Reviewerly/exHarmony huggingface dataset:
Spark solutions for discovering fuzzy association rules in Big Data

The high computational impact when mining fuzzy association rules grows significantly when managing very large data sets, triggering in many cases a memory overflow error and leading to the experiment failure without its conclusion. It is in these cases when the application of Big Data techniques can help to achieve the experiment completion. Therefore, in this paper several Spark algorithms are proposed to handle with massive fuzzy data and discover interesting association rules. For that, we based on a decomposition of interestingness measures in terms of α-cuts, and we experimentally demonstrate that it is sufficient to consider only 10 equidistributed α-cuts in order to mine all significant fuzzy association rules. Additionally, all the proposals are compared and analysed in terms of efficiency and speed up, in several datasets, including a real dataset comprised of sensor measurements from an office building.
Face Sketch Synthesis via Semantic-Driven Generative Adversarial Network

Face sketch synthesis has made significant progress with the development of deep neural networks in these years. The delicate depiction of sketch portraits facilitates a wide range of applications like digital entertainment and law enforcement. However, accurate and realistic face sketch generation is still a challenging task due to the illumination variations and complex backgrounds in the real scenes. To tackle these challenges, we propose a novel Semantic-Driven Generative Adversarial Network (SDGAN) which embeds global structure-level style injection and local class-level knowledge re-weighting. Specifically, we conduct facial saliency detection on the input face photos to provide overall facial texture structure, which could be used as a global type of prior information. In addition, we exploit face parsing layouts as the semantic-level spatial prior to enforce globally structural style injection in the generator of SDGAN. Furthermore, to enhance the realistic effect of the details, we propose a novel Adaptive Re-weighting Loss (ARLoss) which dedicates to balance the contributions of different semantic classes. Experimentally, our extensive experiments on CUFS and CUFSF datasets show that our proposed algorithm achieves state-of-the-art performance.
Towards the evaluation of automatic simultaneous speech translation from a communicative perspective

In recent years, automatic speech-to-speech and speech-to-text translation has gained momentum thanks to advances in artificial intelligence, especially in the domains of speech recognition and machine translation.The quality of such applications is commonly tested with automatic metrics, such as BLEU, primarily with the goal of assessing improvements of releases or in the context of evaluation campaigns.However, little is known about how the output of such systems is perceived by end users or how they compare to human performances in similar communicative tasks.In this paper, we present the results of an experiment aimed at evaluating the quality of a real-time speech translation engine by comparing it to the performance of professional simultaneous interpreters.To do so, we adopt a framework developed for the assessment of human interpreters and use it to perform a manual evaluation on both human and machine performances.In our sample, we found better performance for the human interpreters in terms of intelligibility, while the machine performs slightly better in terms of informativeness.The limitations of the study and the possible enhancements of the chosen framework are discussed.Despite its intrinsic limitations, the use of this framework represents a first step towards a user-centric and communicationoriented methodology for evaluating real-time automatic speech translation.
Optimal distribution-preserving downsampling of large biomedical data sets (opdisDownsampling)

Motivation The size of today’s biomedical data sets pushes computer equipment to its limits, even for seemingly standard analysis tasks such as data projection or clustering. Reducing large biomedical data by downsampling is therefore a common early step in data processing, often performed as random uniform class-proportional downsampling. In this report, we hypothesized that this can be optimized to obtain samples that better reflect the entire data set than those obtained using the current standard method. Results By repeating the random sampling and comparing the distribution of the drawn sample with the distribution of the original data, it was possible to establish a method for obtaining subsets of data that better reflect the entire data set than taking only the first randomly selected subsample, as is the current standard. Experiments on artificial and real biomedical data sets showed that the reconstruction of the remaining data from the original data set from the downsampled data improved significantly. This was observed with both principal component analysis and autoencoding neural networks. The fidelity was dependent on both the number of cases drawn from the original and the number of samples drawn. Conclusions Optimal distribution-preserving class-proportional downsampling yields data subsets that reflect the structure of the entire data better than those obtained with the standard method. By using distributional similarity as the only selection criterion, the proposed method does not in any way affect the results of a later planned analysis.
Item Response Ranking for Cognitive Diagnosis

Cognitive diagnosis, a fundamental task in education area, aims at providing an approach to reveal the proficiency level of students on knowledge concepts. Actually, monotonicity is one of the basic conditions in cognitive diagnosis theory, which assumes that student's proficiency is monotonic with the probability of giving the right response to a test item. However, few of previous methods consider the monotonicity during optimization. To this end, we propose Item Response Ranking framework (IRR), aiming at introducing pairwise learning into cognitive diagnosis to well model the monotonicity between item responses. Specifically, we first use an item specific sampling method to sample item responses and construct response pairs based on their partial order, where we propose the two-branch sampling methods to handle the unobserved responses. After that, we use a pairwise objective function to exploit the monotonicity in the pair formulation. In fact, IRR is a general framework which can be applied to most of contemporary cognitive diagnosis models. Extensive experiments demonstrate the effectiveness and interpretability of our method.
MSST-RT: Multi-Stream Spatial-Temporal Relative Transformer for Skeleton-Based Action Recognition

Skeleton-based human action recognition has made great progress, especially with the development of a graph convolution network (GCN). The most important work is ST-GCN, which automatically learns both spatial and temporal patterns from skeleton sequences. However, this method still has some imperfections: only short-range correlations are appreciated, due to the limited receptive field of graph convolution. However, long-range dependence is essential for recognizing human action. In this work, we propose the use of a spatial-temporal relative transformer (ST-RT) to overcome these defects. Through introducing relay nodes, ST-RT avoids the transformer architecture, breaking the inherent skeleton topology in spatial and the order of skeleton sequence in temporal dimensions. Furthermore, we mine the dynamic information contained in motion at different scales. Finally, four ST-RTs, which extract spatial-temporal features from four kinds of skeleton sequence, are fused to form the final model, multi-stream spatial-temporal relative transformer (MSST-RT), to enhance performance. Extensive experiments evaluate the proposed methods on three benchmarks for skeleton-based action recognition: NTU RGB+D, NTU RGB+D 120 and UAV-Human. The results demonstrate that MSST-RT is on par with SOTA in terms of performance.
Gaussian Differential Privacy

Differential privacy has seen remarkable success as a rigorous and practical formalization of data privacy in the past decade. This privacy definition and its divergence based relaxations, however, have several acknowledged weaknesses, either in handling composition of private algorithms or in analyzing important primitives like privacy amplification by subsampling. Inspired by the hypothesis testing formulation of privacy, this paper proposes a new relaxation, which we term `f-differential privacy' (f-DP). This notion of privacy has a number of appealing properties and, in particular, avoids difficulties associated with divergence based relaxations. First, f-DP preserves the hypothesis testing interpretation. In addition, f-DP allows for lossless reasoning about composition in an algebraic fashion. Moreover, we provide a powerful technique to import existing results proven for original DP to f-DP and, as an application, obtain a simple subsampling theorem for f-DP. In addition to the above findings, we introduce a canonical single-parameter family of privacy notions within the f-DP class that is referred to as `Gaussian differential privacy' (GDP), defined based on testing two shifted Gaussians. GDP is focal among the f-DP class because of a central limit theorem we prove. More precisely, the privacy guarantees of \emph{any} hypothesis testing based definition of privacy (including original DP) converges to GDP in the limit under composition. The CLT also yields a computationally inexpensive tool for analyzing the exact composition of private algorithms. Taken together, this collection of attractive properties render f-DP a mathematically coherent, analytically tractable, and versatile framework for private data analysis. Finally, we demonstrate the use of the tools we develop by giving an improved privacy analysis of noisy stochastic gradient descent.
Decision Making with Differential Privacy under a Fairness Lens

Many agencies release datasets and statistics about groups of individuals that are used as input to a number of critical decision processes. To conform with privacy and confidentiality requirements, these agencies are often required to release privacy-preserving versions of the data. This paper studies the release of differentially private datasets and analyzes their impact on some critical resource allocation tasks under a fairness perspective. The paper shows that, when the decisions take as input differentially private data, the noise added to achieve privacy disproportionately impacts some groups over others. The paper analyzes the reasons for these disproportionate impacts and proposes guidelines to mitigate these effects. The proposed approaches are evaluated on critical decision problems that use differentially private census data.
No-FAT: Architectural Support for Low Overhead Memory Safety Checks

Memory safety continues to be a significant software reliability and security problem, and low overhead and low complexity hardware solutions have eluded computer designers. In this paper, we explore a pathway to deployable memory safety defenses. Our technique builds on a recent trend in software: the usage of binning memory allocators. We observe that if memory allocation sizes (e.g., malloc sizes) are made an architectural feature, then it is possible to overcome many of the thorny issues with traditional approaches to memory safety such as compatibility with unsecured software and significant performance degradation. We show that our architecture, No-FAT, incurs an overhead of 8% on SPEC CPU2017 benchmarks, and our VLSI measurements show low power and area overheads. Finally, as No-FAT’s hardware is aware of the memory allocation sizes, it effectively mitigates certain speculative attacks (e.g., Spectre-V1) with no additional cost. When our solution is used for pre-deployment fuzz testing it can improve fuzz testing bandwidth by an order of magnitude compared to state-of-the-art approaches.
Enhanced SOMHunter for Known-item Search in Lifelog Data

SOMHunter represents a modern light-weight framework for known-item search in datasets of visual data like images or videos. The framework combines an effective W2VV++ text-to-image search approach, a traditional Bayesian like model for maintenance of relevance scores influenced by positive examples, and several types of exploration and exploitation displays. With this initial setting in 2020, already the first prototype of the system turned out to be highly competitive in comparison with other state-of-the-art systems at Video Browser Showdown and Lifelog Search Challenge competitions. In this paper, we present a new version of the system further extending the list of visual data search capabilities. The new version combines localized text queries with collage queries tested at VBS 2021 in two separate systems by our team. Furthermore, the new version of SOMHunter will integrate also the new CLIP text search model recently released by OpenAI. We believe that all the extensions will improve chances to effectively initialize the search that can continue with already supported browsing capabilities.
ML-ANet: A Transfer Learning Approach Using Adaptation Network for Multi-label Image Classification in Autonomous Driving

Abstract To reduce the discrepancy between the source and target domains, a new multi-label adaptation network (ML-ANet) based on multiple kernel variants with maximum mean discrepancies is proposed in this paper. The hidden representations of the task-specific layers in ML-ANet are embedded in the reproducing kernel Hilbert space (RKHS) so that the mean-embeddings of specific features in different domains could be precisely matched. Multiple kernel functions are used to improve feature distribution efficiency for explicit mean embedding matching, which can further reduce domain discrepancy. Adverse weather and cross-camera adaptation examinations are conducted to verify the effectiveness of our proposed ML-ANet. The results show that our proposed ML-ANet achieves higher accuracies than the compared state-of-the-art methods for multi-label image classification in both the adverse weather adaptation and cross-camera adaptation experiments. These results indicate that ML-ANet can alleviate the reliance on fully labeled training data and improve the accuracy of multi-label image classification in various domain shift scenarios.
AI-Based Natural Language Processing for the Generation of Meaningful Information Electronic Health Record (EHR) Data

Big real data are being created by the widespread introduction of electronic health record systems in healthcare, which create new spaces for clinical study. Since a great deal of useful therapeutic knowledge is found in clinical narratives, natural language strategies are used to retrieve details from clinical narratives in electronical health reports, in the sense of an artificial intelligence strategy. This capacity in the treatment of natural language may require automatic diagram analysis in order to classify individual clinically distinctive patients, reducing methodological variability in phenotype-defining, and obscuring biological variability in allergy, asthma, and immunology study. AI works at imitating individual executive processes. This introduces a fundamental change in healthcare, propelled by improved patient data and accelerated development in analytics. We evaluate and address the existing state of AI technologies in healthcare. Various forms (unstructured and structured) of health data may be added to AI. Machine learning approaches for structured data such as the traditional support vector and neural network, modern deep intelligence, and the natural handling of unstructured data are common AI strategies development involves. Cancer, neurology, and cardiology are the main disorders utilizing AI tools. In addition, we discuss the use of stroke AI in the three key fields of early identification, diagnosis, and recovery, and the estimation of outcomes and assessment of prognoses. The volume of digital data stored in electronic health records (EHR) has exploded during the last decade. While it was originally intended to store medical documents and to do routine healthcare function, other scholars noticed that such reports were utilized by different clinical IT applications in a secondary fashion. The machine-learning category has seen significant progress in deep learning over the same time. Current work on the application of thoroughness to clinical activities focused on EHR results, in which a range of deep learning methods and mechanisms extend to many kinds of clinical applications like the retrieval of knowledge and interpretation, the estimation of effects, phenotyping, and de-identification. They consider many shortcomings of current work, including sampling, data complexity, and the absence of formal standards. Real-world conditions and treatment procedures are recorded in EHR providing abundant and more generalizable empirical evidence of effectiveness relative to conventional randomized clinical trials. With the more and more widespread implementation of EHR internationally, the usage of EHR data to promote clinical practice is increasingly required. One big challenge to this is that a vast amount of data in EHR remains narrative. The chapter explains the context of medicinal analysis in natural languages and its specific applications in the retrieval and distribution of narrative knowledge in the EHR to assist clinical research.
Multi-granularity Textual Adversarial Attack with Behavior Cloning

Recently, the textual adversarial attack models become increasingly popular due to their successful in estimating the robustness of NLP models.However, existing works have obvious deficiencies.(1) They usually consider only a single granularity of modification strategies (e.g.word-level or sentence-level), which is insufficient to explore the holistic textual space for generation; (2) They need to query victim models hundreds of times to make a successful attack, which is highly inefficient in practice.To address such problems, in this paper we propose MAYA, a Multi-grAnularitY Attack model to effectively generate high-quality adversarial samples with fewer queries to victim models.Furthermore, we propose a reinforcement-learning based method to train a multi-granularity attack agent through behavior cloning with the expert knowledge from our MAYA algorithm to further reduce the query times.Additionally, we also adapt the agent to attack blackbox models that only output labels without confidence scores.We conduct comprehensive experiments to evaluate our attack models by attacking BiLSTM, BERT and RoBERTa in two different black-box attack settings and three benchmark datasets.Experimental results show that our models achieve overall better attacking performance and produce more fluent and grammatical adversarial samples compared to baseline models.Besides, our adversarial attack agent significantly reduces the query times in both attack settings.
Evaluation of Machine Learning Techniques for Email Spam Classification

Electronic mail (Email) is one of the official and very common way of exchanging data and information over digital and electronic devices.Millions of users worldwide use email to exchange data and information between email servers.On the other hand, unwanted emails or spam became phenomenon challenging major companies and organizations due to the volume of spam which is increasing dramatically every year.Spam is annoying and may contain harmful contents.In addition, spam consume computers, servers, and network resources, causes harmful bottleneck, effect on computing memory and speed of digital devices.Moreover, the time consumed by the users to remove unwanted emails is huge.There are many methods developed to filter spam like keyword matching blacklist/whitelist and header information processing.Though, classical methods like blocking the source to prevent the spam are not effective.This study demonstrates and reviews the performance evaluation of the most popular and effective machine learning techniques and algorithms such as Support Vector Machine, ANN, J48, and Naï ve Bayes for email spam classification and filtering.In con conclusion, support vector machine performs better than any individual algorithm in term of accuracy.This research contributes on the for the development of methods and techniques for better detection and prevention of spam.
Architecture-Driven Digital Image Correlation Technique (ADDICT) for the measurement of sub-cellular kinematic fields in speckle-free cellular materials

Measuring displacement and strain fields at low observable scales of complex microstructures still remains a challenge in experimental mechanics often because of the combination of low definition images with poor texture at this scale. This is the case for cellular materials, for which complex local phenomena can occur. The aim of this paper is to design and validate numerically and experimentally a Digital Image Correlation (DIC) technique for the measurement of local displacement fields of samples with complex cellular geometries (i.e. samples presenting multiple random holes). It consists of a DIC method assisted with a physically sound weak regularization using an elastic B-spline image-based model. This technique introduces a separation of scales above which DIC is dominant and below which it is assisted with image-based modeling. Several in silico experimentations are performed in order to finely analyze the influence of the introduced regularization lengths for different input mechanical behaviors (elastic, elasto-plastic and geometrically non-linear) and in comparison with true error quantification. We show that the method can estimate complex local displacement and strain fields with speckle-free low definition images, even in non-linear regimes such as local buckling or plasticity. Finally, an experimental validation is performed in 2D-DIC to allow for the comparison of the proposed method on low resolution speckle-free images with a classic DIC on speckled high resolution images.
Multimodal and Multitask Approach to Listener's Backchannel Prediction

The listener's backchannel has the important function of encouraging a current speaker to hold their turn and continue to speak, which enables smooth conversation. The listener monitors the speaker's turn-management (a.k.a. speaking and listening) willingness and his/her own willingness to display backchannel behavior. Many studies have focused on predicting the appropriate timing of the backchannel so that conversational agents can display backchannel behavior in response to a user who is speaking. To the best of our knowledge, none of them added the prediction of turn-changing and participants' turn-management willingness to the backchannel prediction model in dyad interactions. In this paper, we proposed a novel backchannel prediction model that can jointly predict turn-changing and turn-management willingness. We investigated the impact of modeling turn-changing and willingness to improve backchannel prediction. Our proposed model is based on trimodal inputs, that is, acoustic, linguistic, and visual cues from conversations. Our results suggest that adding turn-management willingness as a prediction task improves the performance of backchannel prediction within the multi-modal multi-task learning approach, while adding turn-changing prediction is not useful for improving the performance of backchannel prediction.