Reviewerly/exHarmony huggingface dataset:
Multi-subspace Implicit Alignment for Cross-modal Retrieval on Cooking Recipes and Food Images

Cross-modal retrieval technology can help people quickly achieve mutual information between cooking recipes and food images. Both the embeddings of the image and the recipe consist of multiple representation subspaces. We argue that multiple aspects in the recipe are related to multiple regions in the food image. It is challenging to improve the cross-modal retrieval quality by making full use of the implicit connection between multiple subspaces of recipes and images. In this paper, we propose a multi-subspace implicit alignment cross-modal retrieval framework of recipes and images. Our framework learns multi-subspace information about cooking recipes and food images with multi-head attention networks; the implicit alignment at the subspace level promotes narrowing the semantic gap between recipe embeddings and food image embeddings; triple loss and adversarial loss are combined to help our framework for cross-modal learning. The experimental results show that our framework significantly outperforms to state-of-the-art methods in terms of MedR and [email protected] on Recipe 1M.
Spam Detection Over Call Transcript Using Deep Learning

An increasing volume of malicious spam calls has ushered an urgent need for a system that can automatically detect them. A robust spam call detection system must identify and block spam calls with high accuracy. Unlike current techniques that use crowd sourcing and are prone to phone number spoofing, this paper proposes a novel deep learning-based approach to classify phone calls, as spam or ham, based on the generated call transcript. This paper proposes a means of classification using embedded features with a 2-Path Hybrid Model (2PHM), comprising of a Long Short Term Memory Network (LSTM) and a Sentence Similarity Network. The 2PHM uses the transcript generated from the call by converting it into feature vectors, embedded in a high dimensional latent space, using the Universal Sentence Encoder embedding technique. Both paths of the hybrid model generate results that are weighed in an 85:15 ratio to finally generate a classification. The proposed system has been tested against Machine Learning models like Naïve Bayes, K-Nearest Neighbors, Random Forests and Support Vector Machines and performs better than them, with an overall classification accuracy of 93% and a latency of 3 s.
Contraction of Dynamically Masked Deep Neural Networks for Efficient Video Processing

Sequential data such as video are characterized by spatio-temporal redundancies. As of yet, few deep learning algorithms exploit them to decrease the often massive cost during inference. This work leverages correlations in video data to reduce the size and run-time cost of deep neural networks. Drawing upon the simplicity of the typically used ReLU activation function, we replace this function by dynamically updating masks. The resulting network is a simple chain of matrix multiplications and bias additions, which can be contracted into a single weight matrix and bias vector. Inference then reduces to an affine transformation of the input sample with these contracted parameters. We show that the method is akin to approximating the neural network with a first-order Taylor expansion around a dynamically updating reference point. For triggering these updates, one static and three data-driven mechanisms are analyzed. We evaluate the proposed algorithm on a range of tasks, including pose estimation on surveillance data, road detection on KITTI driving scenes, object detection on ImageNet videos, as well as denoising MNIST digits, and obtain compression rates up to <inline-formula> <tex-math notation="LaTeX">$3.6\times $ </tex-math></inline-formula>.
Semi-Quantitative Analysis of Major Elements and Minerals: Clues from a Late Pleistocene Core from Campos Basin

Element and mineral associations are fundamental parameters for palaeoceanographical reconstructions but laboratory methodologies are expensive, time-consuming and need a lot of material. Here, we investigate the quality and reliability of XRF measurements of major elements (Fe, Ti and Ca) using BTX II Benchtop, by comparing them with previous ICP-OES elemental analysis for a set of Late Pleistocene marine sediments from Campos Basin. Although the numerical values of the logarithmic form of the elementary ratios were different, the lnTi/Ca and lnFe/Ca ratios measured by both techniques (XRF and ICP-OES) presented similar downcore results. To correct the XRF intensity data, a linear regression model was calculated and, based on the linear equation generated, the logarithmic values of the elementary XRF ratios were corrected. After the correction, One-Sample t-test and Bland–Altman plot show that both techniques obtained similar results. In addition, a brief paleoceanographic interpretation, during the MIS 5 and MIS 4 periods, was conducted by comparing mineralogical and elementary analysis aiming to reconstruct the variations of the terrigenous input to the studied area. As a conclusion, the results from XRF measurements (BTX II) presented to confirm the viability of such a technique, showing that analysis using BTX II is a reliable, cheap, rapid and non-destructive option for obtaining elementary ratios and mineralogical downcore results at high resolution, allowing stratigraphic and paleoceanographic interpretations.
An Optimal Algorithm for Decentralized Finite-Sum Optimization

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 29 June 2020Accepted: 14 June 2021Published online: 01 November 2021Keywordsdecentralized optimization, stochastic optimization, variance reductionAMS Subject Headings49, 68, 90Publication DataISSN (print): 1052-6234ISSN (online): 1095-7189Publisher: Society for Industrial and Applied MathematicsCODEN: sjope8
Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing Simulation-to-Real Domain Shift in LiDAR Bird's Eye View

The performance of object detection methods based on LiDAR information is heavily impacted by the availability of training data, usually limited to certain laser devices. As a result, the use of synthetic data is becoming popular when training neural network models, as both sensor specifications and driving scenarios can be generated ad-hoc. However, bridging the gap between virtual and real environments is still an open challenge, as current simulators cannot completely mimic real LiDAR operation. To tackle this issue, domain adaptation strategies are usually applied, obtaining remarkable results on vehicle detection when applied to range view (RV) and bird's eye view (BEV) projections while failing for smaller road agents. In this paper, we present a BEV domain adaptation method based on CycleGAN that uses prior semantic classification in order to preserve the information of small objects of interest during the domain adaptation process. The quality of the generated BEVs has been evaluated using a state-of-the-art 3D object detection framework at KITTI 3D Object Detection Benchmark. The obtained results show the advantages of the proposed method over the existing alternatives.
Automatic Search for Bit-Based Division Property

Division properties, introduced by Todo at Eurocrypt 2015, are an extension of square attack (also called saturation attack or integral cryptanalysis). Given their importance, a large number of works tried to offer automatic tools to find division properties, primarily based on MILP or SAT/SMT. This paper studies better modeling techniques for finding division properties using the Constraint Programming and SAT/SMT-based automatic tools. We use the fact that the Quine-McCluskey algorithm produces a concise CNF representation corresponding to the division trail table of an Sbox. As a result, we can offer significantly more compact models, which allow SAT and Constraint Programming tools to outperform previous results.
Indian Sign Language Gesture Recognition in Real-Time using Convolutional Neural Networks

Communication is a basic requirement of an individual to exchange feelings, thoughts, and ideas, but the hearing and speech impaired community finds it difficult to interact with the vast majority of people. Sign language facilitates communication between the hearing and speech impaired person and the rest of society. The Rights of Persons with Disabilities (RPWD) Act, 2016, was also passed by the Indian government, which acknowledges Indian Sign Language (ISL) and mandates the use of sign language interpreters in all government-aided organizations and the public sector proceedings. Unfortunately, a large percentage of the Indian population is not familiar with the semantics of the gestures associated with ISL. To bridge this communication gap, this paper proposes a model to identify and classify Indian Sign Language gestures in real-time using Convolutional Neural Networks (CNN). The model has been developed using OpenCV and Keras implementation of CNNs and aims to classify 36 ISL gestures representing 0-9 numbers and A-Z alphabets by converting them to their text equivalents. The dataset created and used consists of 300 images for each gesture which were fed into the CNN model for training and testing purposes. The proposed model was successfully implemented and achieved 99.91% accuracy for the test images.
Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification

Extreme multi-label classification (XMC) aims to learn a model that can tag data points with a subset of relevant labels from an extremely large label set. Real world e-commerce applications like personalized recommendations and product advertising can be formulated as XMC problems, where the objective is to predict for a user a small subset of items from a catalog of several million products. For such applications, a common approach is to organize these labels into a tree, enabling training and inference times that are logarithmic in the number of labels [23]. While training a model once a label tree is available is well studied, designing the structure of the tree is a difficult task that is not yet well understood, and can dramatically impact both model latency and statistical performance. Existing approaches to tree construction either optimize exclusively for statistical performance or optimize exclusively for latency. We propose an efficient information theory inspired algorithm to construct intermediate operating points that trade off between the benefits of both, which was not previously possible. We corroborate our theoretical analysis with numerical results, showing that on the Wiki-500K [4] benchmark dataset our method can reduce a proxy for expected latency by up to 28% while maintaining the same accuracy as Parabel [23]. On several datasets derived from e-commerce customer logs, our modified label tree is able to improve this expected latency metric by up to 20% while maintaining the same accuracy. Finally, we discuss challenges in realizing these latency improvements in deployed models.
Shakespeare and principal components analysis

Abstract The use of principal components analysis (PCA) in literary studies was pioneered by John Burrows. From him it was adopted by the New Oxford Shakespeare project team, who used it to support their controversial attributions of parts of Arden of Faversham to Shakespeare and parts of the Henry VI trilogy to Marlowe. This essay examines a representative sample of the PCA tests done using function words by that team. It observes that, as with their Zeta method, they used the unsound bisector line method of interpretation. By calculating information which they did not disclose, it finds that their decision to discard all but two principal components was not justified by the data they were using, putting into doubt their interpretations of the results. It shows that their test method is vulnerable by design to the making of false attributions and demonstrates this by a new experiment. By a further experiment, it shows that the PCA tests may be telling us that differences between characters are greater than those between authors, at least in the use of function words. It concludes that the PCA tests done by that project cannot be relied on; and, given the already demonstrated unreliability of their Zeta tests, these new findings invalidate all work presented in the influential book Shakespeare, Computers, and the Mystery of Authorship.
Semi-Supervised Classification via Hypergraph Convolutional Extreme Learning Machine

Extreme Learning Machine (ELM) is characterized by simplicity, generalization ability, and computational efficiency. However, previous ELMs fail to consider the inherent high-order relationship among data points, resulting in being powerless on structured data and poor robustness on noise data. This paper presents a novel semi-supervised ELM, termed Hypergraph Convolutional ELM (HGCELM), based on using hypergraph convolution to extend ELM into the non-Euclidean domain. The method inherits all the advantages from ELM, and consists of a random hypergraph convolutional layer followed by a hypergraph convolutional regression layer, enabling it to model complex intraclass variations. We show that the traditional ELM is a special case of the HGCELM model in the regular Euclidean domain. Extensive experimental results show that HGCELM remarkably outperforms eight competitive methods on 26 classification benchmarks.
In-Situ Joint Light and Medium Estimation for Underwater Color Restoration

The majority of Earth’s surface is situated in the deep sea and thus remains deprived of natural light. Such adverse underwater environments have to be explored with powerful camera-light systems. In order to restore the colors in images taken by such systems, we need to jointly estimate physically-meaningful optical parameters of the light as well as the water column. We thus propose an integrated in-situ estimation approach and a complementary surface texture recovery strategy, which also removes shadows as a by-product. As we operate in a scattering medium under inhomogeneous lighting conditions, the volumetric effects are difficult to capture in closed-form solutions. Hence, we leverage the latest progress in Monte Carlo-based differentiable ray tracing that becomes tractable through recent GPU RTX-hardware acceleration. Evaluations on synthetic data and in a water tank show that we can estimate physically meaningful parameters, which enables color restoration. The approaches could also be employed to other camera-light systems (AUV, robot, car, endoscope) operating either in the dark, in fog – or – underwater.
Automated annotations of epithelial cells and stroma in <scp>hematoxylin–eosin</scp>‐stained whole‐slide images using cytokeratin re‐staining

The diagnosis of solid tumors of epithelial origin (carcinomas) represents a major part of the workload in clinical histopathology. Carcinomas consist of malignant epithelial cells arranged in more or less cohesive clusters of variable size and shape, together with stromal cells, extracellular matrix, and blood vessels. Distinguishing stroma from epithelium is a critical component of artificial intelligence (AI) methods developed to detect and analyze carcinomas. In this paper, we propose a novel automated workflow that enables large-scale guidance of AI methods to identify the epithelial component. The workflow is based on re-staining existing hematoxylin and eosin (H&E) formalin-fixed paraffin-embedded sections by immunohistochemistry for cytokeratins, cytoskeletal components specific to epithelial cells. Compared to existing methods, clinically available H&E sections are reused and no additional material, such as consecutive slides, is needed. We developed a simple and reliable method for automatic alignment to generate masks denoting cytokeratin-rich regions, using cell nuclei positions that are visible in both the original and the re-stained slide. The registration method has been compared to state-of-the-art methods for alignment of consecutive slides and shows that, despite being simpler, it provides similar accuracy and is more robust. We also demonstrate how the automatically generated masks can be used to train modern AI image segmentation based on U-Net, resulting in reliable detection of epithelial regions in previously unseen H&E slides. Through training on real-world material available in clinical laboratories, this approach therefore has widespread applications toward achieving AI-assisted tumor assessment directly from scanned H&E sections. In addition, the re-staining method will facilitate additional automated quantitative studies of tumor cell and stromal cell phenotypes.
Implementing Content Erasure in IPFS

The InterPlanetary File System (IPFS) is employed extensively nowadays by many blockchain projects to store personal data off-chain in order to comply with the Right to be Forgotten (RtbF) provision of the General Data Protection Regulation (GDPR). Nevertheless, upon an erasure request under the RtbF, the onus of removing the actual personal information moves to the IPFS protocol which—due to its decentralized nature—cannot guarantee data erasure across its entire network. Against this background, in this chapter we formalize a secure and anonymous protocol for delegated content erasure requests in the IPFS. The protocol can be easily integrated into the IPFS to disseminate an erasure request among all of its nodes and, eventually, to meet the RtbF requirements. Furthermore, the protocol complies with the primary principle of the IPFS to prevent censoring since the erasure is only allowed to the original content providers or their delegates. We demonstrate the efficacy of the proposed protocol by providing security proofs and experiments which confirm that the overhead introduced does not affect the overall performance of the IPFS.
Hybrid Filter-Wrapper Text Feature Selection Technique for Text Classification

Feature selection is one of the most common and vital data preprocessing techniques in text classification. It is used to reduce the high-dimensional set of features in any dataset by eliminating irrelevant, redundant, and noisy features that are unnecessary from the classification point of view, since it makes it difficult for classifiers or machine learning techniques to produce correct classification results. This research work has accomplished a twofold objective to achieve these goals. Firstly, it aimed to select top-N features with the highest-ranking features using the Information Gain (IG) filter approach. Secondly, it intended to reduce the text feature set obtained by IG using the gray wolf optimizer (GWO) search strategies in the wrapper approach to finding the most informative subset of text features. The Naive Bayes (NB) classifier was introduced to evaluate the performance of this proposed method. Experimentations were carried out on nine benchmark document datasets. Based on the evaluation measures, the results show that the IG-GWO could be used as an alternative method to address the text FS problem compared to the state-of-the-art feature selection algorithms.
Attendance System based on Face Recognition, Face Mask and Body Temperature Detection on Raspberry Pi

The world is currently experiencing a COVID-19 pandemic which has caused many deaths. Then, one of COVID-19 early detection can be done through wearing a face mask and detecting body temperature when entering a room. The purpose of this research is to create an attendance system capable to recognize face, face mask, and measure body temperature. The data was tested against three types of Raspberry Pi (Pi 3B, Pi 4-4Gb, and Pi 4-8Gb). Each type of Raspberry Pi was tested using three metric combinations of different encoding methods and object classification, namely Method 1 - Haar Cascade and LBPH, Method 2 - Haar Cascade, and Tensorflow, Method 3 - MTCNN and Tensorflow. The test results obtained Method 1: FPS 15, accuracy rate 60%, CPU temperature 58°C, Method 2: FPS 4, accuracy rate 90%, CPU temperature 65°C, Method 3: FPS 2, accuracy rate 95%, CPU temperature 68°C. Based on the benchmarks and scoring, it can be concluded that the most optimal attendance system based on face recognition, mask, and body temperature detection is a system with a Raspberry Pi 4-4Gb and Method 1-Haar Cascade and LBPH. For next development, this attendance system needs to be further improved in security and accuracy without increasing CPU load.
Single View Facial Age Estimation Using Deep Learning with Cascaded Random Forests

The task of estimating a person’s real age using unconstrained facial images has been actively studied in biometrics research. We developed several deep learning architectures and supervision methods for facial age estimation and evaluate the impact of different pre-processing and face alignment (or normalization) methods on the feature embedding subspace. The proposed novel two-stage supervised learning model utilizes ResNeXt as a backbone combined with a two-layer random forest (TLRF) to estimate age. Our deep architectures are trained using a custom loss function to handle variations in gender, pose, illumination, ethnicity, expression and context, on the VGG-Face2 MIVIA Age Dataset with over 575K images, as part of the Guess the Age (GTA) contest. Surprisingly, face alignment using FANet during training did not improve accuracy. We were able to achieve an Age Accuracy and Regularity score \(AAR\,=\,7.02\) with a variance \(\sigma \,=\,1.16\) using only ResNeXt. The proposed ResNeXt+TLRF model improved age-class generalizability with a smaller variance of \(\sigma =0.98\) and a second best \(AAR\,=\,6.97\).
Separation of Powers in Federated Learning (Poster Paper)

In federated learning (FL), model updates from mutually distrusting parties are aggregated in a centralized fusion server. The concentration of model updates simplifies FL's model building process, but might lead to unforeseeable information leakage. This problem has become acute due to recent FL attacks that can reconstruct large fractions of training data from ostensibly "sanitized" model updates.
Deploying augmented reality to help engage the public with palaeontological applications

Augmented reality (AR) has huge potential as a science outreach tool, especially around palaeontology where it is possible to bring extinct animals to life. This paper shares our experiences as academic geoscientists in developing a series of AR applications during a three-year period. We do not focus on the technical issues of app design and coding but on the lessons learnt during this time which may be of value to others, whether academics, event coordinators or museum curators interested in using digital technology to enhance user experience or convey educational messages. We identify eight issues that are worth keeping in mind when commissioning, developing, and deploying AR apps for use as a science communication tool. The issues are: (1) download barriers; (2) ageing novelty of AR; (3) the 'stickiness' of an app tends to be moment-locations specific; (4) maintenance of bespoke apps; (5) 'boom and lapse' cycles in funding; (6) difficulty of embedding educational content; (7) priority conflict between users and event organisers; and (8) the quality of the animation is key.