Reviewerly/exHarmony huggingface dataset:
Robust Partially Strong Tracking Extended Consider Kalman Filtering for INS/GNSS Integrated Navigation

Unknown biases or perturbations in the INS/GNSS integrated navigation system may produce unforeseeable negative effects when the navigation states are estimated by using the Kalman filtering and its variants. To mitigate these undesirable effects in the INS/GNSS integrated navigation, a novel partially strong tracking extended consider Kalman filtering (PSTECKF) is proposed. In the presented PSTECKF algorithm, the biases are not estimated, but their covariance and co-covariance are incorporated into the state estimation covariance by using a nonlinear “consider” approach. Based on the above, the PSTECKF also partially introduces an adaptive fading factor into the predicted covariance of the states, which excludes the co-covariance between the states and biases, to compensate the nonlinear approximation errors and navigation system covariance uncertainties. Simulation results demonstrate the performance of the proposed PSTECKF for INS/GNSS integrated navigation is superior to that of the EKF and ECKF when the biases or perturbations happen in a navigation system.
Visual Deprojection: Probabilistic Recovery of Collapsed Dimensions

We introduce visual deprojection: the task of recovering an image or video that has been collapsed along a dimension. Projections arise in various contexts, such as long-exposure photography, where a dynamic scene is collapsed in time to produce a motion-blurred image, and corner cameras, where reflected light from a scene is collapsed along a spatial dimension because of an edge occluder to yield a 1D video. Deprojection is ill-posed-- often there are many plausible solutions for a given input. We first propose a probabilistic model capturing the ambiguity of the task. We then present a variational inference strategy using convolutional neural networks as functional approximators. Sampling from the inference network at test time yields plausible candidates from the distribution of original signals that are consistent with a given input projection. We evaluate the method on several datasets for both spatial and temporal deprojection tasks. We first demonstrate the method can recover human gait videos and face images from spatial projections, and then show that it can recover videos of moving digits from dramatically motion-blurred images obtained via temporal projection.
Refined distributed emotion vector representation for social media sentiment analysis

As user-generated content increasingly proliferates through social networking sites, our lives are bombarded with ever more information, which has in turn has inspired the rapid evolution of new technologies and tools to process these vast amounts of data. Semantic and sentiment analysis of these social multimedia have become key research topics in many areas in society, e.g., in shopping malls to help policymakers predict market trends and discover potential customers. In this light, this study proposes a novel method to analyze the emotional aspects of Chinese vocabulary and then to assess the mass comments of the movie reviews. The experiment results show that our method 1. can improve the machine learning model by providing more refined emotional information to enhance the effectiveness of movie recommendation systems, and 2. performs significantly better than the other commonly used methods of emotional analysis.
Guiding Monte Carlo Tree Search by Scripts in Real-Time Strategy Games

In Real-Time Strategy (RTS) games, the action space grows combinatorially with respect to the number of units. With limited computing budget between actions, methods like Monte Carlo Tree Search (MCTS) tend to get lost in the massive search space. An interesting line of existing work is to incorporate human knowledge in the form of scripts. In this paper, we investigate different possibilities for incorporating scripts into the tree policy while still maintaining the convergence guarantees of MCTS. We also report experiments on incorporating the scripts into the playout policy, which showed that unbiased bots perform better than biased bots.
Color Visual Secret Sharing for QR Code with Perfect Module Reconstruction

Visual secret sharing is a secret sharing scheme where the decryption requires no computation. It has found many applications in online transaction security, privacy protection, and bar code security, etc. Recently, researches have indicated that combining visual secret sharing with the widely used Quick Response code may provide additional security mechanism to online transaction. However, current methods are either pixel-based, which requires high computational complexity or module-based, which sacrifices error correction capability of the original Quick Response code. Designing module-based visual secret sharing for the Quick Response code without sacrificing error correction capability is a challenging problem. To solve this problem, this paper proposes a (3, 3)-threshold visual secret sharing for Quick Response code scheme that fully explores the extra freedom provided by color visual secret sharing and color stacking. The binary secret Quick Response code is encoded into color shares. By stacking all the three shares, a binary color Quick Response code can be reconstructed. After the inherent pre-processing steps in a standard Quick Response code decoder, the original binary secret Quick Response code can be completely reconstructed. Thus, the original error correction capability of the Quick Response code is fully preserved. Theoretical analysis shows that the visual secret sharing for Quick Response code is secure under the condition that the computational device available to the attacker is limited to a decoder for standard Quick Response code. Experimental results verify that the secret Quick Response code cannot be reconstructed from just one share or any two shares. However, it can be 100% reconstructed once the three shares are stacked. The proposed visual secret sharing for Quick Response code is module-based, and it does not sacrifice the error correction capability. Furthermore, No extra pre-processing steps other than the standard Quick Response code decoder are required.
Enhancing Unsupervised Sentence Similarity Methods with Deep ContextualisedWord Representations

Calculating Semantic Textual Similarity (STS) plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction.All modern state of the art STS methods rely on word embeddings one way or another.The recently introduced contextualised word embeddings have proved more effective than standard word embeddings in many natural language processing tasks.This paper evaluates the impact of several contextualised word embeddings on unsupervised STS methods and compares it with the existing supervised/unsupervised STS methods for different datasets in different languages and different domains.
Anti-Forensics of Audio Source Identification Using Generative Adversarial Network

Digital audio recording is the main evidence used in the field of judicial forensics.Recently, a number of digital audio forensic techniques have been developed and the audio source identification (ASI) is one of the most active research topics.Most of existing ASI works mainly focus on improving the performance of detection accuracy and robustness.Little consideration has been given to ASI anti-forensics, which aims at attacking the forensic techniques.To expose the weaknesses of these source identification methods, we propose an anti-forensic framework based on generative adversarial network (GAN) to falsify the source information of an audio by adding specific disturbance.The experimental results show that the falsified audio can deceive the forensic methods effectively, and can even control their conclusions.Three state-of-art ASI methods have been evaluated as the attacking targets.For the confusing attack, the proposed method can significantly reduce their detection accuracies from about 97% to less than 5%.For the misleading attack, a misleading rate about 81.32% has been achieved while ensuring the perceptual quality of the anti-forensic audio.
Smartphone User Authentication Based on Holding Position and Touch-Typing Biometrics

In this advanced age, when smart phones are the norm, people utilize social networking, online shopping, and even private information storage through smart phones. As a result, identity authentication has become the most critical security activity in this period of the intelligent craze. By analyzing the shortcomings of the existing authentication methods, this paper proposes an identity authentication method based on the behavior of smartphone users. Firstly, the sensor data and touch-screen data of the smart phone users are collected through android programming. Secondly, the eigenvalues of this data are extracted and sent to the server. Thirdly, the Support Vector Machine (SVM) and Recurrent Neural Network (RNN) are introduced to train the collected data on the server end, and the results are finally yielded by the weighted average. The results show that the method this paper proposes has great FRR (False Reject Rate) and FAR (False Accept Rate).
LRM: A Location Recombination Mechanism for Achieving Trajectory $k$ -Anonymity Privacy Protection

Trajectory k-anonymity is a prevalent technique for protecting trajectory privacy. However, the existing techniques for generating fake trajectories can be easily broken by an adversary because of the failure to capture the probabilistic features and geographic features of the trajectories. They also reduce data availability. Thus, this paper proposes a location recombination mechanism (LRM) for achieving trajectory k-anonymity privacy protection. First, we propose a metric that measures the location pair similarity between location pairs. Based on this metric, we select sampling locations and divide locations into different equivalent probability classes. Locations in one equivalent probability class have the same probability as one corresponding base location. Then, we also introduce two metrics that measure the probabilistic similarity and geographic similarity between locations. Based on these metrics, we design algorithms to generate fake trajectories. These algorithms can recombine locations sampled from each equivalent probability class into trajectories. All of these trajectories meet the privacy protection requirements for both base trajectories and sampling trajectories. Finally, we evaluate our scheme thoroughly with real-world data. The results show that our method can protect the privacy of base trajectories and sampling trajectories and achieve a better performance of service provider utility and data availability than other schemes.
A Blockchain Based Framework Secured by ECDSA to Curb Drug Counterfeiting

Drug counterfeit has become a global issue. The counterfeit problem has become so huge that it has drawn a significant attention among researchers and policy makers. The fake drug industry is worth of 10 billion dollar per year as estimated by ‘ASSOCHAM’ in 2017. Also estimated by WHO about a million die per year due to fake drugs. The dispensation of fake drugs is the pivotal concern, specially in developing countries like India. One of main reason in drug counterfeiting is imperfect supply chain. There are many loop holes in our current policy of drug supply chain management system. In the present scenario of supply chain, either the information is not shared between the parties during the hand off process at all or a little or irrelevant information is shared. Once a party transfer the drug to another party, the first party doesn't know what happens to the drugs there after. The counterfeit drug not only affect the health condition of patient or the financial loss of genuine manufacturer but also it is posing serious threat to the socio-economic status of the society. In this paper, we explain details of the drug counterfeit along with a little history of the pharmacy industry and its impact on different areas and also to counter the counterfeits using Blockchain technology. Using Blockchain technology we explained here how to add traceability, visibility, and security to the drug supply chain. This proposed system will track the drugs from its origin from the manufacturer to the end the consumer.
Deciding Acceptance in Incomplete Argumentation Frameworks

Expressing incomplete knowledge in abstract argumentation frameworks (AFs) through incomplete AFs has recently received noticeable attention. However, algorithmic aspects of deciding acceptance in incomplete AFs are still under-developed. We address this current shortcoming by developing algorithms for NP-hard and coNP-hard variants of acceptance problems over incomplete AFs via harnessing Boolean satisfiability (SAT) solvers. Focusing on nonempty conflict-free or admissible sets and on stable extensions, we also provide new complexity results for a refined variant of skeptical acceptance in incomplete AFs, ranging from polynomial-time computability to hardness for the second level of the polynomial hierarchy. Furthermore, central to the proposed SAT-based counterexample-guided abstraction refinement approach for the second-level problem variants, we establish conditions for redundant atomic changes to incomplete AFs from the perspective of preserving extensions. We show empirically that the resulting SAT-based approach for incomplete AFs scales at least as well as existing SAT-based approaches to deciding acceptance in AFs.
Supersingular abelian surfaces and Eichler’s class number formula

Let F be a totally real field with ring of integers OF , and D be a totally definite quaternion algebra over F . A well-known formula established by Eichler and then extended by Korner computes the class number of any OF -order in D. In this paper we generalize the Eichler class number formula so that it works for arbitrary Z-orders in D. Our motivation is to count the isomorphism classes of supersingular abelian surfaces in a simple isogeny class over a finite prime field Fp. We give explicit formulas for the number of these isomorphism classes for all primes p.
Multiperspective Light Field Reconstruction Method via Transfer Reinforcement Learning

Compared with traditional imaging, the light field contains more comprehensive image information and higher image quality. However, the available data for light field reconstruction are limited, and the repeated calculation of data seriously affects the accuracy and the real-time performance of multiperspective light field reconstruction. To solve the problems, this paper proposes a multiperspective light field reconstruction method based on transfer reinforcement learning. Firstly, the similarity measurement model is established. According to the similarity threshold of the source domain and the target domain, the reinforcement learning model or the feature transfer learning model is autonomously selected. Secondly, the reinforcement learning model is established. The model uses multiagent (i.e., multiperspective) Q-learning to learn the feature set that is most similar to the target domain and the source domain and feeds it back to the source domain. This model increases the capacity of the source-domain samples and improves the accuracy of light field reconstruction. Finally, the feature transfer learning model is established. The model uses PCA to obtain the maximum embedding space of source-domain and target-domain features and maps similar features to a new space for label data migration. This model solves the problems of multiperspective data redundancy and repeated calculations and improves the real-time performance of maneuvering target recognition. Extensive experiments on PASCAL VOC datasets demonstrate the effectiveness of the proposed algorithm against the existing algorithms.
Exploratory Factor Analysis (EFA) for Adversity Quotient (AQ) Instrument among Youth

Adversity Quotient (AQ) Instrument was developed to determine the level of AQ among youth in Malaysia. The new items generated needs more empirical evidences on checking how well the items can measure the constructs of AQ. Literature search and pilot study were carried out and appropriate items were extracted. The exploratory factor analysis (EFA) was applied to verify the validity and reliability of the items. A survey with quantitative approach conducted to 552 youth using convenient sampling. The instrument with five point Likert scale consisted of 60 items with four main constructs of AQ namely Control, Ownership, Reach and Endurance. The Cronbachs alpha shows that the overall score was 0.949 and each constructs had between ranges of 0.755 to 0.880 indicating high item reliability. The total of 39 from 60 items shows the factor loadings are more than 0.6 with ranged from 0.603 - 0.874 for each constructs. The final outcome explained 58.99 percents of data variance. The result had proven that all constructs are relevant in measuring youth adversity. The implications towards the psychometric assessment was clear in providing empirical evidence for confirming the constructs by theory and also monitoring best items that fully explained the measurement.
Delay-based reservoir computing: tackling performance degradation due to system response time

We analyze the degradation of the computational capacity of delay-based reservoir computers due to system response time. We demonstrate that this degradation is reduced when the delay time is greater than the data injection time. Performance improvement is demonstrated on several benchmarking tasks.
Connection between BosonSampling with quantum and classical input states

BosonSampling is a problem of sampling events according to the transition probabilities of indistinguishable photons in a linear optical network. Computational hardness of BosonSampling depends on photon-number statistics of the input light. BosonSampling with multi-photon Fock states at the input is believed to be classically intractable but there exists an efficient classical algorithm for classical input states. In this paper, we present a mathematical connection between BosonSampling with quantum and classical light inputs. Specifically, we show that the generating function of a transition probability for Fock-state BosonSampling (FBS) can be expressed as a transition probability of thermal-light inputs. The closed-form expression of a thermal-light transition probability allows all possible transition probabilities of FBS to be obtained by calculating a single matrix permanent. Moreover, the transition probability of FBS is shown to be expressed as an integral involving a Gaussian function multiplied by a Laguerre polynomial, resulting in a fast oscillating integrand. Our work sheds new light on computational hardness of FBS by identifying the mathematical connection between BosonSampling with quantum and classical light.
A Probe into Spoken English Recognition in English Education Based on Computer-Aided Comprehensive Analysis

At present, computer-aided spoken English learning is becoming increasingly popular among learners. The computer-aided comprehensive analysis tech-nology can evaluate and correct learner's spoken pronunciation, thereby im-proving their pronunciation. Based on computer-aided comprehensive analy-sis, this paper aims to explore the automatic recognition and scoring methods of spoken English in English education. For this, it studies the effective matching of the feedback information with the known pronunciation scoring results, and then develops a computer evaluation plug-in consisting of dif-ferent modules such as user login, English spoken speech acquisition and recognition, voice evaluation, speech broadcast, and spoken dialogue. The research results show that the computer evaluation plug-in matches and compares the extracted feature parameters of input speech with the standard features, scores the spoken language input by the learner, and gives the cor-rect pronunciation so that the learner can get feedback in time. For different stages of English learning, the focus of recognition technology and the spo-ken recognition algorithms applied also vary. The research findings provide theoretical and technical support for oral English recognition, error correction and scoring.
An Approach for Streaming Data Feature Extraction Based on Discrete Cosine Transform and Particle Swarm Optimization

Incremental feature extraction algorithms are designed to analyze large-scale data streams. Many of them suffer from high computational cost, time complexity, and data dependency, which adversely affects the processing of the data stream. With this motivation, this paper presents a novel incremental feature extraction approach based on the Discrete Cosine Transform (DCT) for the data stream. The proposed approach is separated into initial and sequential phases, and each phase uses a fixed-size windowing technique for processing the current samples. The initial phase is performed only on the first window to construct the initial model as a baseline. In this phase, normalization and DCT are applied to each sample in the window. Subsequently, the efficient feature subset is determined by a particle swarm optimization-based method. With the construction of the initial model, the sequential phase begins. The normalization and DCT processes are likewise applied to each sample. Afterward, the feature subset is selected according to the initial model. Finally, the k-nearest neighbor classifier is employed for classification. The approach is tested on the well-known streaming data sets and compared with state-of-the-art incremental feature extraction algorithms. The experimental studies demonstrate the proposed approach’s success in terms of recognition accuracy and learning time.
Smart Forgetting for Safe Online Learning with Gaussian Processes

The identification of unknown dynamical systems using supervised learning enables model-based control of systems that cannot be modeled based on first principles. While most control literature focuses on the analysis of a static dataset, online learning control, where data points are added while the controller is running, has rarely been studied in depth. In this paper, we present a novel approach for online learning control based on Gaussian process models. To avoid computational difficulties with growing datasets, we propose a safe forgetting mechanism. Using an entropy criterion, data points are evaluated with respect to the future trajectory of the closed loop system and are ``forgotten'' if the stability of the system can further be guaranteed. The approach is evaluated in a simulation and in a robotic experiment to show its real-time capability.
Hiding data in audio files: A smoothing-based approach to improve the quality of the stego audio

In this digital era, transmitting data through a computer network has become common. Moreover, some applications have also been developed to do it. Nevertheless, users may not be aware of the security aspect of this data transmission, which can lead to disclosing this private message. In a case when a sensitive message is the object to transmit, a security mechanism should be applied. Data hiding is one of the methods introduced to work for this issue. In this algorithm, the message is embedded into the cover, such as an audio file, before being transmitted; on the other side, the recipient extracts it. However, the size of the message and the quality of the resulted stego data are still challenging. In this paper, we focus on these two problems by considering some factors: embedding space, embedding process, reducing, and smoothing steps. Firstly, the audio signal is discretized to obtain audio samples. Next, these samples are interpolated to provide spaces for hiding the secret. Considering that the quality of the generated stego audio is likely to drop, reducing and smoothing steps are designed. The experimental results show that this approach can improve the quality of the stego based on the specified payload capacity. That is, there is an increase in PSNR value of at least 10 dB, depending on the payload size and the methods.