Reviewerly/exHarmony huggingface dataset:
Complex Convolutional Neural Network for Signal Representation and Its Application to Radar Emitter Recognition

Signal representation and identification in wireless communication has recently aroused substantial concern due to the remarkable evolution of data-driven techniques. Consider there are vital complementary information between inphase (I) and quadrature (Q) components, and I and Q have respective emphasis on the signal. Therefore compact signal representation requires their joint interaction. However, it is a pity that most methods ignore their implicit relevance. This letter considers the codify of tacit knowledge between I/Q pairs. First, a complex-valued convolutional unit is proposed to mine individual information and to explore their complementation. Second, a complex convolutional neural network (CCNN) is built to achieve radar emitter recognition end-to-end. Experimental results demonstrate CCNN’s superiority on measured radar signals in comparison to other methods from literature.
Gaussian processes for the analysis of electrochemical impedance spectroscopy data: Prediction, filtering, and active learning

Electrochemical impedance spectroscopy (EIS) is a widespread characterization technique used to study electrochemical systems. However, several shortcomings still limit the application of this technique. First, EIS data, unless acquired in well-controlled experiments, is intrinsically noisy, hindering spectra regression and prediction. Second, many physicochemical properties, such as the charge-transfer resistance, are determined through non-unique equivalent circuits. Third, probed frequencies are usually log-spaced with a fixed number of points per decade, which is not necessarily optimal. Gaussian processes can be used to filter out noise in EIS data, determine the charge-transfer resistance as a stochastic variable, and optimize frequency placement. In this regard, a Gaussian-process-based, active-learning framework is developed to optimize EIS frequency selection for quick and accurate measurements. This work opens new avenues of research regarding the use of Gaussian processes for EIS experiment optimization.
Self sovereign and blockchain based access control: Supporting attributes privacy with zero knowledge

Recent years have witnessed, especially in Europe, a shift aimed at bringing users back at the center of digital systems. This has driven innovation towards the affirmation of decentralized systems, in line with the Self Sovereign Identity paradigm. User control over the consumption and disclosure of their data is a key topic of such drive. In this paper we show how it is possible to apply this increasingly popular concept to a traditionally centralized and opaque digital process: Access Control systems. To this aim we expand the XACML standard for Attribute Based Access Control systems with the novel concept of private attributes, i.e. attributes whose values should not be disclosed while still contributing to a policy evaluation result after user consent. Basing our proposal on blockchain systems, we show how to leverage smart contracts and zero knowledge proofs to allow for transparent policies evaluation without disclosing the value of such sensible attributes. Beside formalizing our goals, presenting the system architecture, and discussing its advantages and drawbacks with respect to the traditional model, we provide a reference example to show our proposal innovative capabilities and provide a prototype experimental evaluation to prove its feasibility.
Natural Test Generation for Precise Testing of Question Answering Software

Question answering (QA) software uses information retrieval and natural language processing techniques to automatically answer questions posed by humans in a natural language. Like other AI-based software, QA software may contain bugs. To automatically test QA software without human labeling, previous work extracts facts from question answer pairs and generates new questions to detect QA software bugs. Nevertheless, the generated questions could be ambiguous, confusing, or with chaotic syntax, which are unanswerable for QA software. As a result, a relatively large proportion of the reported bugs are false positives. In this work, we proposed QAQA, a sentence-level mutation based metamorphic testing technique for QA software. To eliminate false positives and achieve precise automatic testing, QAQA leverages five Metamorphic Relations (MRs) as well as semantics-guided search and enhanced test oracles. Our evaluation on three QA datasets demonstrates that QAQA outperforms the state-of-the-art in both quantity (8,133 vs. 6,601 bugs) and quality (97.67% vs. 49% true positive rate) of the reported bugs. Moreover, the test inputs generated by QAQA successfully reduce MR violation rate from 44.29% to 20.51% when being adopted in fine-tuning the QA software under test.
Web Service Recommendation via Combining Bilinear Graph Representation and xDeepFM Quality Prediction

With the increasing number of Web services, how to provide developers with Web services that meet their Mashup requirements accurately and efficiently has become a challenging problem. Therefore, focusing on the problem of “recommending appropriate services to build high-quality Mashup applications”, this paper proposes a Web service recommendation method via combining bilinear graph attention representation and xDeepFM (eXtreme Deep Factorization Machine) quality prediction. This method is based on content and structure-oriented service function classification and combines it with the service invocation prediction based on multi-dimensional quality attributes. Firstly, it uses the Word2Vec model to learn the latent semantic representations from service description documents. Then, it constructs the service relationship network according to tags and shared annotation relationships of Web services. Next, a bilinear aggregator is used to model the pairwise interactions between neighbor service nodes. Integrated with the traditional weighted sum aggregator, a bilinear graph neural network (BGNN) with stronger node representation ability is constructed. It exploits BGNN to calculate the representation of service nodes in the network and divides services into different functionality clusters. Finally, the high-quality representation results are combined with multi-dimensional QoS attributes. Aiming at the Web services in the service cluster, it utilizes xDeepFM to model and mine the complex interactions between Web services” features, and predict and rank the invocation scores of Web services. The experimental results on the real dataset of ProgrammableWeb show that compared with the other ten methods, the proposed approach has better performance in terms of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accuracy</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Recall</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F1</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Logloss</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AUC</i> , and has better performance in classification and recommendation.
Towards Homogeneous Modality Learning and Multi-Granularity Information Exploration for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) is a challenging and essential task, which aims to retrieve a set of person images over visible and infrared camera views. In order to mitigate the impact of large modality discrepancy existing in heterogeneous images, previous methods attempt to apply generative adversarial network (GAN) to generate the modality-consisitent data. However, due to severe color variations between the visible domain and infrared domain, the generated fake cross-modality samples often fail to possess good qualities to fill the modality gap between synthesized scenarios and target real ones, which leads to sub-optimal feature representations. In this work, we address cross-modality matching problem with Aligned Grayscale Modality (AGM), an unified dark-line spectrum that reformulates visible-infrared dual-mode learning as a gray-gray single-mode learning problem. Specifically, we generate the grasycale modality from the homogeneous visible images. Then, we train a style tranfer model to transfer infrared images into homogeneous grayscale images. In this way, the modality discrepancy is significantly reduced in the image space. In order to reduce the remaining appearance discrepancy, we further introduce a multi-granularity feature extraction network to conduct feature-level alignment. Rather than relying on the global information, we propose to exploit local (head-shoulder) features to assist person Re-ID, which complements each other to form a stronger feature descriptor. Comprehensive experiments implemented on the mainstream evaluation datasets include SYSU-MM01 and RegDB indicate that our method can significantly boost cross-modality retrieval performance against the state of the art methods.
SEAT-YOLO: A squeeze-excite and spatial attentive you only look once architecture for shadow detection

Recently, several deep learning-based architectures have been utilized for the recognition of shadow regions in images and videos with the aim of classification and segmentation of shadow instances. The present work aims towards solving the problem of shadow recognition as a detection and regression problem. To solve the problem of shadow detection in images and videos, we have developed SEAT-YOLO, a deep learning-based shadow detection architecture. The backbone network of the proposed architecture is based on convolution layers, squeeze-excite blocks, spatial attention module, and spatial pyramid pooling layer and the detection network utilizes YOLO detection heads. The proposed SEAT-YOLO architecture is trained and tested on the publicly available SBU shadow detection dataset. For shadow detection on the SBU dataset, it achieved a mAP value of 59.53 %. Furthermore, the proposed SEAT-YOLO is capable of detecting small shadow regions with high accuracy which is a challenge with most deep learning-based detection architectures. Moreover, the other advantage of the proposed SEAT-YOLO is its ability to detect multiple shadow regions in a single image by drawing precise bounding boxes for overlapping shadows. The proposed SEAT-YOLO architecture has high practical implications in driverless vehicles for detection of shadow regions and take effective driving decisions.
Self-regulated learning with mobile devices for university students: exploring the impacts on academic vocabulary development

Abstract The present study explored the impact of mobile-assisted self-regulated learning (SRL) on developing university students’ knowledge of academic vocabulary in English. The participants were 49 students in experimental (N = 28) and control (N = 21) learning conditions. Those in the experimental group studied 70 academic words using digital flashcards application installed on their mobile devices (i.e., smartphones), and the control group learned the same words using traditional materials (i.e., word lists). The participants’ vocabulary knowledge was tested before and after the treatment, and their test scores were analyzed using mixed between-within-subjects analysis of variance (ANOVA). The findings indicated a significant main effect for time in both SRL interventions, as both strategies were effective in scaffolding university students’ academic vocabulary development, and the learning gains were significant in post- and delayed post-tests. Additionally, the findings revealed a significant main effect for mobile-assisted SRL in that participants in the experimental group outperformed those in the control group on post-treatment tests. Accordingly, the results provide empirical evidence for the effectiveness of mobile-assisted vocabulary learning in the SRL mode. The study offers implications for addressing the academic vocabulary learning needs of English as a Foreign Language (EFL) university students.
A survey on narrative extraction from textual data

Abstract Narratives are present in many forms of human expression and can be understood as a fundamental way of communication between people. Computational understanding of the underlying story of a narrative, however, may be a rather complex task for both linguists and computational linguistics. Such task can be approached using natural language processing techniques to automatically extract narratives from texts. In this paper, we present an in depth survey of narrative extraction from text, providing a establishing a basis/framework for the study roadmap to the study of this area as a whole as a means to consolidate a view on this line of research. We aim to fulfill the current gap by identifying important research efforts at the crossroad between linguists and computer scientists. In particular, we highlight the importance and complexity of the annotation process, as a crucial step for the training stage. Next, we detail methods and approaches regarding the identification and extraction of narrative components, their linkage and understanding of likely inherent relationships, before detailing formal narrative representation structures as an intermediate step for visualization and data exploration purposes. We then move into the narrative evaluation task aspects, and conclude this survey by highlighting important open issues under the domain of narratives extraction from texts that are yet to be explored.
GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation

Conversations have become a critical data format on social media platforms. Understanding conversation from emotion, content and other aspects also attracts increasing attention from researchers due to its widespread application in human-computer interaction. In real-world environments, we often encounter the problem of incomplete modalities, which has become a core issue of conversation understanding. To address this problem, researchers propose various methods. However, existing approaches are mainly designed for individual utterances rather than conversational data, which cannot fully exploit temporal and speaker information in conversations. To this end, we propose a novel framework for incomplete multimodal learning in conversations, called "Graph Complete Network (GCNet)," filling the gap of existing works. Our GCNet contains two well-designed graph neural network-based modules, "Speaker GNN" and "Temporal GNN," to capture temporal and speaker dependencies. To make full use of complete and incomplete data, we jointly optimize classification and reconstruction tasks in an end-to-end manner. To verify the effectiveness of our method, we conduct experiments on three benchmark conversational datasets. Experimental results demonstrate that our GCNet is superior to existing state-of-the-art approaches in incomplete multimodal learning.
A chaotic PRNG tested with the heuristic Differential Evolution

A pseudo random number generator (PRNG) is built with a chaotic map without fixed points implemented using fixed-point arithmetic. The generated binary sequences by the PRNG pass all the TestU01 statistical tests to prove the randomness of the generated binary sequences. Three multimodal benchmark functions are optimized with the heuristic Differential Evolution (DE) using the proposed PRNG, and these are compared with the DE using the standard random function available in the C programming language. If the shuffling technique is added to the generated chaotic random sequences, both approaches give the same statistically solutions. A hardware design in FPGA of the PRNG also is shown.
Privacy-preserving federated mining of frequent itemsets

In the growing concerns about data privacy and increasingly stringent data security regulations, it is not feasible to directly mine data or share data if the dataset contains private data. Collecting and analyzing data from multiple parties becomes difficult. Federated learning can analyze multiple datasets while preventing the original data from being sent. However, existing federated learning frameworks are based on the Apriori property of mining frequent patterns, which has the disadvantage of low efficiency and multiple scanning datasets. Therefore, to improve mining efficiency, a federated learning framework (named FedFIM) is proposed in this paper. FedFIM collects the noisy responses sent by participants, which are used by the server to reconstruct the noisy dataset. After that, the noisy dataset is applied to the non-Apriori algorithm to mine frequent patterns. In addition, FedFIM incorporates a differential privacy-preserving mechanism into federated learning, which addresses the need for federated modeling and protects data privacy. Experiments show that FedFIM has a shorter running time and better applicability compared to the most advanced benchmark.
Active learning for regression by inverse distance weighting

This paper proposes an active learning (AL) algorithm to solve regression problems based on inverse-distance weighting functions for selecting the feature vectors to query. The algorithm has the following features: (i) supports both pool-based and population-based sampling; (ii) is not tailored to a particular class of predictors; (iii) can handle known and unknown constraints on the queryable feature vectors; and (iv) can run either sequentially, or in batch mode, depending on how often the predictor is retrained. The potentials of the method are shown in numerical tests on illustrative synthetic problems and real-world datasets. An implementation of the algorithm, that we call IDEAL (Inverse-Distance based Exploration for Active Learning), is available at http://cse.lab.imtlucca.it/bemporad/ideal.
Self-supervised contrastive learning with random walks for medical image segmentation with limited annotations

Medical image segmentation has seen significant progress through the use of supervised deep learning. Hereby, large annotated datasets were employed to reliably segment anatomical structures. To reduce the requirement for annotated training data, self-supervised pre-training strategies on non-annotated data were designed. Especially contrastive learning schemes operating on dense pixel-wise representations have been introduced as an effective tool. In this work, we expand on this strategy and leverage inherent anatomical similarities in medical imaging data. We apply our approach to the task of semantic segmentation in a semi-supervised setting with limited amounts of annotated volumes. Trained alongside a segmentation loss in one single training stage, a contrastive loss aids to differentiate between salient anatomical regions that conform to the available annotations. Our approach builds upon the work of Jabri et al. (2020), who proposed cyclical contrastive random walks (CCRW) for self-supervision on palindromes of video frames. We adapt this scheme to operate on entries of paired embedded image slices. Using paths of cyclical random walks bypasses the need for negative samples, as commonly used in contrastive approaches, enabling the algorithm to discriminate among relevant salient (anatomical) regions implicitly. Further, a multi-level supervision strategy is employed, ensuring adequate representations of local and global characteristics of anatomical structures. The effectiveness of reducing the amount of required annotations is shown on three MRI datasets. A median increase of 8.01 and 5.90 pp in the Dice Similarity Coefficient (DSC) compared to our baseline could be achieved across all three datasets in the case of one and two available annotated examples per dataset.
High-dimensional spatial mode sorting and optical circuit design using multi-plane light conversion

Multi-plane light converters (MPLCs) are an emerging class of optical devices capable of converting a set of input spatial light modes to a new target set of output modes. This operation represents a linear optical transformation—a much sought after capability in photonics. MPLCs have potential applications in both the classical and quantum optics domains, in fields ranging from optical communications to optical computing and imaging. They consist of a series of diffractive optical elements (the “planes”), typically separated by a free space. The phase delays imparted by each plane are determined by the process of inverse-design, most often using an adjoint algorithm known as the wavefront matching method (WMM), which optimizes the correlation between the target and actual MPLC outputs. In this work, we investigate high mode capacity MPLCs to create arbitrary spatial mode sorters and linear optical circuits. We focus on designs possessing low numbers of phase planes to render these MPLCs experimentally feasible. To best control light in this scenario, we develop a new inverse-design algorithm, based on gradient ascent with a specifically tailored objective function, and show how, in the low-plane limit, it converges to MPLC designs with a substantially lower modal cross-talk and higher fidelity than those achievable using the WMM. We experimentally demonstrate several prototype few-plane high-dimensional spatial mode sorters, operating on up to 55 modes, capable of sorting photons based on their Zernike mode or orbital angular momentum state, or an arbitrarily randomized spatial mode basis. We discuss the advantages and drawbacks of these proof-of-principle prototypes and describe future improvements. Our work points to a bright future for high-dimensional MPLC-based technologies.
DF-UDetector: An effective method towards robust deepfake detection via feature restoration

The abuse of deepfakes, a rising face swap technique, causes severe concerns about the authenticity of visual content and the dissemination of misinformation. To alleviate the threats imposed by deepfakes, a vast body of data-centric detectors has been deployed. However, the performance of these methods can be easily defected by degradations on deepfakes. To improve the performance of degradation deepfake detection, we creatively explore the recovery method in the feature space to preserve the artifacts for detection instead of directly in the image domain. In this paper, we propose a method, namely DF-UDetector, against degradation deepfakes by modeling the degraded images and transforming the extracted features to a high-quality level. To be specific, the whole model consists of three key components: an image feature extractor to capture image features, a feature transforming module to map the degradation features into a higher quality, and a discriminator to determine whether the feature map is of high quality enough. Extensive experiments on multiple video datasets show that our proposed model performs comparably or even better than state-of-the-art counterparts. Moreover, DF-UDetector outperforms by a small margin when detecting deepfakes in the wild.