Reviewerly/exHarmony huggingface dataset:
PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs

Deep CNNs, though have achieved the state of the art performance in image classification tasks, remain a black-box to a human using them. There is a growing interest in explaining the working of these deep models to improve their trustworthiness. In this paper, we introduce a Posthoc Architecture-agnostic Concept Extractor (PACE) that automatically extracts smaller sub-regions of the image called concepts relevant to the black-box prediction. PACE tightly integrates the faithfulness of the explanatory framework to the black-box model. To the best of our knowledge, this is the first work that extracts class-specific discriminative concepts in a posthoc manner automatically. The PACE framework is used to generate explanations for two different CNN architectures trained for classifying the AWA2 and Imagenet- Birds datasets. Extensive human subject experiments are conducted to validate the human interpretability and consistency of the explanations extracted by PACE. The results from these experiments suggest that over 72% of the concepts extracted by PACE are human interpretable.
Improving Object Detection in Art Images Using Only Style Transfer

Despite recent advances in object detection using deep learning neural networks, these neural networks still struggle to identify objects in art images such as paintings and drawings. This challenge is known as the cross depiction problem and it stems in part from the tendency of neural networks to prioritize identification of an object's texture over its shape. In this paper we propose and evaluate a process for training neural networks to localize objects — specifically people — in art images. We generate a large dataset for training and validation by modifying the images in the COCO dataset using AdaIn style transfer. This dataset is used to fine-tune a Faster R-CNN object detection network, which is then tested on the existing People-Art testing dataset. The result is a significant improvement on the state of the art and a new way forward for creating datasets to train neural networks to process art images.
A Privacy-Preserving Blockchain Supervision Framework in the Multiparty Setting

Data supervision is an effective method to ensure the legality of user data on blockchain. However, the massive growth of data makes it difficult to achieve data supervision in existing blockchain applications. Also, data supervision often leads to problems such as disclosure of transaction data and user privacy information. To address these issues, this paper proposes a privacy-preserving blockchain supervision system (BSS) in the multiparty setting, where a supervision chain is introduced to realize data supervision on blockchain. All sensitive information such as user information in the supervising data is encrypted by the attribute-based encryption (ABE) technology, so that both privacy protection and access control on user data can be achieved. Theoretical analysis and comparison show that the proposed BSS scheme is efficient, and experimental analysis indicates the practicality of our BSS scheme.
A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation

This paper addresses weakly supervised amodal instance segmentation, where the goal is to segment both visible and occluded (amodal) object parts, while training provides only ground-truth visible (modal) segmentations. Following prior work, we use data manipulation to generate occlusions in training images and thus train a segmenter to predict amodal segmentations of the manipulated data. The resulting predictions on training images are taken as the pseudo-ground truth for the standard training of Mask-RCNN, which we use for amodal instance segmentation of test images. For generating the pseudo-ground truth, we specify a new Amodal Segmenter based on Boundary Uncertainty estimation (ASBU) and make two contributions. First, while prior work uses the occluder’s mask, our ASBU uses the occlusion boundary as input. Second, ASBU estimates an uncertainty map of the prediction. The estimated uncertainty regularizes learning such that lower segmentation loss is incurred on regions with high uncertainty. ASBU achieves significant performance improvement relative to the state of the art on the COCOA and KINS datasets in three tasks: amodal instance segmentation, amodal completion, and ordering recovery.
Action recognition for educational proposals applying concepts of Social Assistive Robotics

Action recognition has been gaining interest in research due to its great number of applications. So, the main contribution of this manuscript is a human–robot interaction framework, relying on dimension reduction of the system's inputs in order to require a smaller dataset for training of an Artificial Neural Network. Our motivation is the development of a Social Assistive Robotics application. In summary, we choose nine standard actions to guide a robot, and two neutral ones to represent stand-by or resting cases. The dataset is created by people with different body shape, for robustness purposes, using only 5 to 10 samples of each class per person. Offline and online tests validate the method's accuracy and confusion matrices clarify the results. A TicTacToe game using a ground robot exemplify a real world application, where each action represents a desired spot in the game. The results confirm a high accuracy, above 96.7%, in all the tests. Based on this, we can conclude our preprocessing strategy and classifier are capable of identifying the action patterns, even for a tiny dataset; thus, it is recommended for educational proposals due to its simplicity.
Intelligent Trigonometric Particle Filter for visual tracking

Visual tracking is one of the pre-eminent tasks in several computer vision applications. Particle filter (PF) is extensively used in visual tracking for intelligent surveillance system applications, hugely significant. But the re-sampling procedure of PF will result in sample impoverishment, which will affect the precision of tracking simultaneously. In this paper, a new tracking technique, called Trigonometric Particle Filter (TPF), based on PF optimized by Sine Cosine Algorithm (SCA), which contains trigonometric sine and cosine functions, is proposed. An enhanced method for improving the number of target particles used in a Sine Cosine Algorithm for trigonometric particle filter includes SCA ahead of the re-sampling step. This step ensures a more extensive particle set Achievement of the proposed TPF tracker is inspected and assessed on Visual Tracker Benchmark (VOT) databases. The proposed TPF tracker is compared with evolutionary-based methods like the Spider monkey optimization assisted PF (SMO-PF), Firefly algorithm-based PF (FAPF) method, Particle swarm optimization-based PF (PSO-PF) and Particle filter, recent four correlation filter-based trackers, and also with other ten state-of-the-art tracking methods. We demonstrate that visual tracking using TPF delivers additional consistent and proficient tracking outcomes than compared trackers.
Raman Spectroscopy Coupled with Reflectance Spectroscopy as a Tool for the Characterization of Key Hydrothermal Alteration Minerals in Epithermal Au–Ag Systems: Utility and Implications for Mineral Exploration

Raman spectroscopy of fine-grained hydrothermal alteration minerals, and phyllosilicates in particular, presents certain challenges. However, given the increasingly widespread recognition of field portable visible–near infrared–shortwave infrared (Vis-NIR-SWIR) spectroscopy as a valuable tool in the mineral exploration industry, Raman microspectroscopy has promise as an approach for developing detailed complementary information on hydrothermal alteration phases in ore-forming systems. Here we present exemplar high-quality Raman and Vis-NIR-SWIR spectra of four key hydrothermal alteration minerals (pyrophyllite, white mica, chlorite, and alunite) that are common in precious metal epithermal systems, from deposits on the island of Newfoundland, Canada. The results reported here demonstrate that Raman microspectroscopy can accurately characterize pyrophyllite, white mica, chlorite, and alunite and provide details on their compositional variation at the microscale. In particular, spectral differences in the 1000–1150 cm −1 white mica Raman band allows the distinction between low-Tschermak phases (muscovite, paragonite) and phases with higher degrees of Tschermak substitution (phengitic white mica composition). The peak position of the main chlorite Raman band shifts between 683 cm −1 for Mg-rich chlorite and 665 cm −1 for Fe-rich chlorite and can be therefore used for semiquantitative estimation of the Fe 2+ content in chlorite. Furthermore, while Vis-NIR-SWIR macrospectroscopy allows the rapid identification of the overall composition of the most abundant hydrothermal alteration mineral in a given sample, Raman microspectroscopy provides an in-depth spectral and chemical characterization of individual mineral grains, preserving the spatial and paragenetic context of each mineral and allowing for the distinction of chemical variation between (and within) different mineral grains. This is particularly useful in the case of alunite, white mica, and chlorite, minerals with extensive solid solution, where microscale characterization can provide information on the alteration zonation useful for mineral exploration and provide insight into mineral deposit genesis.
LED source interferometer for microscopic fringe projection profilometry using a Gates’ interferometer configuration

The fringe projection technique is considered one of the most reliable solutions for structured light technology, developed for non-contact three-dimensional (3D) data acquisition. When working with this technique, applying it on a microscopic scale, one often encounters some problems such as the limited depth of field of the projected fringe pattern, typical of the imaging lenses, or the formation of speckle-noise when laser interferometric fringes are projected, which are problems that can cause significant errors on test surfaces whose height exceeds the depth of field, and on which, the other hand, speckle noise affects the resolution of the technique. This paper presents a Gates’ interferometer configuration with a LED source to project a fringe pattern without speckle noise and a very long field depth. We explain how a non-located sinusoidal fringe pattern can be obtained using two-beamsplitter cubes and a LED light source. Moreover, we compare measurements made using the LED and laser concerning a commercial contact profilometer.
Review on spatial domain image steganography techniques

Steganography techniques leads main role in embedding process of secret information into cover information in order to provide good security. These techniques are very essential, when sending the confidential information from one place to another place in the field of defense and medical. Hence, it is a challenging and important task to hide the secret information in to cover information. This review provides classification of steganography techniques, existing and proposed techniques with advantages and disadvantages. As per observations of existing methods, much amount of information hides using spatial domain techniques than transform domain techniques. This review mainly focuses on the spatial domain image steganography techniques.
A blockchain-based trust management framework with verifiable interactions

There has been tremendous interest in the development of formal trust models and metrics through the use of analytics (e.g., Belief Theory and Bayesian models), logics (e.g., Epistemic and Subjective Logic) and other mathematical models. The choice of trust metric will depend on context, circumstance and user requirements and there is no single best metric for use in all circumstances. Where different users require different trust metrics to be employed the trust score calculations should still be based on all available trust evidence. Trust is normally computed using past experiences but, in practice (especially in centralised systems), the validity and accuracy of these experiences are taken for granted. In this paper, we provide a formal framework and practical blockchain-based implementation that allows independent trust providers to implement different trust metrics in a distributed manner while still allowing all trust providers to base their calculations on a common set of trust evidence. Further, our design allows experiences to be provably linked to interactions without the need for a central authority. This leads to the notion of evidence-based trust with provable interactions. Leveraging blockchain allows the trust providers to offer their services in a competitive manner, charging fees while users are provided with payments for recording experiences. Performance details of the blockchain implementation are provided.
Large-Scale ASR Domain Adaptation Using Self- and Semi-Supervised Learning

Self- and semi-supervised learning methods have been actively investigated to reduce labeled training data or enhance model performance. However, these approaches mostly focus on in-domain performance for public datasets. In this study, we utilize the combination of self- and semi-supervised learning methods to solve unseen domain adaptation problems in a large-scale production setting for online ASR model. This approach demonstrates that using the source domain data with a small fraction of the target domain data (3%) can recover the performance gap compared to a full data baseline: 13.5% relative WER improvement for target domain data.
Anomaly Detection Based on Selection and Weighting in Latent Space

With the high requirements of automation in the era of Industry 4.0, anomaly detection plays an increasingly important role in high safety and reliability in the production and manufacturing industry. Recently, autoencoders have been widely used as a backend algorithm for anomaly detection. Different techniques have been developed to improve the anomaly detection performance of autoencoders. Nonetheless, little attention has been paid to the latent representations learned by autoencoders. In this paper, we propose a novel selection-and-weighting-based anomaly detection framework called SWAD. In particular, the learned latent representations are individually selected and weighted. Experiments on both benchmark and real-world datasets have shown the effectiveness and superiority of SWAD. On the benchmark datasets, the SWAD framework has reached comparable or even better performance than the state-of-the-art approaches.
A Modified Jellyfish Search Optimizer With Orthogonal Learning Strategy

The jellyfish search optimizer (JSO) is one of the newest swarm intelligence algorithms which has been widely used to solve different real-world optimization problems. However, its most challenging task is to regulate the exploration and exploitation search to avoid problems in harmonic convergence or be trapped into local optima. In this paper, we propose a new variant of JSO named OJSO, based on orthogonal learning with the aim to improve the capability of global searching of the original algorithm. The orthogonal learning is a strategy for discovering more useful information from two recent solution vectors by predicting the best combination using limited trials instead of exhaustive trials via an orthogonal experimental design. To evaluate the effectiveness of our approach, 23 benchmark functions are used. The evaluation process leads us to conclude that the proposed algorithm strongly outperforms the original algorithm in all aspects except the execution time.
Alternative Tower Field Construction for Quantum Implementation of the AES S-Box

Grover’s search algorithm allows a quantum adversary to find a <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -bit secret key of a block cipher by making O( <inline-formula><tex-math notation="LaTeX">$2^{k/2}$</tex-math></inline-formula> ) block cipher queries. Resistance of a block cipher to such an attack is evaluated by quantum resources required to implement Grover’s oracle for the target cipher. The quantum resources are typically estimated by the <inline-formula><tex-math notation="LaTeX">$\textit {T}$</tex-math></inline-formula> -depth of its circuit implementation and the number of qubits used by the circuit (width). Since the AES S-box is the only component which requires <inline-formula><tex-math notation="LaTeX">$\textit {T}$</tex-math></inline-formula> -gates in a quantum implementation of AES, recent research has put its focus on efficient implementation of the AES S-box. However, any efficient implementation with low <inline-formula><tex-math notation="LaTeX">$\textit {T}$</tex-math></inline-formula> -depth will not be practical in the real world without considering qubit consumption of the implementation. In this work, we propose three methods of trade-off between time and space for the quantum implementation of the AES S-box. In particular, one of our methods turns out to use the smallest number of qubits among the existing methods, significantly reducing its <inline-formula><tex-math notation="LaTeX">$\textit {T}$</tex-math></inline-formula> -depth.
Product Recommendation System for E-Commerce using Collaborative Filtering and Textual Clustering

In the past decade internet usage has seen an exponential growth. This growth has given opportunity for other businesses which rely on it to grow and prosper too. One such avenue is E-commerce. E-commerce is growing rapidly and with increase in popularity of E-Commerce, Recommendation has become very important too. Recommendation means providing relevant suggestions to the user as per his/her interest and need. Recommendations to users can be given based on price, area of living, wish listed items, cart items, searched items and previous purchased items. Recommendation systems improve user experience, boost sales and enhance user's engagement. Our proposed recommendation system will recommend products both for new users and existing users. This recommendation system uses model based collaborative filtering and recommends products based on rating and previous purchase history of the old users. Also the new users will get recommendations of new products, trending products and products on sale. Existing users will get recommendations based on recently viewed products, complementary products, etc. Since we're setting up a new e-commerce website, initially there are no user ratings for different products, so in this case, recommendations are made based on the textual clustering analysis of product description. Model based Collaborative Filtering along with Textual Clustering will help us in improving accuracy and targeting all types of users. E Commerce is gaining popularity and the recommendation system with E Commerce is like an icing on the cake.
Evaluating Reasoning in Natural Arguments: A Procedural Approach

Abstract In this paper, we formulate a procedure for assessing reasoning as it is expressed in natural arguments. The procedure is a specification of one of the three aspects of argumentation assessment distinguished in the Comprehensive Assessment Procedure for Natural Argumentation (CAPNA) (Hinton, 2021) that makes use of the argument categorisation framework of the Periodic Table of Arguments (PTA) (Wagemans, 2016, 2019, 2020c). The theoretical framework and practical application of both the CAPNA and the PTA are described, as well as the evaluation procedure that combines the two. The procedure is illustrated through an evaluation of the reasoning of two example arguments from a recently published text.
Emoji in social media discourse about working from home

This paper applies a social semiotic framework for exploring the functions of emoji in digital discourse about working from home (WFH). This is an important and prevalent discourse in the on-going COVID-19 pandemic due to widespread ‘lockdowns’ aimed at reducing the spread of the virus which have had a profound impact upon how and where people work. The paper focuses in particular on ideational meaning, that is, how experience is construed, exploring how people use language and emoji to express ideational meanings about their daily lives in a large corpus of tweets about WFH. This exploration involves corpus-based discourse analysis focused on how language and emoji coordinate to make meaning, drawing on the concept of intermodal coupling to understand the convergence of meaning across semiotic modes. The paper also considers the role of visual images in this coordination.
IMU-Based Estimation of Lower Limb Motion Trajectory With Graph Convolution Network

In recent years, the motion capture system has received a lot of attention because of its wide application, such as movie animation, sport and medical applications. In the field of rehabilitation, motion capture systems are often used to collect the motion information of the patient when he/she is performing rehabilitation tasks. It can be used to quantify the patient’s rehabilitation effectiveness and provide the physician with more objective data as a reference. Using inertial sensors is one of motion capture methods. However, the major challenge to reconstruct accurate human posture with sensor measurements are signal noise, bias and inaccurate gyroscope estimation during long-term wearing. Based on the reason mentioned above, this paper proposes a graph convolutional network (GCN)-based deep learning architecture that uses effective information collected by inertial sensors to predict a sequence of position of each joint of a human’s lower limbs in the 3D space throughout the entire motion. Such motion prediction result based on the deep learning model is to reduce the joint’s tracking error relying only on direct calculations based on measurements of inertial sensors. In the final experiment, the lower limb motion trajectory during walking is used to verify that the method proposed in this work can actually outperform the traditional ones as just mentioned by achieving 2.9 mm drop in mean per joint position error (MPJPE) in 3.6M dataset, resulting in 12.2 mm in MPJPE, and mitigate the drift in real scenario. In our future work, we plan to verify the effectiveness of the proposed model and system for rehabilitation through clinical studies.
Attribute-Based Conditional Proxy Re-encryption in the Standard Model Under LWE

Attribute-based conditional proxy re-encryption (AB-CPRE) allows delegators to carry out attribute-based control on the delegation of decryption by setting policies and attribute vectors. The fine-grained control of AB-CPRE makes it suitable for a variety of applications, such as cloud storage and distributed file systems. However, all existing AB-CPRE schemes are constructed under classical number-theoretic assumptions, which are vulnerable to quantum cryptoanalysis. Therefore, we propose the first AB-CPRE scheme based on the learning with errors (LWE) assumption. Constructed from fully key-homomorphic encryption (FKHE) and key-switching techniques, our scheme is unidirectional, single-hop, and enables a polynomial-depth boolean circuit as its policy. Furthermore, we split the ciphertext into two independent parts to avoid two-level or multi-level encryption/decryption mechanisms. Taking advantage of it, we then extend our single-hop AB-CPRE into an efficient and concise multi-hop one. No matter how many transformations are performed, the re-encrypted ciphertext is in constant size, and only one encryption/decryption algorithm is needed. Both of our schemes are proved to be selective secure against chosen-plaintext attacks (CPA) in the standard model.