Reviewerly/exHarmony huggingface dataset:
Geometric one-class classifiers using hyper-rectangles for knowledge extraction

One-class classification (OCC) is to properly classify unknown data by developing a classifier, which can learn about a given training dataset with only one class. Its importance has been increasing in situations such that (i) there exists little or no information pertaining to other classes, or (ii) there are many unknown and heterogeneous classes. However, many OCC algorithms developed so far have some limitations as (i) they are black-box algorithms so that the user cannot clearly understand the internal classification mechanism of generated models, and (ii) there are no dominant criteria for node branching in decision-tree-based algorithms. Based on these motivations, in this work, we propose two efficient one-class classifiers using hyper-rectangles (h-rtgl) to describe a dataset with only one class: one-class hyper-rectangle descriptor by merging and one-class hyper-rectangle descriptor by partitioning, depending on the methods generating intervals and h-rtgls. The suggested classifiers can control the number and volume of the generated h-rtgls, which can affect the classification accuracy and deal with overfitting issue. We show the superiority of the proposed classifiers by a numerical experiment using UCI machine learning repository.
Real-time visual tracking with ELM augmented adaptive correlation filter

Accuracy, robustness and efficiency are three general requirements for the online visual tracking. Although the correlation based trackers achieved competitive results on both accuracy and efficiency, they still suffer from time-consuming scale searching and are prone to drifting. In this paper, an Extreme Learning Machine (ELM) augmented adaptive correlation filter is proposed to address these issues. We enhance the correlation based tracker in four aspects. Firstly, a keypoints tracking based scale estimation method is proposed to handle the target scale variation during tracking. Secondly, the powerful and efficient features including intensity, Fast Histogram of Oriented Gradient (FHOG) and Discriminative color Descriptors (DD) are integrated to further boost the tracking performance. Thirdly, dynamic updating mechanism is introduced to better adapt the correlation filter to the appearance changes. Moreover, to handle the drift brought by severe occlusion, we train an online ELM based classifier to re-initialize object in case of tracking failure. Extensive experimental results on the Object Tracking Benchmark (OTB) dataset show that our proposed tracker performs favorably against state-of-the-art methods in terms of accuracy, efficiency, and robustness.
A time-aware searchable encryption scheme for EHRs

Despite the benefits of EHRs (Electronic Health Records), there is a growing concern over the risks of privacy exposure associated with the technologies of EHR storing and transmission. To deal with this problem, a time-aware searchable encryption with designated server is proposed in this paper. It is based on Boneh's public key encryption with keyword search and Rivest's timed-release cryptology. Our construction has three features: the user cannot issue a keyword search query successfully unless the search falls into the specific time range; only the authorized user can generate a valid trapdoor; only the designated server can execute the search. Applying our scheme in a multi-user environment, the number of the keyword ciphertexts would not increase linearly with the number of the authorized users. The security and performance analysis shows that our proposed scheme is securer and more efficient than the existing similar schemes.
Extrapolation of Experimental Results through Analogical Reasoning from Latent Classes

In the human sciences, experimental research is used to establish causal relationships. However, the extrapolation of these results to the target population can be problematic. To facilitate extrapolation, we propose to use the statistical technique Latent Class Regression Analysis in combination with the analogical reasoning theory for extrapolation. This statistical technique can identify latent classes that differ in the effect of X on Y. In order to extrapolate by means of analogical reasoning, one can characterize the latent classes by a combination of features and then compare these features to features of the target.
A projection-based split-and-merge clustering algorithm

A novel split-and-merge clustering algorithm is proposed by using projection technology and K-means method. There are two key technologies in the proposed method: shape recognition based on projection and split-and-merge process based on K-means. By projecting the data onto the connection of any two cluster centers, no matter how large the dimension of data is, we can always obtain an one-dimension density curve of the projection to guarantee an acceptable amount of calculation. Further embedded the kernel density estimate, we can determine the distribution of clusters by the shape of the one-dimensional density curve. In the split-and-merge process, this algorithm not only addresses the sensitivity in selecting initial cluster centers, but also automatically give a reasonable number of clusters. We also discuss the possibility to extend the projection split-and-merge method from K-means to density based methods (as EM algorithm and Cross-entropy clustering). Both simulation and real data experimental results show that our method performance well especially under strict data conditions.
Adversarial training and decoding strategies for end-to-end neural conversation models

This paper presents adversarial training and decoding methods for neural conversation models that can generate natural responses given dialog contexts. In our prior work, we built several end-to-end conversation systems for the 6th Dialog System Technology Challenges (DSTC6) Twitter help-desk dialog task. These systems included novel extensions of sequence adversarial training, example-based response extraction, and Minimum Bayes-Risk based system combination. In DSTC6, our systems achieved the best performance in most objective measures such as BLEU and METEOR scores and decent performance in a subjective measure based on human rating. In this paper, we provide a complete set of our experiments for DSTC6 and further extend the training and decoding strategies more focusing on improving the subjective measure, where we combine responses of three adversarial models. Experimental results demonstrate that the extended methods improve the human rating score and outperform the best score in DSTC6.
Improved decryption quality with a random reference beam cryptosystem

The secure managing of sensible data is one of the main challenges nowadays. During the last years several advances have demonstrated the ability of optical encrypting systems to protect information. In spite of these developments, there are some evident issues to be solved. One of the main concerns is the limitation of the experimental cryptosystems in the size of the inputs that can be properly protected and recovered by the encrypting system. In the case of the experimental joint transform correlator (JTC) cryptosystem, the data to be encoded and the key are placed side by side in the input plane. Therefore, the size of the object to be encrypted is limited by the size of the encryption key and the separation between the input and the key. This limitation leads to degradation on the recovered data according the size and the spatial frequencies content of the object for a given recording media. In this work we experimentally implement an interferometric cryptosystem in which the encryption key is a ground glass diffuser (GGD) located in the reference arm. In the object arm, the information to be encoded is displayed in a spatial light modulator (SLM) and placed in contact with another GGD. This system allows the projection of the input object in the whole SLM area. The encryption process results from the interference between the reference beam and the Fourier transform of the input object plane. We experimentally analyze the quality of the decrypted object in relation with the size of the input original object. Then, we compare the performance of this system with the JTC cryptosystem. Finally, we implement a protocol for a multi-user environment under the rotation of the key. Experimental results show the viability, versatility and applicability of the proposal.
Encrypted image transmission in OFDM-based VLC systems using symbol scrambling and chaotic DFT precoding

In this paper, a joint symbol-scrambling and chaotic discrete Fourier transform (DFT) precoding scheme is proposed for orthogonal frequency-division multiplexing (OFDM) visible light communication (VLC) systems for encrypted image data. In the proposed scheme, the original image data is first divided into sub-blocks and then an inter-block scrambling operation is applied to the resulting image data. Secondly, the real and imaginary parts of the transmitted QAM symbols are encrypted using two separate chaotic sequences generated from different chaotic maps. Thirdly, the resulting encrypted QAM signals are transformed into new signals using a chaotic DFT-precoding matrix in order to further improve the security and bit error rate (BER) performance of the OFDM signals. The mathematical model of our proposed encrypted OFDM-based VLC system is established and the BER formula is derived. The obtained BER formula is confirmed by our simulation results. Our simulation results also show that both symbol scrambling and image-block scrambling have no effect on the BER of the proposed DFT-precoded OFDM-based VLC system. The proposed scheme not only improves the quality of the transmitted image but also protects the transmitted image data from eavesdroppers.
Affine invariant fusion feature extraction based on geometry descriptor and BIT for object recognition

It is difficult to recognise an image with affine transformation due to viewing angle anddistance variations. Therefore, affine invariant feature extraction is avaluable technology in the field of image recognition. Inspired by bio-visualmechanism, an affine invariant for object recognition method based on a fusionfeature framework is proposed in this study, which employs geometry descriptorand double biologically inspired transformation (DBIT). First, a shape featureof interest detector is adopted to detect contour features. Then, the areaestimation of affine region detector is utilised to construct area ratio featurevectors. Second, an orientation edge detector is built to highlight the edges ofdifferent directions. On this basis, local space frequency detector is adoptedto measure the spatial frequency at each direction and interval, which convertsthe output map into DBIT feature vectors. A weighted fusion strategy isperformed based on Pearson correlation distance to fuse the geometry feature andDBIT feature. Some tests for Alphanumeric, Coil-100 MPEG-7, Mixed NationalInstitute of Standards and Technology (MNIST) and Olivetti Research Laboratoryface images database (ORL) database remain highly stable recognition accuracy,even when the shear factor is between −0.5 and + 0.5. The experiment resultsshow the authors’ proposed approach has a nice performance in featureinvariance, selectivity and recognition accuracy.
Asymmetric Distortion Function for JPEG Steganography Using Block Artifact Compensation

This article describes how the existing distortion functions for JPEG steganography allot same cost for ±1 embedding changes. Because of the correlation of natural image, however, changes with different polarities make different influences on image. Therefore, the embedding costs for ±1 embedding changes should not be equivalent. This article proposes a general method to distinguish the embedding costs for different polarities of embedding changes for JPEG images with the help of reference images constructed by block artifact compensation. The original JPEG image is decompressed into spatial domain firstly, and then the block artifact is compensated by smoothing filtering implemented on border pixels of each 8 × 8 block. After that, the compensated image which is more similar to the original uncompressed image is recompressed into DCT domain and adopted as side information to guide the adjusting of the given distortion function. Experiment results show that after the proposed method is employed, the security performance of current popular JPEG steganographic methods is observably increased.
On relations between CCZ- and EA-equivalences

In the present paper we introduce some sufficient conditions and a procedure for checking whether, for a given function, CCZ-equivalence is more general than EA-equivalence together with taking inverses of permutations. It is known from Budaghyan et al. (IEEE Trans. Inf. Theory 52.3, 1141–1152 2006; Finite Fields Appl. 15(2), 150–159 2009) that for quadratic APN functions (both monomial and polynomial cases) CCZ-equivalence is more general. We prove hereby that for non-quadratic APN functions CCZ-equivalence can be more general (by studying the only known APN function which is CCZ-inequivalent to both power functions and quadratics). On the contrary, we prove that for power non-Gold APN functions, CCZ equivalence coincides with EA-equivalence and inverse transformation for n ≤ 8. We conjecture that this is true for any n.
Investigating subclasses of abstract dialectical frameworks

Dialectical frameworks (ADFs) are generalizations of Dung argumentation frameworks where arbitrary relationships among arguments can be formalized. This additional expressibility comes with the price of higher computational complexity, thus an understanding of potentially easier subclasses is essential. Compared to Dung argumentation frameworks, where several subclasses such as acyclic and symmetric frameworks are well understood, there has been no in-depth analysis for ADFs in such direction yet (with the notable exception of bipolar ADFs). In this work, we introduce certain subclasses of ADFs and investigate their properties. In particular, we show that for acyclic ADFs, the different semantics coincide. On the other hand, we show that the concept of symmetry is less powerful for ADFs and further restrictions are required to achieve results that are similar to the known ones for Dung's frameworks. A particular such subclass (support-free symmetric ADFs) turns out to be closely related to argumentation frameworks with collective attacks (SETAFs); we investigate this relation in detail and obtain as a by-product that even for SETAFs symmetry is less powerful than for AFs. We also discuss the role of odd-length cycles in the subclasses we have introduced. Finally, we analyse the expressiveness of the ADF subclasses we introduce in terms of signatures.
A lightweight secure conjunctive keyword search scheme in hybrid cloud

Keyword search over encrypted sensitive data is an essential technique in cloud computing. Many traditional secure retrieval schemes of the encrypted data mainly apply to single file-owner and single retrieval-party scenario, which cannot be used in cloud environment effectively. The emergence of Attribute-Based Encryption (ABE) provides a new solution to deliver secure and multi-parties keyword search over the encrypted data. However, most existing attribute-based keyword search schemes suffer problems, such as, imposed heavy computational burden for file-owner, limited expressive capability of access control policy, supported for single keyword search only, etc. Hence, we present a Lightweight secure Conjunctive Keyword Search scheme in hybrid cloud environment (LCKS) based on ciphertext-policy ABE algorithm, which supports file-owner authorized conjunctive keyword search for multi-parties. By delegating most computational tasks to private cloud servers, LCKS enables file-owners to generate keyword index efficiently while delivering a fine-grained access control policy through adopting an expressive tree structure. Both security analysis and performance evaluation show that LCKS is secure, highly efficient, and well suited for hybrid cloud.
Two User Adaptation-Derived Features for Biometrical Classifications of User Identity in 3D-Sensor-Based Body Gesture Recognition Applications

For human gesture recognition applications, 3D-sensor-based approaches have received considerable attention and are crucial for future applications of an advanced body sensor network (BSN). A practical gesture recognition system is to apply active gesture patterns of a gesture-making user for body action classifications. 3D-sensor-based gesture recognition, categorized as biometric recognition in BSN applications, is greatly lack of the extensible cognition ability due to substandard recognition accuracy on the gesture-making user identity. To overcome this problem, this paper proposes an active gesture-based user identity recognition approach using a robust feature design, called user adaptation (UA) features, derived from a UA process. Two different UA features, namely Eigen Centroid-UA and Eigen Transform-UA features, were developed in this paper to accurately represent the adaptive learning tendency of gesture recognition for a specific gesture-making user. Compared with traditional 3D sensor gesture-based identity recognition approaches that employ only the feature of fixed body skeleton information without any UA designs, the presented UA-feature can exhibit fine adaptation learning continuously to the specific action user; therefore, superior identity recognition accuracy will be constantly ensured. To demonstrate the efficiency and effectiveness of the developed robust UA features in this paper, experiments on gesture-making user identification by Gaussian mixture model applying the proposed Eigen Centroid-UA feature and verification by support vector machine applying the proposed Eigen Transform-UA feature were conducted.
Comparative assessment of simplified indexes for the spectral characterisation of photovoltaic systems

The electrical output of a photovoltaic system is often related to the plane-of-array global irradiance and the cell operating temperature. However, the spectral distribution of the irradiance also influences the system electrical behaviour, through its coupling with the spectral response of the photovoltaic material employed. Many authors have evaluated the spectral performance of different types of semiconductors under real operating conditions worldwide by using different methods and spectral indexes. However, there are not comparative studies on the accuracy of these methods in the literature. In this paper, two simplified existing indexes and a novel introduced index are compared to the spectral factor method in order to assess their relative accuracy for the climate of Granada, Southern Spain, and considering the most widespread used photovoltaic materials. Results show the election of a suitable index depends on the type of material, availability of spectral response information of the material, computing capabilities and required accuracy. The maximum annual relative error with respect to the spectral factor considering every index and material is 1.77%, while the maximum monthly relative error can reach 4.22%.
Guiding intelligent surveillance system by learning-by-synthesis gaze estimation

We describe a novel learning-by-synthesis method for estimating the gaze direction of an automated intelligent surveillance system. Recently, the progress of integrated learning has proposed a training model for synthetic images, which can effectively reduce the cost of human and material resources. However, due to the different distributions between the real and synthetic images, the desired performance cannot be achieved from the synthetic image learning as compared to the real images. In order to solve this problem, the previous method was to improve the authenticity of the composite image by learning the model. However, this kind of method had the disadvantage that the distortion was not improved and the level of authenticity was unstable. In order to solve this problem, we propose a new structure to improve the composite image. By referring to the idea of style transformation, we can effectively reduce the distortion of the image and minimize the need for actual data annotation. We estimate that this can produce highly realistic images, which we have demonstrated through qualitative and user research. We quantitatively evaluate the generated images by training the gaze estimation model. We use the refined synthetic dataset to show significant improvements compared with using the raw synthetic dataset.
Region-of-Interest Detection Based on Statistical Distinctiveness for Panchromatic Remote Sensing Images

Region-of-interest (ROI) detection plays a significant role in the analysis and interpretation of remote sensing images (RSI), due to the huge size of satellite images and their explosive growth in quantity. However, when applied to panchromatic RSI directly, traditional saliency models cannot achieve satisfying performance for two reasons: one is the computational efficiency decrease caused by the huge image size; the other is the absence of color information for panchromatic RSI. Thus, in this letter, an ROI detection model based on statistical distinctiveness (SD) is proposed for saliency analysis and ROIs detection in panchromatic RSI. The proposed SD model incorporates both the lower order SD (LSD) and the higher order SD (HSD), in order to identify regions of interest that are highly distinctive from the rest of the scene. Finally, the saliency map is determined by fusing cue maps obtained by calculating LSD locally and HSD globally. Experimental results show that our approach achieves promising results when compared with existing state-of-the-art saliency detection models.
Semi-automated development of conceptual models from natural language text

The process of converting natural language specifications into conceptual models requires detailed analysis of natural language text, and designers frequently make mistakes when undertaking this transformation manually. Although many approaches have been used to partly automate this process, one of the main limitations is the lack of a domain-independent ontology that can be used as a repository for entities and relationships, thus guiding the transformation process. In this paper, a semi-automated system for mapping natural language text into conceptual models is proposed. The system, called SACMES, combines a linguistic approach with an ontological approach and human intervention to achieve the task. SACMES learns from the natural language specifications that it processes and stores the information that is learnt in a conceptual model ontology and a user history knowledge database. It then uses the stored information to improve performance and reduce the need for human intervention. The evaluation conducted on SACMES demonstrates that: (1) by using the system, precision and recall for users identifying entities of conceptual models is increased by 6% and 13%, respectively, while for relationships, increases are even higher, 14% for precision and 23% for recall; (2) the performance of the system is improved by processing more natural language requirements, and thus, the need for human intervention is decreased.