Reviewerly/exHarmony huggingface dataset:
Synthesis of Compositional Animations from Textual Descriptions

"How can we animate 3D-characters from a movie script or move robots by simply telling them what we would like them to do?" "How unstructured and complex can we make a sentence and still generate plausible movements from it?" These are questions that need to be answered in the long-run, as the field is still in its infancy. Inspired by these problems, we present a new technique for generating compositional actions, which handles complex input sentences. Our output is a 3D pose sequence depicting the actions in the input sentence. We propose a hierarchical two-stream sequential model to explore a finer joint-level mapping between natural language sentences and 3D pose sequences corresponding to the given motion. We learn two manifold representations of the motion, one each for the upper body and the lower body movements. Our model can generate plausible pose sequences for short sentences describing single actions as well as long complex sentences describing multiple sequential and compositional actions. We evaluate our proposed model on the publicly available KIT Motion-Language Dataset containing 3D pose data with human-annotated sentences. Experimental results show that our model advances the state-of-the-art on text-based motion synthesis in objective evaluations by a margin of 50%. Qualitative evaluations based on a user study indicate that our synthesized motions are perceived to be the closest to the ground-truth motion captures for both short and compositional sentences.
Mineral Physicochemistry Underlying Feature-Based Extraction of Mineral Abundance and Composition from Shortwave, Mid and Thermal Infrared Reflectance Spectra

Reflectance spectroscopy allows cost-effective and rapid mineral characterisation, addressing mineral exploration and mining challenges. Shortwave (SWIR), mid (MIR) and thermal (TIR) infrared reflectance spectra are collected in a wide range of environments and scales, with instrumentation ranging from spaceborne, airborne, field and drill core sensors to IR microscopy. However, interpretation of reflectance spectra is, due to the abundance of potential vibrational modes in mineral assemblages, non-trivial and requires a thorough understanding of the potential factors contributing to the reflectance spectra. In order to close the gap between understanding mineral-diagnostic absorption features and efficient interpretation of reflectance spectra, an up-to-date overview of major vibrational modes of rock-forming minerals in the SWIR, MIR and TIR is provided. A series of scripts are proposed that allow the extraction of the relative intensity or wavelength position of single absorption and other mineral-diagnostic features. Binary discrimination diagrams can assist in rapidly evaluating mineral assemblages, and relative abundance and chemical composition of key vector minerals, in hydrothermal ore deposits. The aim of this contribution is to make geologically relevant information more easily extractable from reflectance spectra, enabling the mineral resources and geoscience communities to realise the full potential of hyperspectral sensing technologies.
Blockchain-Based Access Control Scheme for Secure Shared Personal Health Records over Decentralised Storage

Blockchain technology provides a tremendous opportunity to transform current personal health record (PHR) systems into a decentralised network infrastructure. However, such technology possesses some drawbacks, such as issues in privacy and storage capacity. Given its transparency and decentralised features, medical data are visible to everyone on the network and are inappropriate for certain medical applications. By contrast, storing vast medical data, such as patient medical history, laboratory tests, X-rays, and MRIs, significantly affect the repository storage of blockchain. This study bridges the gap between PHRs and blockchain technology by offloading the vast medical data into the InterPlanetary File System (IPFS) storage and establishing an enforced cryptographic authorisation and access control scheme for outsourced encrypted medical data. The access control scheme is constructed on the basis of the new lightweight cryptographic concept named smart contract-based attribute-based searchable encryption (SC-ABSE). This newly cryptographic primitive is developed by extending ciphertext-policy attribute-based encryption (CP-ABE) and searchable symmetric encryption (SSE) and by leveraging the technology of smart contracts to achieve the following: (1) efficient and secure fine-grained access control of outsourced encrypted data, (2) confidentiality of data by eliminating trusted private key generators, and (3) multikeyword searchable mechanism. Based on decisional bilinear Diffie–Hellman hardness assumptions (DBDH) and discrete logarithm (DL) problems, the rigorous security indistinguishability analysis indicates that SC-ABSE is secure against the chosen-keyword attack (CKA) and keyword secrecy (KS) in the standard model. In addition, user collusion attacks are prevented, and the tamper-proof resistance of data is ensured. Furthermore, security validation is verified by simulating a formal verification scenario using Automated Validation of Internet Security Protocols and Applications (AVISPA), thereby unveiling that SC-ABSE is resistant to man-in-the-middle (MIM) and replay attacks. The experimental analysis utilised real-world datasets to demonstrate the efficiency and utility of SC-ABSE in terms of computation overhead, storage cost and communication overhead. The proposed scheme is also designed and developed to evaluate throughput and latency transactions using a standard benchmark tool known as Caliper. Lastly, simulation results show that SC-ABSE has high throughput and low latency, with an ultimate increase in network life compared with traditional healthcare systems.
Joint Optimization of Communications and Federated Learning Over the Air

Federated learning (FL) is an attractive paradigm for making use of rich distributed data while protecting data privacy. Nonetheless, nonideal communication links and limited transmission resources may hinder the implementation of fast and accurate FL. In this paper, we study joint optimization of communications and FL based on analog aggregation transmission in realistic wireless networks. We first derive closed-form expressions for the expected convergence rate of FL over the air, which theoretically quantify the impact of analog aggregation on FL. Based on the analytical results, we develop a joint optimization model for accurate FL implementation, which allows a parameter server to select a subset of workers and determine an appropriate power scaling factor. Since the practical setting of FL over the air encounters unobservable parameters, we reformulate the joint optimization of worker selection and power allocation using controlled approximation. Finally, we efficiently solve the resulting mixed-integer programming problem via a simple yet optimal finite-set search method by reducing the search space. Simulation results show that the proposed solutions developed for realistic wireless analog channels outperform a benchmark method, and achieve comparable performance of the ideal case where FL is implemented over noise-free wireless channels.
Category-based fractal modelling: A novel model to integrate the geology into the data for more effective processing and interpretation

Parent lithology exerts a major influence on the associated regolith geochemistry and affects the setting of population or domain boundaries. In regional geochemical mapping and anomaly detection, a lack of samples pertaining to one or more key lithologies may results in interpolation errors. Such errors can be addressed via category-based fractal modelling to characterise different background and anomalous populations by taking both error propagation and geochemical data values into account. Such geochemical anomaly classification models have been developed using Monte Carlo simulation (MCSIM) within a given lithological domain to provide estimates of population breaks. Two types of category-based fractal models used are (i) related lithological groups representative samples where there is an adequate number of samples derived from a given lithological or geological domain, and (ii) related lithological groups whole samples and a group of Gaussian simulated samples regenerated based on the mean and standard deviation of the available samples per lithological group. Both models were applied to VMS-related element concentrations in the low-density till geochemical mapping of Sweden following centred log-ratio (clr) transformation. Compared with the concentration-area fractal model the category-based fractal models provided more reliable estimates for population thresholds and identification of anomalous classes.
PetroBlock: A Blockchain-Based Payment Mechanism for Fueling Smart Vehicles

Current developments in information technology and increased inclination towards smart cities have led to the initiation of a plethora of features by technology-oriented companies (i.e., car manufacturers) to improve users’ privacy and comfort. The invention of smart vehicle technology paved the way for the excessive use of machine-to-machine technologies. Moreover, third-party sharing of financial services are also introduced that support machine-to-machine (M2M) communication. These monetary systems’ prime focus is on improving reliability and security; however, they overlook aspects like behaviors and users’ need. For instance, people often hand over their bank cards or share their credentials with their colleagues to withdraw money on their behalf. Such behaviors may originate issues about privacy and security that can have severe losses for the card owner. This paper presents a novel blockchain-based strategy for payment of fueling of smart cars without any human interaction while maintaining transparency, privacy, and trust. The proposed system is capable of data sharing among the users of the system while securing sensitive information. Moreover, we also provide a blockchain-based secure privacy-preserving strategy for payment of fueling among the fuel seller and buyer without human intervention. Furthermore, we have also analytically evaluated several experiments to determine the proposed blockchain platform’s usability and efficiency. Lastly, we harness Hyperledger Caliper to assess the proposed system’s performance in terms of transaction latency, transactions per second, and resource consumption.
Image Enhancement Driven by Object Characteristics and Dense Feature Reuse Network for Ship Target Detection in Remote Sensing Imagery

As the application scenarios of remote sensing imagery (RSI) become richer, the task of ship detection from an overhead perspective is of great significance. Compared with traditional methods, the use of deep learning ideas has more prospects. However, the Convolutional Neural Network (CNN) has poor resistance to sample differences in detection tasks, and the huge differences in the image environment, background, and quality of RSIs affect the performance for target detection tasks; on the other hand, upsampling or pooling operations result in the loss of detailed information in the features, and the CNN with outstanding results are often accompanied by a high computation and a large amount of memory storage. Considering the characteristics of ship targets in RSIs, this study proposes a detection framework combining an image enhancement module with a dense feature reuse module: (1) drawing on the ideas of the generative adversarial network (GAN), we designed an image enhancement module driven by object characteristics, which improves the quality of the ship target in the images while augmenting the training set; (2) the intensive feature extraction module was designed to integrate low-level location information and high-level semantic information of different resolutions while minimizing the computation, which can improve the efficiency of feature reuse in the network; (3) we introduced the receptive field expansion module to obtain a wider range of deep semantic information and enhance the ability to extract features of targets were at different sizes. Experiments were carried out on two types of ship datasets, optical RSI and Synthetic Aperture Radar (SAR) images. The proposed framework was implemented on classic detection networks such as You Only Look Once (YOLO) and Mask-RCNN. The experimental results verify the effectiveness of the proposed method.
Simpler is better: Lifting interpretability-performance trade-off via automated feature engineering

Machine learning has proved to generate useful predictive models that can and should support decision makers in many areas. The availability of tools for AutoML makes it possible to quickly create an effective but complex predictive model. However, the complexity of such models is often a major obstacle in applications, especially in terms of high-stake decisions. We are experiencing a growing number of examples where the use of black boxes leads to decisions that are harmful, unfair or simply wrong. In this paper, we show that very often we can simplify complex models without compromising their performance; however, with the benefit of much needed transparency. We propose a framework that uses elastic black boxes as supervisor models to create simpler, less opaque, yet still accurate and interpretable glass box models. The new models were created using newly engineered features extracted with the help of a supervisor model. We supply the analysis using a large-scale benchmark on several tabular data sets from the OpenML database. There are tree main results of this paper: 1) we show that extracting information from complex models may improve the performance of simpler models, 2) we question a common myth that complex predictive models outperform simpler predictive models, 3) we present a real-life application of the proposed method.
Integrating Blockchain With Artificial Intelligence for Privacy-Preserving Recommender Systems

Data privacy is one of the intriguing problems in e-commerce site. For personal or business purposes, users have to disclose their private data to these e-commerce sites. Often such businesses use these highly sensitive data for computing artificial intelligence-driven analyses like recommendation generation without user consent. In the case of recommendation generation, data need to be analyzed at the business platforms. An automated personalization, based on artificial intelligence, on a list of products with respect to user interest is generated by a recommender system. However, the secure utilization of user data is absent in such systems. This paper proposes Private-Rec, a privacy-preserving platform for a recommendation system through the integration of artificial intelligence and blockchain. In Private-Rec, blockchain gives the user a secure environment through the distributed attribute in which data can be used with the required permission. Under this platform, users receive incentives (i.e., point, discount) from the recommended company for sharing their data to be used for computing recommendations. The Private-Rec platform has been studied empirically.
Survey on 3D face reconstruction from uncalibrated images

Recently, a lot of attention has been focused on the incorporation of 3D data into face analysis and its applications. Despite providing a more accurate representation of the face, 3D facial images are more complex to acquire than 2D pictures. As a consequence, great effort has been invested in developing systems that reconstruct 3D faces from an uncalibrated 2D image. However, the 3D-from-2D face reconstruction problem is ill-posed, thus prior knowledge is needed to restrict the solutions space. In this work, we review 3D face reconstruction methods proposed in the last decade, focusing on those that only use 2D pictures captured under uncontrolled conditions. We present a classification of the proposed methods based on the technique used to add prior knowledge, considering three main strategies, namely, statistical model fitting, photometry, and deep learning, and reviewing each of them separately. In addition, given the relevance of statistical 3D facial models as prior knowledge, we explain the construction procedure and provide a list of the most popular publicly available 3D facial models. After the exhaustive study of 3D-from-2D face reconstruction approaches, we observe that the deep learning strategy is rapidly growing since the last few years, becoming the standard choice in replacement of the widespread statistical model fitting. Unlike the other two strategies, photometry-based methods have decreased in number due to the need for strong underlying assumptions that limit the quality of their reconstructions compared to statistical model fitting and deep learning methods. The review also identifies current challenges and suggests avenues for future research.
Assessment of new solar radiation nowcasting methods based on sky-camera and satellite imagery

This work proposes and evaluates methods for extending the forecasting horizon of all-sky imager (ASI)-based solar radiation nowcasts and estimating the uncertainty of these predictions. In addition, we evaluated procedures for improving the temporal resolution and latency of satellite-imagery-derived solar nowcasts. Based on these contributions, we assessed the reliability of ASIs and satellite-derived solar radiation nowcasts, with 1-min time-resolution and up-to-90-min ahead. The study was conducted in a location in Southern Spain using a set of cloudy days, specifically selected as representative of the most challenging conditions regarding solar radiation nowcasting. The results reveal that the use of ASI-based models provide low benefits compared to the use of satellite-based models for point solar radiation nowcasting. Given the frequency of occurrence of the different sky types in the study area, the results suggest that the use of a simple smart persistence algorithm, in combination with a low-resolution satellite nowcasting model could be an adequate choice, avoiding the challenges associated with the use of ASIs.
Speculative interference attacks: breaking invisible speculation schemes

Recent security vulnerabilities that target speculative execution (e.g., Spectre) present a significant challenge for processor design. These highly publicized vulnerabilities use speculative execution to learn victim secrets by changing the cache state. As a result, recent computer architecture research has focused on invisible speculation mechanisms that attempt to block changes in cache state due to speculative execution. Prior work has shown significant success in preventing Spectre and other attacks at modest performance costs. In this paper, we introduce speculative interference attacks, which show that prior invisible speculation mechanisms do not fully block speculation-based attacks that use cache state. We make two key observations. First, mis-speculated younger instructions can change the timing of older, bound-to-retire instructions, including memory operations. Second, changing the timing of a memory operation can change the order of that memory operation relative to other memory operations, resulting in persistent changes to the cache state. Using both of these observations, we demonstrate (among other attack variants) that secret information accessed by mis-speculated instructions can change the order of bound-to-retire loads. Load timing changes can therefore leave secret-dependent changes in the cache, even in the presence of invisible speculation mechanisms. We show that this problem is not easy to fix. Speculative interference converts timing changes to persistent cache-state changes, and timing is typically ignored by many cache-based defenses. We develop a framework to understand the attack and demonstrate concrete proof-of-concept attacks against invisible speculation mechanisms. We conclude with a discussion of security definitions that are sufficient to block the attacks, along with preliminary defense ideas based on those definitions.
A Novel Optimized Graph-Based Transform Watermarking Technique to Address Security Issues in Real-Time Application

The multimedia technologies are gaining a lot of popularity these days. Many unauthorized persons are gaining the access of multimedia such as videos, audios, and images. The transmission of multimedia across the Internet by unauthorized person has led to the problem of illegal distribution. The problem arises when copyrighted data is getting accessed without the knowledge of copyright owner. The videos are the most attacked data during COVID-19 pandemic. In this paper, the frame selection video watermarking technique is proposed to tackle the issue. The proposed work enlightens frame selection followed by watermarking embedding and testing of the technique against various attacks. The embedding of the watermark is done on selected frames of the video. The additional security feature Hyperchaotic Encryption is applied on watermark before embedding. Watermark embedding is done using graph-based transform and singular-valued decomposition and the performance of the technique is further optimized using hybrid combination of grey wolf optimization and genetic algorithm. Many researchers face the challenge of quality loss after embedding of watermark. Proposed technique will aim to overcome those challenges. A total of 6 videos (Akiyo, Coastguard, Foreman, News, Bowing, and Pure Storage) are used for carrying out research work. The performance evaluation of the proposed technique has been carried out after processing it against practical video processing attacks Gaussian noise, sharpening, rotation, blurring, and JPEG compression.
DewaterNet: A fusion adversarial real underwater image enhancement network

Underwater image enhancement algorithms have attracted much attention in underwater vision task. However, these algorithms are mainly evaluated on different datasets and metrics. In this paper, we utilize an effective and public underwater benchmark dataset including diverse underwater degradation scenes to enlarge the test scale and propose a fusion adversarial network for enhancing real underwater images. Meanwhile, the multiple inputs and well-designed multi-term adversarial loss can not only introduce multiple input image features, but also balance the impact of multi-term loss functions. The proposed network tested on the benchmark dataset achieves better or comparable performance than the other state-of-the-art methods in terms of qualitative and quantitative evaluations. Moreover, the ablation study experimentally validates the contributions of each component and hyper-parameter setting of loss functions.
Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP

Transfer learning, particularly approaches that combine multi-task learning with pre-trained contextualized embeddings and fine-tuning, have advanced the field of Natural Language Processing tremendously in recent years.In this paper we present MACHAMP, a toolkit for easy fine-tuning of contextualized embeddings in multi-task settings.The benefits of MACHAMP are its flexible configuration options, and the support of a variety of natural language processing tasks in a uniform toolkit, from text classification and sequence labeling to dependency parsing, masked language modeling, and text generation. 1