Reviewerly/exHarmony huggingface dataset:
A Complete Quantum Circuit to Solve the Information Set Decoding Problem

Providing strong security margins against cryptanalytic attackers equipped with quantum computers is a major research direction fostered by the US National Institute of Standards and Technology (NIST) Post-quantum Cryptography Standardization process. Among the viable candidates, code-based asymmetric cryptosystems are one of the prominent approaches. In this work, we propose the first fully detailed quantum circuit to compute the solution to the Information Set Decoding problem, the main cryptanalytic tool against such cryptosystems. We evaluate the cryptanalytic effort with our circuit design on actual parameters from cryptosystems admitted to the final stage of the NIST standardization process and compare it with the previous conservative asymptotic estimates. We show that the actual computational effort of our solution is smaller than the one estimated via asymptotics by a factor of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> . We also perform a comparison of our results with the quantum-computational effort of breaking the AES cipher, following the guidelines of the US NIST in evaluating the security of the ciphers. To do this, we translate our design on gates of the Clifford+T gate set only, one of the most promising candidate for fault-tolerant quantum computation, and report that the parameter choices for Classic McEliece and BIKE, two candidates admitted to the final round of the NIST standardization process provide an adequate security margin with respect to our ISD solution technique.
A survey of lattice based expressive attribute based encryption

Attribute Based Encryption (ABE) can be employed to enforce fine grained access control over encrypted data. Due to expressiveness, ABE schemes are currently employed in cloud computing and storage systems. The classical ABE schemes based on bilinear pairing are vulnerable to quantum cryptanalysis, whereas ABE schemes from lattices can resist quantum attacks. In this work, we comprehensively survey various kinds of attribute based encryption schemes in the lattice setting in terms of expressiveness, complexity assumptions, efficiency, security and so on. We also discuss attribute based encryption schemes from lattices deserving further research to specify future directions for cryptographers.
A Geometric Perspective on Functional Outlier Detection

We consider functional outlier detection from a geometric perspective, specifically: for functional datasets drawn from a functional manifold, which is defined by the data’s modes of variation in shape, translation, and phase. Based on this manifold, we developed a conceptualization of functional outlier detection that is more widely applicable and realistic than previously proposed taxonomies. Our theoretical and experimental analyses demonstrated several important advantages of this perspective: it considerably improves theoretical understanding and allows describing and analyzing complex functional outlier scenarios consistently and in full generality, by differentiating between structurally anomalous outlier data that are off-manifold and distributionally outlying data that are on-manifold, but at its margins. This improves the practical feasibility of functional outlier detection: we show that simple manifold-learning methods can be used to reliably infer and visualize the geometric structure of functional datasets. We also show that standard outlier-detection methods requiring tabular data inputs can be applied to functional data very successfully by simply using their vector-valued representations learned from manifold learning methods as the input features. Our experiments on synthetic and real datasets demonstrated that this approach leads to outlier detection performances at least on par with existing functional-data-specific methods in a large variety of settings, without the highly specialized, complex methodology and narrow domain of application these methods often entail.
Interpolation and extrapolation between the magnetic chiral states using autoencoder

Autoencoder, an artificial neural network, was adopted to generate spin structures that interpolate and extrapolate between two distinct magnetic chiral states, the labyrinth structure and the skyrmion structure. We trained the autoencoder using two distinct magnetic chiral structures. Each input data is encoded through a deep learning process into a latent code, a new representation of the information in a reduced dimensional space. We investigated the latent space to acquire information on the structure of the latent code distribution. With the acquired information, we successfully produced various magnetic structures that exhibit plausible properties under various external fields not provided in the training data. The latent codes were modified by two algorithms. The first algorithm utilizes inversion and translation operation in the latent space and the second algorithm uses recursive flow with a modification bias. The first produced structures preserving the chiral structure of original data and the second produced statistically plausible states.
PARVP

Networked cameras continue to be an attractive target of cyber-attacks and therefore present huge risks to organizations. The use of vulnerable credentials (manufacturers default or publicly known) by these devices remains a primary concern for network and cybersecurity teams. This paper aims to assist enterprise network operators to systematically and passively assess the risk of using default credentials or vulnerable authentication schemes for directly accessing connected cameras. Our contributions are two-fold: (1) We analyze HTTP traffic traces of enterprise-grade network cameras (sourced from popular manufacturers including Cisco, Axis, and Pelco), identify the signature of their authentication techniques, including Basic, regular Digest, and Web Service Security (WSS), extracted from request packets, and develop a system with an algorithm (PARVP) for automatic and passive assessment of authentication risks; and (2) We apply PARVP to traffic traces of about 1.4 million HTTP authentication sessions selectively collected from network traffic of more than 1000 cameras (in our university campus network) during three weeks, and draw insights into risks, including cameras that accept default passwords (though hashed) and camera controllers that reveal passwords (though obsolete) by insecure authentication.
Semigroup of Finite-State Deterministic Intuitionistic Fuzzy Automata with Application in Fault Diagnosis of an Aircraft Twin-Spool Turbofan Engine

The twin-spool turbofan engine is an important component of almost every modern aircraft. Fault detection at an early stage can improve engine performance and health. The current research is based on the construction of an inference system for fault diagnosis in a generalized fuzzy environment. For such an inference system, finite-state deterministic intuitionistic fuzzy automata (FDIFA) are established. A semigroup of FDIFA and its algebraic properties including substructures and structure-preserving maps are studied. The FDIFA semigroups are used as variables for the inference system, and FDIFA semigroup homomorphisms are used to indicate the relation between variables. The newly established model is then applied to diagnose the possible fault and their nature in aircraft twin-spool turbofan engines by modelling the performance of the supercharger and air cooler.
Carbon Management of Multi-Datacenter Based On Spatio-Temporal Task Migration

With the massive deployment of geographically distributed data centers (DCs) and the demand surge for cloud services, their high energy consumption and carbon pollution are increasingly problematic. Therefore, mitigating the harmful effects of the carbon footprint of DCs has become a critical challenge. This paper takes the first step toward analyzing the complementary characteristics of global multi-regional renewable energy sources (RES). There is an opportunity to schedule workloads flexibly and track RES to reduce emissions. Integrating DCs and inter-DC network to form a refined emission model for the trade-off between emission cutting effects from scheduling and carbon costs of workload migration, we propose the spatio-temporal task migration mechanism to pursue low carbon in dual-dimension: This paper shifts intensive workloads to locations sufficient in RES at a coarse scale, and adjust the execution time of the workload in response to real-time RES fluctuations at a fine scale. Thus, the emission overage is shifted and offset by RES in a complementary manner; meanwhile, the acceptance of RES is enhanced. Finally, experiments with real-world data show that our method can optimally coordinate demand with RES and mitigate carbon pollution in geographically distributed DCs, and verify the performance and applicability with various-parameter scenarios.
Examining the Effect of Perceived Responsibility on Online Bystander Intervention, Target Hardening, and Inaction

Failure to take responsibility for intervening has been identified as a primary barrier to bystander intervention. Building on these findings, we examine how perceptions of responsibility affect responses to witnessing victimization in the online realm-a topic that has received limited attention. Using a maximum-likelihood selection model, we analyze data from the Pew American Trends Panel (N = 3709) to estimate the effects of respondents' perceptions of the role different groups should play in addressing online harassment on their likelihood to engage in intervention, target hardening, or inaction in response to witnessing online harassment, conditioned upon their likelihood of having witnessed such behavior. Findings indicate that the greater role respondents believe online users should have in addressing online harassment, the more likely they are to intervene. (b = .310). The greater role respondents believe law enforcement or elected officials should have in addressing online harassment, the less likely they are to intervene (b = -.135 and -.072, respectively). These findings have implications for future efforts to curb online harassment through users' crime prevention efforts.
Multilabel convolution neural network for facial expression recognition and ordinal intensity estimation

Facial Expression Recognition (FER) has gained considerable attention in affective computing due to its vast area of applications. Diverse approaches and methods have been considered for a robust FER in the field, but only a few works considered the intensity of emotion embedded in the expression. Even the available studies on expression intensity estimation successfully assigned a nominal/regression value or classified emotion in a range of intervals. Most of the available works on facial expression intensity estimation successfully present only the emotion intensity estimation. At the same time, others proposed methods that predict emotion and its intensity in different channels. These multiclass approaches and extensions do not conform to man heuristic manner of recognising emotion and its intensity estimation. This work presents a Multilabel Convolution Neural Network (ML-CNN)-based model, which could simultaneously recognise emotion and provide ordinal metrics as the intensity estimation of the emotion. The proposed ML-CNN is enhanced with the aggregation of Binary Cross-Entropy (BCE) loss and Island Loss (IL) functions to minimise intraclass and interclass variations. Also, ML-CNN model is pre-trained with Visual Geometric Group (VGG-16) to control overfitting. In the experiments conducted on Binghampton University 3D Facial Expression (BU-3DFE) and Cohn Kanade extension (CK+) datasets, we evaluate ML-CNN’s performance based on accuracy and loss. We also carried out a comparative study of our model with some popularly used multilabel algorithms using standard multilabel metrics. ML-CNN model simultaneously predicts emotion and intensity estimation using ordinal metrics. The model also shows appreciable and superior performance over four standard multilabel algorithms: Chain Classifier (CC), distinct Random K label set (RAKEL), Multilabel K Nearest Neighbour (MLKNN) and Multilabel ARAM (MLARAM).
Video Retrieval Queries of Large Scale Images: An Efficient Approach

We address the challenge for retrieving videos from large scale data base using images as query is necessary to implement for many applications. Here a symmetric comparison methodology for vectors Fisher is provided. It is queried for basic items with different degrees of clutter are rigorously investigated. The advantages of these techniques are shown here. The aggregation of locally-based characteristics by shots is assessed. For the experiment, four different aggregation modes are offered. These combined modes help to reduce the expectancy and memory requirement of recovery by additional than ‘3X.’ With the intention of growth, the proficiency and accuracy of the process of retrieval, here described a top K-Image videos queries. Numerous applications, for example content linking and product monitoring, need retrieving films from vast libraries utilising image queries. We provide a novel retrievals architecture wherein picture queries are immediately compared to the database video, considerably enhancing retrievals scalability over a reference point system that explorations the databases at the video frames levels.
ContriSci: A BERT-Based Multitasking Deep Neural Architecture to Identify Contribution Statements from Research Papers

With the rapid growth of scientific literature, it is becoming increasingly difficult to identify scientific contribution from the deluge of research papers. Automatically identifying the specific contribution made in a research paper would help quicker comprehension of the work, faster literature survey, comparison with the related works, etc. Here in this work, we investigate methods to automatically extract the contribution statements from research articles. We design a multitask deep neural network leveraging section identification and citance classification of scientific statements to predict whether a given scientific statement specifies a contribution or not. In the long-run, we envisage to create a knowledge graph of scientific contributions for machine comprehension and more straightforward navigation of research contributions in a particular domain. Our approach achieves the best performance over earlier methods (a relative improvement of 8.08% in terms of \(F_1\) score) for contributing sentence identification over a dataset of Natural Language Processing (NLP) papers. We make our code available at here (https://github.com/ammaarahmad1999/Sem-Eval-2021-Task-A).
Investigation of DNN-HMM and Lattice Free Maximum Mutual Information Approaches for Impaired Speech Recognition

Assistive tools that recognize impaired speech due to neurological disorders are emerging and its a fairly complex task. An Intelligent Impaired Speech Recognition system helps persons with speech impairment to improve their interactions with outside world. Impaired speakers have difficulty in pronouncing words which results in partial or incomplete speech contents. Existing Automatic Speech Recognition systems are not effective for Impaired Speech Recognition due to the speaker specific variations which depend on the severity of the neurological disorders. In this work, we have investigated two important approaches namely, Deep Neural Network-Hidden Markov Model and Lattice Free Maximum Mutual Information approach for effective recognition of impaired speech. The training and testing samples are collected from persons with different neurological disorders at varied intelligibility levels such as high, medium, low and very low. The recognition accuracy is evaluated and compared using two datasets namely 20 acoustically similar words and 50 words Impaired Speech Corpus in Tamil.
RoboCup@Home: Evaluation of voice recognition systems for domestic service robots and introducing Latino Dataset

Service robotics is a growing area with a huge impact on humans&#x2019; everyday life. RoboCup@Home competition has been pushing the limits of service robot research by presenting more challenging tests for the competitors every year. Fluid communication between service robot systems and humans is a must to improve robot performance. This challenging task has two main issues (i) the noisy environment, (ii) the speaker&#x2019;s accents since most operators are non-native English speakers. In this paper, we present an evaluation of state-of-the-art ASR systems in the context of the RoboCup@Home competition. Also, we introduce the Latino Dataset, a novel dataset of RoboCup@Home-like commands from non-native Latin American speakers. Several experiments were conducted in order to simulate the RoboCup@Home competition environment. To this end, four datasets were chosen to cover the whole accents of English commands the robots are exposed (i) clean native English, (ii) native English with noise, (iii) non-native Asian English, and (iv) non-native Latin American English. The results show that PicoVoice and Kaldi would perform better in RoboCup@Home environments.
A Survey on Trends of Two-Factor Authentication

The rapid increase of cybercriminals on identity theft and data breaches due to weak authentication schemes, user's poor password management experience, and attacks such as phishing and man-in-the-middle attacks firmly shifts the traditional way of authentication method (i.e., user name/passwords) to multifactor authentication security control. However, different organization starts implementing different ways of two-factor authentication (2FA) prevention mechanism which is the simplest form of multifactor authentication (MFA). This 2FA is mostly achieved by combining different factors such as what you know (i.e., PIN, password, etc.), what you have (i.e., one-time password (OTP), token, digital certificate, etc.) and what you are (i.e., fingerprint, iris, etc.) with traditional username and password. Even if an organization such as banking and e-commerce sectors started using this solution still multifactor authentication, not in impenetrable stage and multiple cases, has come out that highlights some of the weaknesses of these security measures. Bad guys will use a variety of attack methods such as social engineering (phishing), man-in-the-middle attacks (MITA), and breaching of weak credential to gain authentication access of a secure network infrastructure. The most effective methods for mitigating the risk of authentication process from being compromised by bad guys are to find out a solution that reduces the process of the user and builds the authentication process with strong cryptography which follows standards mainly by using digital certificates. In this survey study, we will briefly analyze different types of two-factor authentication based on their performance and we will suggest the best strong methods of multifactor authentication accordingly.
Attendance Management System Using Facial Recognition and Image Augmentation Technique

Over decades the attendance of students has been taken using methods involving paper. The limitations of this method are widely known and clearly understood, it is time-consuming, prone to errors and there is always a chance of proxy attendance. Many techniques that are implemented in today’s time are vastly unreliable and are majorly inefficient, like biometrics and Radio Frequency Identification (RFID), more importantly when there is a pandemic that majorly spreads via touch. This clearly presents an opportunity in the field of facial feature detection and face recognition. We propose an effective and modish solution to mark attendance using the face recognition technique including Haar Cascade and Local Binary Pattern Histogram algorithms. The system will recognize the face of an individual or multiple students and compare them with the predefined face encoding to make a CSV file of attendees with their details. To create the database we will use image augmentation techniques. This system can also be used to tackle the problem of fake attendance and proxies.
A Lightweight Fine-Grained Action Recognition Network for Basketball Foul Detection

In recent years, deep neural networks for action recognition has attracted extensive attention because of its wide range of applications such as anomaly behavior detection in smart surveillance system. Among the proposed deep learning models, 3DCNN works very well in the action classification of large data sets, including UCF-101, HMDB-51, and Kinetics. However, for the classification of fine-grained actions, current action recognition models still need improvement. The fine-grained action means that the difference from the normal action is very small, and the time of occurrence is extremely short and difficult to distinguish. For example, in the basketball game, the foul action is a kind of fine-grained actions. Foul action recognition is very challenging because fouls in basketball games are always instantaneous and very similar to normal actions. In this paper, we propose a lightweight fine-grained action recognition model for basketball foul detection. Compared with other action recognition models such as two-stream model, 3DCNN, our proposed network has a better effect on this subtle classification task, and is lighter in parameters. The visualized foul feature distribution is concentrated in a few frames that supports our initial hypothesis that fouls always happen instantaneously. Finally, the output of this research can be used to assist in training basketball referees.
Trustworthy Pre-processing of Sensor Data in Data On-Chaining Workflows for Blockchain-Based IoT Applications

Prior to provisioning sensor data to smart contracts, a pre-processing of the data on intermediate off-chain nodes is often necessary. When doing so, originally constructed cryptographic signatures cannot be verified on-chain anymore. This exposes an opportunity for undetected manipulation and presents a problem for applications in the Internet of Things where trustworthy sensor data is required on-chain.
Multi-modal Human pose estimation based on probability distribution perception on a depth convolution neural network

Existing human pose estimation methods have some problems, such as key-point representation quantization error, algorithm inconsistency, and easy-to-introduce unequal quantization error. Addressing these issues, this paper proposes a method of human key-point coordinate representation based on probability distribution perception on a depth convolution neural network. In this method, the perceived probability of the key-point coordinate distribution is extracted by a convolutional neural network, and the cross entropy is formed with the prediction probability distribution as the loss function. Under the constraint of minimizing the Kullback-Leibler (KL) distance between the prediction and the ground truth, the convolution neural network is optimized, and the coordinates of the heat map are finally located. The experimental results show that compared with the traditional method based on a heat map, the proposed method can accelerate the reasoning speed of the model in the COCO training set, and at the same time, it can improve the map by 1.7