Reviewerly/exHarmony huggingface dataset:
Meta-Meta Classification for One-Shot Learning

We present a new approach, called meta-meta classification, to learning in small-data settings. In this approach, one uses a large set of learning problems to design an ensemble of learners, where each learner has high bias and low variance and is skilled at solving a specific type of learning problem. The meta-meta classifier learns how to examine a given learning problem and combine the various learners to solve the problem. The meta-meta learning approach is especially suited to solving few-shot learning tasks, as it is easier to learn to classify a new learning problem with little data than it is to apply a learning algorithm to a small data set. We evaluate the approach on a one-shot, one-class-versus-all classification task and show that it is able to outperform traditional meta-learning as well as ensembling approaches.
CPPNet: A Coverage Path Planning Network

This paper presents a deep-learning based CPP algorithm, called Coverage Path Planning Network (CPPNet). CPPNet is built using a convolutional neural network (CNN) whose input is a graph-based representation of the occupancy grid map while its output is an edge probability heat graph, where the value of each edge is the probability of belonging to the optimal TSP tour. Finally, a greedy search is used to select the final optimized tour. CPPNet is trained and comparatively evaluated against the TSP tour. It is shown that CPPNet provides near-optimal solutions while requiring significantly less computational time, thus enabling real-time coverage path planning in partially unknown and dynamic environments.
A Correlation Fault Attack on Rotating S-Box Masking AES

Masking is a commonly used countermeasure for protecting cryptographic implementations from power side channel analysis. Rotating S-Box masking (RSM) is a state-of-the-art masking technique, whose effectiveness has been testified in ongoing worldwide DPA contest. However, masking does not provide sufficient protection against active side channel attacks through fault injection. In this paper, we propose a simple yet effective correlation fault attack on two generations of RSM AES schemes. We demonstrate through mathematical formulations that the random offset and shuffles in RSM do not affect fault fusion. Round keys can be recovered by leveraging the strong linear correlation in fault effects at multiple locations. Experimental results have demonstrated that random fault injections in the penultimate and antepenultimate rounds allow full recovery of the last round key with two correct-faulty ciphertext pairs or four faulty ciphertexts under the same plaintext. Our method has a key search complexity of 2<sup>16</sup> and eliminates the time-consuming fault attack template or model training process.
AMiner Citation-Data Preprocessing for Recommender Systems on Scientific Publications

Recommender Systems (RS) are used to find user’s interested items among a huge amount of digital information, recently called Big Data, with the purpose of making valuable personalized recommendations. These systems use data from digital, online libraries to train, test and evaluate system’s efficiency. Along this line, data preprocessing is an essential and valuable step to achieve information-preserving data reduction and, in addition, to create input files with the appropriate format needed by a RS. This paper describes our approach for data preprocessing using a scientific publications’ dataset (Computer Science) found in AMiner (https://www.aminer.org/). The proposed approach consists of two phases: creation of a collection of articles based on user preferences and preprocessing this collection. The experimental results demonstrate the value of our approach with at least 79.8% information-preserving data reduction.
Auditing saliency cropping algorithms

In this paper, we audit saliency cropping algorithms used by Twitter, Google and Apple to investigate issues pertaining to the male-gaze cropping phenomenon as well as race-gender biases that emerge in post-cropping survival ratios of face-images constituting 3 × 1 grid images. In doing so, we present the first formal empirical study which suggests that the worry of a male-gaze-like image cropping phenomenon on Twitter is not at all far-fetched and it does occur with worryingly high prevalence rates in real-world full-body single-female-subject images shot with logo-littered backdrops. We uncover that while all three saliency cropping frameworks considered in this paper do exhibit acute racial and gender biases, Twitter’s saliency cropping framework uniquely elicits high male-gaze cropping prevalence rates. In order to facilitate reproducing the results presented here, we are open-sourcing both the code and the datasets that we curated at shorturl.at/iuzK9. We hope the computer vision community and saliency cropping researchers will build on the results presented here and extend these investigations to similar frameworks deployed in the real world by other companies such as Microsoft and Facebook.
ConsistSum

Unsupervised opinion summarization techniques are designed to condense the review data and summarize informative and salient opinions in the absence of golden references. Existing dominant methods generally follow a two-stage framework: first creating the synthetic "review-summary" paired datasets and then feeding them into the generative summary model for supervised training. However, these methods mainly focus on semantic similarity in synthetic dataset creation, ignoring the consistency of aspects and sentiments in synthetic pairs. Such inconsistency also brings a gap to the training and inference of the summarization model.
Application of Deep Learning and Machine Learning in Pattern Recognition

In today’s digital environment, the pattern is everywhere. It is present in many aspects of our daily life. Algorithms can be used to detect or mathematically observe a pattern physically. Vector feature values represent a pattern in the digital world. With the advent of artificial intelligence (AI) techniques in the recent era, there are so many machine learning (ML) and deep learning (DL) models that have been developed. ML is the branch of AI, which can perform tasks like data analysis, analytical model building, and classification without being explicitly programmed. DL is the subset of ML in AI. In DL, mostly artificial neural networks (ANNs) are utilized. It has the capability of work based upon unsupervised learning from the data that is unlabeled and unstructured. Using these DL and ML models, extract the meaningful features from the given image or video is known as pattern recognition (PR). PR is used in many engineering applications such as computer vision, natural language processing, character recognition, robotics, speech recognition, and so on. It is also used in many medical image processing applications and telemedicine. The present work discussed the pattern recognition problem and its various stages in detail. In addition to that, the application of deep learning and machine learning in pattern recognition is also explained briefly.
FPGA-Based Deep Convolutional Neural Network Optimization Method

With the increasing demand for computing speed and real-time data processing in various fields, deep learning and convolutional neural networks are more and more widely used in the field of computer vision. FPGA-based deep convolutional neural networks (CNN) have been proposed and developed rapidly due to its high parallel processing ability, portability, and low power consumption. To further improve the network efficiency, this paper studies the software acceleration tool Vivado HLS provided by Xilinx, the quantification and pruning of convolution neural network model, which can effectively optimize the network model and accelerate the reasoning process.
Single-shot dense active stereo with pixel-wise phase estimation based on grid-structure using CNN and correspondence estimation using GCN

Active stereo systems based on static pattern projection, a.k.a. oneshot scan, have been widely used for measuring dynamic scenes. Many patterns used for oneshot active stereo have grid-structures and grid-wise codes. For such systems, the grid structure is first detected, and graph matching methods are applied to estimate correspondences. However, such graph matching is often vulnerable to graph connection errors caused by grid structure analysis based on image features. Also, dense reconstruction for such systems is an open problem, where pixel-wise correspondence estimation from sparse image features is required. We propose a learning-based method to capture grid structure information and pixel-wise positional information simultaneously. We also propose to represent the grid structure by graphs with augmented connections other than 4-neighbor connections and applying them to a graph convolutional network (GCN). The proposed method can analyze large variety of grid patterns, has auto-calibration capability, can reconstruct dense shapes for fast moving objects.
Alternative Collaborative Learning for Character Recognition in Low-Resolution Images

Character recognition in a single image is a technology utilized in various sensor platforms, such as smart parking and text-to-speech systems, and numerous studies are being conducted to improve its performance by experimenting with novel approaches. However, when low-quality images were inputted to a character recognition neural network for recognition, a difference in the resolution of the training image and low-quality image results in poor accuracy. To resolve this problem, this study proposes a collaborative trainable mechanism that integrates a global image feature extraction-based super-resolution neural network with a character recognition neural network. This collaborative trainable mechanism helps the character recognizer to be robust to inputs with varying quality in the real world. The alternative collaborative learning and character recognition performance test was conducted using the license plate image dataset among various character images, and the effectiveness of the proposed algorithm was verified using a performance test.
PAROT: Translating natural language to SPARQL

This paper provides a dependency based framework for converting natural language to SPARQL. We present a tool known as PAROT (which echos answers from ontologies) which is able to handle user's queries that contain compound sentences, negation, scalar adjectives and numbered list. PAROT employs a number of dependency based heuristics to convert user's queries to user's triples. The user's triples are then processed by the lexicon into ontology triples. It is these ontology triples that are used to construct SPARQL queries. From the experiments conducted, PAROT provides state of the art results.
AdaptKT: A Domain Adaptable Method for Knowledge Tracing

Knowledge tracing is a crucial and fundamental task in online education systems, which can predict students' knowledge state for personalized learning. Unfortunately, existing methods are domain-specific, whereas there are many domains (e.g., subjects, schools) in the real education scene and some domains suffer from the problem of lacking sufficient data. Therefore, how to exploit the knowledge in other domains, to improve the model's performance for target domain remains pretty much open. We term this problem as Domain Adaptation for Knowledge Tracing (DAKT), which aims to transfer knowledge from the source domain to the target one for knowledge tracing. In this paper, we propose a novel adaptable method, namely Adaptable Knowledge Tracing (AdaptKT), which contains three phases to explore this problem. Specifically, phase I is instance selection. Given the question texts of two domains, we train an auto-encoder to select and embed similar instances from both domains. Phase II is distribution discrepancy minimizing. After obtaining the selected instances and their linguistic representations, we train a knowledge tracing model and adopt the Maximum Mean Discrepancy (MMD) to minimize the discrepancy between the distributions of the domain-specific knowledge states. Phase III is fine-tuning of the output layer. We replace the output layer of the model that trained in phase II by a new one to make the knowledge tracing model's output dimension matches the number of knowledge concepts in the target domain. The new output layer is trained while other parameters that before it are frozen. We conduct extensive experiments on two large-scale real-world datasets, where the experimental results clearly demonstrate the effectiveness of AdaptKT for solving DAKT problem. We will public the code on the Github after the acceptance of the paper.
Transferring Inter-Class Correlation for Teacher–Student frameworks with flexible models

The Teacher–Student (T–S) framework is widely utilized in classification tasks, through which the performance of one neural network (the student) can be improved by transferring knowledge from another trained neural network (the teacher). As the transferring knowledge is related to the network capacities and structures between the teacher and the student, how to define knowledge effectively remains an open question. To address this issue, we design a novel and flexible transferring knowledge, Self-Attention based Inter-Class Correlation (ICC) map, which reveals the correlation between every two classes in a mini-batch. Based on the ICC map, we propose a T–S framework, Inter-Class Correlation Transfer (ICCT), in which the knowledge from the teacher with a higher, equal, or lower capacity than the student can bring the benefit to the training process of the student. The ICCT can be applied flexibly on the heterogeneous network structures of the T–S pairs and exhibits excellent compatibility with existing frameworks with hidden-layers knowledge. Notably, the analysis of the ICCT demonstrates that students comprehensively learn the teacher’s knowledge in conjunction with their own understanding, rather than mimicking the teacher’s knowledge entirely. Extensive experiments are conducted in CIFAR-10, CIFAR-100, and ILSVRC2012 image classification datasets in different T–S application scenarios with different network structures. The results demonstrate that the ICCT can improve the student’s performance and outperform other state-of-the-art T–S frameworks.
Truth tables for modal logics T and S4, by using three-valued non-deterministic level semantics

Abstract Novel three-valued non-deterministic level semantics for modal logics $\textbf {T}$ and $\textbf {S4}$ are presented. A criterion for partial level valuations is given, making it possible to create truth tables. Additionally, semantics and truth tables for $\textbf {0}$ (defined as $\textbf {PC}$ plus rule of necessitation) and $\textbf {0T}$ with only two values are based on Ivlev’s work. We need Kearns’ notion of level valuations: a generalization of Dugundji’s theorem shows that there is no non-deterministic semantics for modal logics up to $\textbf {S5}$, containing the rule of necessitation.
Semantic Malayalam Dialogue System For Covid-19 Question Answering Using Word Embedding And Cosine Similarity

Covid-19 is a global pandemic, has affected millions of people physically and mentally. The dynamic and rapidly growing situation with COVID-19 made it more difficult to discourse accurate and authoritative information about the disease, in most of the Indian local languages like Malayalam. To resolve this issue, here we propose a semantic Malayalam Dialogue System for COVID-19 related Question Answering. This is a user-friendly knowledge system to automatically deliver relevant answers to COVID-19 related queries in the Malayalam language. The proposed system proceeds in three stages; Document pre-processing, Semantic modelling using word embedding and Answer Retrieval. The NLP techniques are used for document processing, word embedding - CBOW and Skip Gram methods, Neural Network models are used for Semantic Modelling and finally, a cosine similarity measure is used to map and retrieve the answers for the user's queries. The experiment was conducted with our own Malayalam dataset; and compared the performance of two Word2Vec algorithms - CBOW and Skip Gram. The result, with our data set, shows that Skip-Gram is more efficient than CBOW and CBOW is faster than the Skip Gram model.
Image Restoration by Deep Projected GSURE

Ill-posed inverse problems appear in many image processing applications, such as deblurring and super-resolution. In recent years, solutions that are based on deep Convolutional Neural Networks (CNNs) have shown great promise. Yet, most of these techniques, which train CNNs using external data, are restricted to the observation models that have been used in the training phase. A recent alternative that does not have this drawback relies on learning the target image using internal learning. One such prominent example is the Deep Image Prior (DIP) technique that trains a network directly on the input image with the least-squares loss. In this paper, we propose a new image restoration framework that is based on minimizing a loss function that includes a "projected-version" of the Generalized Stein Unbiased Risk Estimator (GSURE) and parameterization of the latent image by a CNN. We propose two ways to use our framework. In the first one, where no explicit prior is used, we show that the proposed approach outperforms other internal learning methods, such as DIP. In the second one, we show that our GSURE-based loss leads to improved performance when used within a plug-and-play priors scheme.
Implementation of Smart Card for Promoting Higher Education in the Developing World

No doubt that quality education is a need of any nation today. In light of this fact, each nation put a particular effort to improve education and academic research. The e-learning system in this connection will concentrate on educational services such as learning materials stuff, video sharing, and podcasting-a more socially connected Internet where people can contribute as much as they can consume a technological foundation for delivering services. In such context, this article highlights the utilization of a simple ICT toolkit called a smart card used for communication purposes in the e-learning backlog. The findings of the study revealed that such toolkit is one of the best technological tools amongst other approaches used to help in avoiding some social barriers for learning in conservative societies.
Archetypes and Homophilic Avatars: New Approaches to Studying Far-Right Facebook Practice

Background: The concept of archetypes is often deployed to understand people engaged with far-right or right-wing online content. However, this can lead to essentializing assumptions that overlook how users are mobilized to participate with these politics. Analysis: This article theorizes how users are mobilized to perform far-right politics by conceptualizing them as embodying homophilic avatars, treating online practice as performative and homophily as an online and offline force. Conclusion and implications: Through qualitative analysis on Facebook, the homophilic avatar shows how users are driven to engage with far-right or right-wing content via political performativity and homophily, as opposed to psychological tendencies.
Proposal for a quantum random number generator using coherent light and a non-classical observable

The prototype quantum random number (random bit) generators (QRNG) consists of one photon at a time falling on a $50:50$ beam splitter followed by random detection in one or the other other output beams due to the irreducible probabilistic nature of quantum mechanics. Due to the difficulties in producing single photons on demand, in practice, pulses of weak coherent (laser) light are used. In this paper we take a different approach, one that uses moderate coherent light. It is shown that a QRNG can be implemented by performing photon-number parity measurements. For moderate coherent light, the probabilities for obtaining even or odd parity in photon counts are $0.5$ each. Photon counting with single-photon resolution can be performed through use of a cascade of beam splitters and single-photon detectors as was done recently in a photon-number parity-based interferometry experiment involving coherent light.
Interpretable Relation Learning on Heterogeneous Graphs

Relation learning, widely used in recommendation systems or relevant entity search over knowledge graphs, has attracted increasing attentions in recent years. Existing methods like network embedding and graph neural networks (GNNs), learn the node representations from neighbors and calculate the similarity score for relation prediction. Despite effective prediction performance, they lack explanations to the predicted results. We propose a novel interpretable relation learning model named IRL, which can not only predict whether relations exist between node pairs, but also make the inference more transparent and convincing. Specifically, we introduce a meta-path based path encoder to model sequential dependency between nodes through recurrent neural network. We also apply the self-supervised GNN on the extracted sub-graph to capture the graph structure by aggregating information from neighbors, which are fed into the meta-path encoder. In addition, we propose a meta-path walk pruning strategy for positive path generation and an adaptive negative sampling method for negative path generation to improve the quality of paths, which both consider the semantics of nodes in the heterogeneous graph. We conduct extensive experiments on two public heterogeneous graph data, AMiner and Delve, for different relation prediction tasks, which demonstrate significant improvements of our model over the existing embedding-based and sequential modeling-based methods.
Self-Supervised Learning of Depth and Ego-Motion From Videos by Alternative Training and Geometric Constraints from 3-D to 2-D

Self-supervised learning of depth and ego-motion from unlabeled monocular videos has acquired promising results and drawn extensive attention. Most of the existing methods jointly train the depth and pose networks by photometric consistency of adjacent views based on the principle of structure-from-motion (SFM). However, the coupled relationship of the depth and pose networks based on the scene reprojection seriously influences the learning performance due to the scale ambiguity of image reconstruction-based geometry learning or the error accumulation between the learning-based method and multiview geometry-based method. In this article, we aim to improve the performance of depth and pose estimation without the auxiliary tasks and reduce the influence of the above problems on algorithm performance by alternatively training each task and geometric constraints from 3-D to 2-D. Distinct from jointly training the depth and pose networks, our key idea is to better utilize the mutual dependency between two tasks by alternatively training each network with respective geometric constraints while fixing the other. To make the optimization process easier, the iterative closest point (ICP)-based 3-D structural consistency-embedded epipolar geometric constraints are further introduced into depth and pose networks learning, which can take full advantage of both geometric methods. Then, a log-scale 3-D structural consistency loss is designed to put more emphasis on the smaller depth values during training. Extensive experiments on various benchmark data sets indicate the superiority of our algorithm over the state-of-the-art self-supervised methods.