Reviewerly/exHarmony huggingface dataset:
3D palatal rugae recognition based on Fractional Fourier Transform

Following a major disaster, most of the biological characteristics of the victims are typically destroyed, which increases the difficulty of forensic identification. However, palatal rugae cannot be easily destroyed owing to its unique location characteristics, owing to which it has become a new type of biological feature used in forensic identification under special environments. This study proposes a new method of 3D palatal rugae recognition which uses fractional Fourier transform (FRFT). The isometric slicing operation is first used to simplify the complexity of the algorithm and improve the efficiency of the 3D data storage and expression. FRFT is the used to extract the fractional domain features of the profile images obtained by slicing the 3D palatal rugae, and the support vector machine recursive feature elimination (SVM-RFE) method is used for feature selection in the fractional domain. The use of FRFT increases the number of the palatal rugae features extracted from the time-frequency domain, and can improve the effectiveness of feature extraction. The recognition accuracy rate of the proposed 3D palatal rugae recognition system can reach up to 97.8%. The proposed method meets the requirements of 3D palatal rugae recognition, and can help in performing rapid forensic identification. The method proposed in this study is useful in reducing the dependence on human experts and promoting the digitization of a forensic identification system based on palatal rugae.
Automated imbalanced classification via layered learning

In this paper we address imbalanced binary classification (IBC) tasks. Applying resampling strategies to balance the class distribution of training instances is a common approach to tackle these problems. Many state-of-the-art methods find instances of interest close to the decision boundary to drive the resampling process. However, under-sampling the majority class may potentially lead to important information loss. Over-sampling also may increase the chance of overfitting by propagating the information contained in instances from the minority class. The main contribution of our work is a new method called ICLL for tackling IBC tasks which is not based on resampling training observations. Instead, ICLL follows a layered learning paradigm to model the data in two stages. In the first layer, ICLL learns to distinguish cases close to the decision boundary from cases which are clearly from the majority class, where this dichotomy is defined using a hierarchical clustering analysis. In the subsequent layer, we use instances close to the decision boundary and instances from the minority class to solve the original predictive task. A second contribution of our work is the automatic definition of the layers which comprise the layered learning strategy using a hierarchical clustering model. This is a relevant discovery as this process is usually performed manually according to domain knowledge. We carried out extensive experiments using 100 benchmark data sets. The results show that the proposed method leads to a better performance relatively to several state-of-the-art methods for IBC.
Multichannel Matrix Randomized Autoencoder

The existing randomized autoencoders are generally designed for vectorization data resulting in destroying the original structure information inevitably when dealing with multi-dimension data such as image and video. To address this issue, a one-side matrix randomized AE (OMRAE) is developed that takes the two-dimensional (2D) data as inputs directly by the linear mapping on one-side of inputs with matrix multiplication. For multichannel 2D (M2D) data, a multichannel OMRAE (OMMRAE) is proposed by training the output weights to rebuild each channel of inputs respectively. In this way, the structural information of each channel and the interaction between channels are explored. Then, a double-side structure using 2 OMMRAEs to simultaneously extracts the row and column structure information of M2D is developed. At last, a novel hierarchical matrix randomized neural networks is constructed for one-class classification where each layer passes information by bilinear mapping derived from DMMRAE. Experiments are conducted on 2 benchmark datasets for the effectiveness demonstration. Comparisons to several state-of-the-art AEs reveal that the proposed OMMRAE/DMMRAE can obtain better performance with a compact network size. The source code would be available at https://github.com/ML-HDU/MMRAE .
Hierarchically structured task-agnostic continual learning

Abstract One notable weakness of current machine learning algorithms is the poor ability of models to solve new problems without forgetting previously acquired knowledge. The Continual Learning paradigm has emerged as a protocol to systematically investigate settings where the model sequentially observes samples generated by a series of tasks. In this work, we take a task-agnostic view of continual learning and develop a hierarchical information-theoretic optimality principle that facilitates a trade-off between learning and forgetting. We derive this principle from a Bayesian perspective and show its connections to previous approaches to continual learning. Based on this principle, we propose a neural network layer, called the Mixture-of-Variational-Experts layer, that alleviates forgetting by creating a set of information processing paths through the network which is governed by a gating policy. Equipped with a diverse and specialized set of parameters, each path can be regarded as a distinct sub-network that learns to solve tasks. To improve expert allocation, we introduce diversity objectives, which we evaluate in additional ablation studies. Importantly, our approach can operate in a task-agnostic way, i.e., it does not require task-specific knowledge, as is the case with many existing continual learning algorithms. Due to the general formulation based on generic utility functions, we can apply this optimality principle to a large variety of learning problems, including supervised learning, reinforcement learning, and generative modeling. We demonstrate the competitive performance of our method on continual reinforcement learning and variants of the MNIST, CIFAR-10, and CIFAR-100 datasets.
Modulation Format Identification Technology Based on a Searching Cluster Boundary Clustering Algorithm

In this study, a modulation format identification (MFI) scheme based on a searching cluster boundary (SCB) algorithm is designed for space optical communication. Particularly, we design a clustering algorithm based on local low-density samples as the boundaries of clusters and use the boundary to distinguish different clusters; this is unaffected by the distribution of samples in the clusters. The SCB algorithm was applied to MFI, and a verification experiment for space optical communication was performed. The experimental results show that the SCB algorithm achieves good results with clusters of different shapes and the designed MFI scheme can accurately identify modulation formats of different orders with few symbols.
Deep contextual disambiguation of homonyms and polysemants

Abstract A new metric method for word sense disambiguation is put forward, being formulated within a phenomenological analogy to the wave function of an observable quantity in quantum mechanics where the actual meaning of a multivalued word (a homonym or a polysemant) is determined by its context. The choice of the actualized sense is determined by the minimal semantic distance between the semantics of possible word senses and that of the context, where the meanings of the word in question and the context follow from their semantic fields based on lexicographic hyperchains. In contrast to the common models, our method accounts for semantic polarity. The formulated method showed good results in disentangling polysemy, which was not possible to achieve within the contextualized embedding approacdh based on bidirectional encoder representations from transformers (BERT).
Image segmentation using K-means clustering, Gabor filter and moving mesh method

Some medical images such as blood vessels or nerve canal are very narrow, it is hardly segmented. In this paper, we propose a two-dimensional image segmentation algorithm combining K-means clustering, Gabor filter and moving mesh method. The mesh is moved adaptively concentrated in steep region and the location feature is added to our clustering algorithm. Moreover, the image feature values are obtained by the convolution operation between the image and Gabor filter. The advantage of this approach is that the segmentation result of the K-means clustering algorithm can be improved by constantly adding new features such as location, pixel or Gabor filter. The experiments indicate that our algorithms can quickly segment medical and crack images without compromising the quality of the results. The results demonstrated that the proposed method achieved precision 94.31%, recall 97.84%, F1 score 95.96%.
Digital image correlation for accurate strain measurement on sharp notched specimens

Notch stress concentration and the notch effects in general, play a significant role in the field of structural mechanics. Also, the strain measurement in notches is still a challenge. In cases, when the notch geometry is not exactly defined e.g., weld joints etc., the measurement of strains in notches becomes even more important. Therefore, the present work focuses on the problem of strain measurement on notched specimens, especially on sharp notches (notches with small minimum radius) by using the digital image correlation method. Initial measurements were carried out on specimens with known stress concentration and the results were compared with the analytical and numerical solution. Multiple camera positions with respect to the specimen are compared and discussed. Minimum number of pixels per notch radius for accurate measurement was determined based on the measured data. This limitation is influenced by several effects. The presented work focuses deeper on the influence of postprocessing filter methods. Several possible solutions to overcome this limitation are presented. The obtained results and the knowledge gained by previous measurements were utilized by direct practical measurements, weld notch stress concentration measurement.
On the localisability of gaps in adhesive joints loaded in mode I

During the manufacturing process of adhesive bonds different faults can occur, leading to a loss of structural integrity of the entire structure. This requires a thorough study of the connection behaviour in the presence of faults and localisation of such faults. For this purpose, double cantilever beam specimens were manufactured with and without predefined gaps. A soft, rubber-like adhesive system (Sikaflex®-521 UV) was investigated. The experimental investigations include several measurement methods to identify the deflection of the adherend beams and to calculate the J-Integral in situ during testing. Optical measurement techniques such as digital image correlation and shearography are compared to backface strain measurement by optical fibres. In addition to quantifying of the effects of certain defects on Mode I fracture behaviour, the applicability of the selected test methods and their respective limitations are discussed. All measurement methods are suitable for detecting gaps with different advantages and disadvantages. For example, it is possible to evaluate the J-integral from the backface strain and digital image correlation measurement with the deflection curve approach.
Implementing a Build Manager in Ada

A build manager is a tool used to automate the building, testing and delivery of software projets. Porion is a new build manager that was first presented at the FOSDEM 2022 event in the "Continuous Integration and Continuous Deployment Developer Room" [1] and in the "Ada Developer Room" [2]. This paper is a summary of these two presentations and it focuses on the complexity of designing a build manager. It highlights some security issues that apply to a build manager and its implementation. It explains the overall architecture that was chosen and the reason of the choice. Finally it presents the Ada generation tools that have been used in this project.
A Temporal Chrominance Trigger for Clean-Label Backdoor Attack Against Anti-Spoof Rebroadcast Detection

We propose a stealthy clean-label video backdoor attack against Deep Learning (DL)-based models aiming at detecting a particular class of spoofing attacks, namely video rebroadcast attacks. The injected backdoor does not affect spoofing detection in normal conditions, but induces a misclassification in the presence of a specific triggering signal. The proposed backdoor relies on a temporal trigger altering the average chrominance of the video sequence. The backdoor signal is designed by taking into account the peculiarities of the Human Visual System (HVS) to reduce the visibility of the trigger, thus increasing the stealthiness of the backdoor. To force the network to look at the presence of the trigger in the challenging clean-label scenario, we choose the poisoned samples used for the injection of the backdoor following a so-called Outlier Poisoning Strategy (OPS). According to OPS, the triggering signal is inserted in the training samples that the network finds more difficult to classify. The effectiveness of the proposed backdoor attack and its generality are validated experimentally on different datasets and anti-spoofing rebroadcast detection architectures.
Virtual Reality for Learning

The consumer age of the Personal Computer and mobile devices has opened up a new world of opportunities for innovative teaching methodologies, many based on serious games and virtual worlds. Similar levels of market penetration are expected for the use of Immersive Virtual Reality (iVR) over upcoming decades, once all the core technologies for game engines and head-mounted displays are available on the market at affordable prices. In this chapter, a general overview of the state of the art of iVR learning experiences is presented. Firstly, the advantages of iVR over traditional learning are described - advantages that must be considered when defining iVR experiences for the optimization of student learning and satisfaction. Secondly, the relationship between learning theories and iVR experiences is briefly summarized; an area where constructivist theories appear to be the most commonly used theory in iVR experiences. Thirdly, some examples of the success of iVR applications at different learning levels, from primary school to higher education, are summarized. Fourthly, the key factors for the successful design and use of an iVR experience in education are identified, from the predesign stage to the final evaluation - with special attention given to the different possibilities of each type of HMD for different kinds of educational experiences. Finally, the main limitations of iVR for learning today and the future trends of this technology for teaching are also identified and discussed.
Hyperparameter-Free Localized Simple Multiple Kernel K-Means With Global Optimum

The newly proposed localized simple multiple kernel k-means (SimpleMKKM) provides an elegant clustering framework which sufficiently considers the potential variation among samples. Although achieving superior clustering performance in some applications, we observe that it is required to pre-specify an extra hyperparameter, which determines the size of the localization. This greatly limits its availability in practical applications since there is a little guideline to set a suitable hyperparameter in clustering tasks. To overcome this issue, we firstly parameterize a neighborhood mask matrix as a quadratic combination of a set of pre-computed base neighborhood mask matrices, which corresponds to a group of hyperparameters. We then propose to jointly learn the optimal coefficient of these neighborhood mask matrices together with the clustering tasks. By this way, we obtain the proposed hyperparameter-free localized SimpleMKKM, which corresponds to a more intractable minimization-minimization-maximization optimization problem. We rewrite the resultant optimization as a minimization of an optimal value function, prove its differentiability, and develop a gradient based algorithm to solve it. Furthermore, we theoretically prove that the obtained optimum is the global one. Comprehensive experimental study on several benchmark datasets verifies its effectiveness, comparing with several state-of-the-art counterparts in the recent literature. The source code for hyperparameter-free localized SimpleMKKM is available at https://github.com/xinwangliu/SimpleMKKMcodes/.
RES-StS: Referring Expression Speaker via Self-Training With Scorer for Goal-Oriented Vision-Language Navigation

It is a rather practical but difficult task to find a specified target object via autonomous exploration based on natural language descriptions in an unstructured environment. Since the human-annotated data is expensive to gather for the goal-oriented vision-language navigation (GVLN) task, the size of the standard dataset is inadequate, which has significantly limited the accuracy of previous techniques. In this work, we aim to improve the robustness and generalization of the navigator by dynamically providing high-quality pseudo-instructions using a proposed RES-StS paradigm. Specifically, we establish a referring expression speaker (RES) to predict descriptive instructions for the given path to the goal object. Based on an environment-and-object fusion (EOF) module, RES derives spatial representations from the input trajectories, which are subsequently encoded by a number of transformer layers. Additionally, given that the quality of the pseudo labels is important for data augmentation while the limited dataset may also hinder RES learning, we propose to equip RES with a more effective generation ability by using the self-training approach. A trajectory-instruction matching scorer (TIMS) network based on contrastive learning is proposed to selectively use rehearsal of prior knowledge. Finally, all network modules in the system are integrated by suggesting a multi-stage training strategy, allowing them to assist one another and thus enhance performance on the GVLN task. Experimental results demonstrate the effectiveness of our approach. Compared with the SOTA methods, our method improves SR, SPL, and RGS by 4.72%, 2.55%, and 3.45% respectively, on the REVERIE dataset, and 4.58%, 3.75% and 3.14% respectively, on the SOON dataset.
Face Forgery Detection by 3D Decomposition and Composition Search

Detecting digital face manipulation has attracted extensive attention due to fake media's potential risks to the public. However, recent advances have been able to reduce the forgery signals to a low magnitude. Decomposition, which reversibly decomposes an image into several constituent elements, is a promising way to highlight the hidden forgery details. In this paper, we investigate a novel 3D decomposition based method that considers a face image as the production of the interaction between 3D geometry and lighting environment. Specifically, we disentangle a face image into four graphics components including 3D shape, lighting, common texture, and identity texture, which are respectively constrained by 3D morphable model, harmonic reflectance illumination, and PCA texture model. Meanwhile, we build a fine-grained morphing network to predict 3D shapes with pixel-level accuracy to reduce the noise in the decomposed elements. Moreover, we propose a composition search strategy that enables an automatic construction of an architecture to mine forgery clues from forgery-relevant components. Extensive experiments validate that the decomposed components highlight forgery artifacts, and the searched architecture extracts discriminative forgery features. Thus, our method achieves the state-of-the-art performance.
PharmaLedger a Blockchain-enabled Healthcare Platform

This article presents human-centric healthcare solutions that could revolutionize the medical sector by creating trust with the help of blockchain technologies. This article summarizes the experience of creating a blockchain platform and a Digital Trust Ecosystem in a private-public partnership between research entities and ten big pharma companies. This partnership builds on a previous research project that pioneered the OpenDSU blockchain technology. This article summarizes the research results obtained in blockchain technologies under the influence of the pharma industry culture and regulations.
Vagueness in Artificial Intelligence: The ‘Fuzzy Logic’ of AI-Related Patent Claims

Abstract Artificial intelligence is an emerging technology with an average growth rate of 64.70% in patent filings worldwide and 39.71% in Europe between 2015 and 2019, according to Espacenet queries. This trend has raised several concerns regarding the implications for Intellectual Property rights, including disclosure, which is one of the main components justifying the existence of the patent system. Despite its importance, this requirement has been disputed due to certain tendencies that distort the functions that the act of disclosure fulfils, such as promoting innovation, research or teaching. With artificial intelligence–related patents being granted, the disclosure requirement is once again compromised, and perhaps even more due to the particularities that this branch of technology entails. In this sense, one main concern lies in the question of how artificial intelligence–related patents should be disclosed, as well as the fulfilment with this requirement. To this aim, two case studies are conducted to show the heterogeneity of compliance with this requirement, focusing on patents filed at the European Patent Office. The methodology used in the case studies consists in the analysis of a core-AI patent and an AI application patent which allow to assess the differences in the disclosure of both inventions. The heterogeneity between both case studies highlights the relevance of the topic and allows to propose three recommendations to improve sufficiency of disclosure in AI-related patent claims: metrics and benchmark analysis, standardisation of patent claim construction by introducing a ‘claim chart’ and introducing a peer review patent programme.