications. The ALO algorithm copies the hunting mechanism of antlions to ants in nature. Community detection in social networks is conclusive to understanding the concepts of the networks. Identifying network communities can be viewed as a problem of clustering a set of nodes into communities. k-median clustering is one of the popular techniques that has been applied in clustering. The problem of clustering network can be formalized as an optimization problem where a qualitatively objective function that captures the intuition of a cluster as a set of nodes with better in ternal connectivity than external connectivity is selected to be optimized. In this paper, a mixture antlion optimization and k-median for solving the community detection problem is proposed and named as K-median Modularity ALO. Experimental results which are applied on real life networks show the ability of the mixture antlion optimization and k-median to detect successfully an optimized community structure based on putting the modularity as an objective function.
Learning Coefficient of Vandermonde Matrix-Type Singularities in Model Selection

In recent years, selecting appropriate learning models has become more important with the increased need to analyze learning systems, and many model selection methods have been developed. The learning coefficient in Bayesian estimation, which serves to measure the learning efficiency in singular learning models, has an important role in several information criteria. The learning coefficient in regular models is known as the dimension of the parameter space over two, while that in singular models is smaller and varies in learning models. The learning coefficient is known mathematically as the log canonical threshold. In this paper, we provide a new rational blowing-up method for obtaining these coefficients. In the application to Vandermonde matrix-type singularities, we show the efficiency of such methods.
Don't Paint It Black: White-Box Explanations for Deep Learning in Computer Security.

Deep learning is increasingly used as a building block of security systems. Unfortunately, neural networks are hard to interpret and typically opaque to the practitioner. The machine learning community has started to address this problem by developing methods for explaining the predictions of neural networks. While several of these approaches have been successfully applied in the area of computer vision, their application in security has received little attention so far. It is an open question which explanation methods are appropriate for computer security and what requirements they need to satisfy. In this paper, we introduce criteria for comparing and evaluating explanation methods in the context of computer security. These cover general properties, such as the accuracy of explanations, as well as security-focused aspects, such as the completeness, efficiency, and robustness. Based on our criteria, we investigate six popular explanation methods and assess their utility in security systems for malware detection and vulnerability discovery. We observe significant differences between the methods and build on these to derive general recommendations for selecting and applying explanation methods in computer security.
The advantage of truncated permutations

Constructing a Pseudo Random Function (PRF) is a fundamental problem in cryptology. Such a construction, implemented by truncating the last m bits of permutations of {0,1}n was suggested by Hall et al. (1998). They conjectured that the distinguishing advantage of an adversary with q queries, Advn,m(q), is small if q=o(2(n+m)∕2), established an upper bound on Advn,m(q) that confirms the conjecture for m<n∕7, and also declared a general lower bound Advn,m(q)=Ω(q2∕2n+m). The conjecture was essentially confirmed by Bellare and Impagliazzo (1999). Nevertheless, the problem of estimating Advn,m(q) remained open. Combining the trivial bound 1, the birthday bound, and a result of Stam (1978) leads to the upper bound Advn,m(q)=Ominq(q−1)2n,q2n+m2,1.In this paper we show that this upper bound is tight for every 0≤m<n and any q. This, in turn, verifies that the converse to the conjecture of Hall et al. is also correct, i.e., that Advn,m(q) is negligible only for q=o(2(n+m)∕2).
PENGARUH FLUKTUASI MUKA AIR TERHADAP DEBIT ALIRAN PADA SUNGAI KETUPAK PADA SAAT MUSIM PENGHUJAN

The fluctuations in Ketupak river water affect the amount of inundation that occurs in G Dwijaya Village, Tugumulyo Subdistrict, Musi Rawas Regency.  The research aims to obtain a cross section of river channels to drain runoff water, so that runoff water due to maximum rainfall does not cause inundation.  the Ketupak river channel is unable to float the flow discharge that occurs during the rainy season, besides that there is also a lot of sediment, garbage, trunks and branches that interfere with ketupak river flow, so a re-dimension of the river channel needs to be done.  Based on the calculation of the Ketupak River discharge in the rainy season using the Chow formula method, flowrate (Q) is obtained at 5.22 m³ / sec.  From the results of calculations According to the 5-year rainfall data from 2012-2016, Q = 0.719 m³ / s, so when the rainy season the flow in the river becomes 5.94 m³ / sec (Q ≤ Q max), to meet Q max = equal to 5.94 m³ / sec, it is necessary to add a river cross-sectional area of 12.33 m² to 15.63 m² so that Q is obtained at 5.94 m³ / sec (Q ≥ Q max).
Denoising of Power Quality Disturbance Signal Based on Ant Colony optimization Wavelet Threshold Estimation

Based on the problem of the fast and effectively extraction for condition signals of electrical plants in digital substation, this paper presents an approach of ant colony optimization threshold estimation (ACOTE) for de-noising of partial discharge (PD) signals. A class of shrinkage functions with continuous derivatives based on the SURE estimation and ACO estimation are utilized for the threshold estimation. The ACO estimation is competent to obtain the global optimum thresholds and to raise the efficiency of adaptive searching computation. For verifying the de-noising results, two methods of standard soft wavelet threshold estimation (STE) and standard hard wavelet threshold estimation (HTE) are used for de-noising of two typical artificial stable signals, simulative PD signal and the field PD signal. The results show that the white noise can be removed effectively by the ACOTE, the distortion of which is smaller than the signals de-noised by the STE and HTE. Meanwhile, the ACOTE is a much less time-consuming scheme and exhibits a promising prospect in practical application.
A Lattice-Based Enhanced Privacy ID

The Enhanced Privacy ID (EPID) scheme is currently used for hardware enclave attestation by an increasingly large number of platforms that implement Intel Software Guard Extensions (SGX). However, the scheme currently deployed by Intel is supported on Elliptic Curve Cryptography (ECC), and will become insecure should a large quantum computer become available. As part of National Institute of Standards and Technology (NIST)'s effort for the standardisation of post-quantum cryptography, there has been a great boost in research on lattice-based cryptography. As this type of cryptography is more widely used, one expects that hardware platforms start integrating specific instructions that accelerate its execution. In this article, a new EPID scheme is proposed, supported on lattice primitives, that may benefit not only from future research developments in post-quantum cryptography, but also from instructions that may extend Intel's Instruction Set Architecture (ISA) in the future. This paper presents a new security model for EPID in the Universal Composability (UC) framework. The proposed Lattice-based EPID (LEPID) scheme is proved secure under the new model. Experimentally compared with a closely related Lattice-based Direct Anonymous Attestation (DAA) (LDAA) scheme from related art, it is shown that the private-key size is reduced 1.5 times, and that signature and verification times are sped up up to 1.4 and 1.1 times, respectively, for the considered parameters, when LEPID is compared with LDAA. Moreover, the signature size compares favourably to LDAA for small and medium-sized communities .
A Hybrid Q-learning Algorithm to Score a Moving Ball for Humanoid Robots

In this paper, we investigate an effective and robust algorithm as a hybrid Q-learning algorithm to instant kick of a moving ball to score the goal. In this regard, as an application, we lied proposed algorithm’s characteristics on the humanoid soccer robots. In this paper, we propose a hybridization of Heuristically Generating Possible Actions algorithm (HGPA) and Q-Learning algorithm. In each decision-making process first, the HGPA generates suitable possible actions, and then an adopted Q-Learning algorithm trains and selects the best action. The main objectives of HGPA are focused on the speeding up the learning process of the Q-learning algorithm, and it improves the quality of decision-making process. The simulation results show superior success rates of proposed algorithm in comparison to commonly used Q-learning algorithm in similar problems.