Reviewerly/exHarmony huggingface dataset:
Homogeneity of Engineering Courses from an Assignment Management Model in Virtual Learning Environments

The management models for assigning courses to professors in higher education have shown the need to involve technologies for the construction of homogeneous virtual classrooms. This work presents a proposal for the creation of pilot virtual classrooms that allow a quick start of the teaching-learning process in first year students, measuring its impact. The objective is to determine the levels of acceptance of pre-existing significant learning resources in large groups of the same subject. The results, collected through a survey of 64 participants who passed the Programming and Linear Algebra course of the Engineering Careers at Universidad Politécnica Salesiana of Guayaquil, in Ecuador, reflect satisfaction values that reach 91%. In addition, the content of the classrooms influences a 94% positivity in learning and a 94% usefulness from the students' perspective. The limitations found suggest a continuous improvement in the early year's teacher assignment management model. It is expected to carry out a following study based on the experience of the teachers responsible for the courses in a longitudinal work for the next courses to be offered. This work contributes to the development of good practices in the educational management of higher education institutions.
Perspective Chapter: Sexual Cybercrime – The Transition from the Virtual Aggression to the Physical Aggression

Cybercrime is all criminal offenses committed on telecommunications networks in general and more particularly on the Internet. Sexual cybercrime promotes the transition from virtual sexual assault to physical or bodily assault, the victims of which are often women and children through intimidation and harassment as well as the absence of consent. The damage caused by the cybersex crime of the victims can lead to serious repercussions on their physical and mental health. The obligation of the fight against this social scourge has imposed its analysis on several ways to generate the means of protection because it is the only way to preserve our families and our children particularly from being victims of sexual cybercrime.
FABRIC: Fast and Secure Unbounded Cross-System Encrypted Data Sharing in Cloud Computing

Existing proxy re-encryption (PRE) schemes to secure cloud data sharing raise challenges such as supporting the heterogeneous system efficiently and achieving the unbounded feature. To address this problem, we proposed a fast and secure unbounded cross-domain proxy re-encryption scheme, named FABRIC, which enables the delegator to authorize the semi-trusted cloud server to convert one ciphertext of an identity-based encryption (IBE) scheme to another ciphertext of an attribute-based encryption (ABE) scheme. As the first scheme to achieve the feature mentioned above, FABRIC not only enjoys constant computation overhead in the encryption, decryption, and re-encryption phases when the quantity of attributes increases, but is also unbounded such that the new attributes or roles could be adopted into the system anytime. Furthermore, FABRIC achieves adaptive security under the decisional linear assumption (DLIN). Eventually, detailed theoretical and experimental analysis proved that FABRIC enjoys excellent performance in efficiency and practicality in the cloud computing scenario.
Oscillator-Inspired Dynamical Systems to Solve Boolean Satisfiability

Dynamical systems can offer a novel non-Boolean approach to computing. Specifically, the natural minimization of energy in the system is a valuable property for minimizing the objective functions of combinatorial optimization problems, many of which are still challenging to solve using conventional digital solvers. In this work, we design two oscillator-inspired dynamical systems to solve quintessential computationally intractable problems in Boolean satisfiability (SAT). The system dynamics are engineered such that they facilitate solutions to two different flavors of the SAT problem. We formulate the first dynamical system to compute the solution to the 3-SAT problem, while for the second system, we show that its dynamics map to the solution of the Max-NAE-3-SAT problem. Our work advances understanding of how this physics-inspired approach can be used to address challenging problems in computing.
PerspectiveNet: An Object Detection Method With Adaptive Perspective Box Network Based on Density-Aware

Aiming at the problems of object mutual occlusion and inaccurate confidence in complex traffic environments, we propose an adaptive network object detection algorithm with a perspective box based on density-aware. Firstly, an adaptive convolutional neural network is designed according to the complexity of image density. Res2Net-101 is used as the backbone. For the images with objects concentrated on both sides and occluded each other more, a high-density network is selected to improve the accuracy of object detection, and a low-density network is selected for the images with significant and simple objects. Secondly, in the high-density network, to improve the accuracy of object detection and reduce the missed detection problem caused by occlusion, a perspective box is added to the detection head, and the transparent box is fused with the prediction box to obtain more accurate positioning. Finally, we propose the perspective loss function, which is based on the repulsion loss of focusing smooth function and combined with feature loss and classification loss to form the overall loss of the model. The experimental results show that the model has a good detection effect compared with the state-of-the-art object detection model in complex traffic environments. Without reducing the detection speed, the mAP of PerspectiveNet on the KITTI dataset is 86.2%, which is 3.6% higher than that of VarifocalNet. On the Cityscapes dataset, the mAP is 96.3%, which is 1.7% higher than that of VarifocalNet.
RBFK cipher: a randomized butterfly architecture-based lightweight block cipher for IoT devices in the edge computing environment

Abstract Internet security has become a major concern with the growing use of the Internet of Things (IoT) and edge computing technologies. Even though data processing is handled by the edge server, sensitive data is generated and stored by the IoT devices, which are subject to attack. Since most IoT devices have limited resources, standard security algorithms such as AES, DES, and RSA hamper their ability to run properly. In this paper, a lightweight symmetric key cipher termed randomized butterfly architecture of fast Fourier transform for key (RBFK) cipher is proposed for resource-constrained IoT devices in the edge computing environment. The butterfly architecture is used in the key scheduling system to produce strong round keys for five rounds of the encryption method. The RBFK cipher has two key sizes: 64 and 128 bits, with a block size of 64 bits. The RBFK ciphers have a larger avalanche effect due to the butterfly architecture ensuring strong security. The proposed cipher satisfies the Shannon characteristics of confusion and diffusion. The memory usage and execution cycle of the RBFK cipher are assessed using the fair evaluation of the lightweight cryptographic systems (FELICS) tool. The proposed ciphers were also implemented using MATLAB 2021a to test key sensitivity by analyzing the histogram, correlation graph, and entropy of encrypted and decrypted images. Since the RBFK ciphers with minimal computational complexity provide better security than recently proposed competing ciphers, these are suitable for IoT devices in an edge computing environment.
Equivalence Checking of Parameterized Quantum Circuits

Variational quantum algorithms have been introduced as a promising class of quantum-classical hybrid algorithms that can already be used with the noisy quantum computing hardware available today by employing parameterized quantum circuits. Considering the non-trivial nature of quantum circuit compilation and the subtleties of quantum computing, it is essential to verify that these parameterized circuits have been compiled correctly. Established equivalence checking procedures that handle parameter-free circuits already exist. However, no methodology capable of handling circuits with parameters has been proposed yet. This work fills this gap by showing that verifying the equivalence of parameterized circuits can be achieved in a purely symbolic fashion using an equivalence checking approach based on the ZX-calculus. At the same time, proofs of inequality can be efficiently obtained with conventional methods by taking advantage of the degrees of freedom inherent to parameterized circuits. We implemented the corresponding methods and proved that the resulting methodology is complete. Experimental evaluations (using the entire parametric ansatz circuit library provided by Qiskit as benchmarks) demonstrate the efficacy of the proposed approach. The implementation is open source and publicly available as part of the equivalence checking tool QCEC (https://github.com/cda-tum/qcec) which is part of the Munich Quantum Toolkit (MQT).
Software Tools for Decoding Quantum Low-Density Parity-Check Codes

Quantum Error Correction (QEC) is an essential field of research towards the realization of large-scale quantum computers. On the theoretical side, a lot of effort is put into designing error-correcting codes that protect quantum data from errors, which inevitably happen due to the noisy nature of quantum hardware and quantum bits (qubits). Protecting data with an error-correcting code necessitates means to recover the original data, given a potentially corrupted data set-a task referred to as decoding. It is vital that decoding algorithms can recover error-free states in an efficient manner. While theoretical properties of recent QEC methods have been extensively studied, good techniques to analyze their performance in practically more relevant settings is still a widely unexplored area. In this work, we propose a set of software tools that allows to numerically experiment with so-called Quantum Low-Density Parity Check codes (QLDPC codes)-a broad class of codes, some of which have recently been shown to be asymptotically good. Based on that, we provide an implementation of a general decoder for QLDPC codes. On top of that, we propose an efficient heuristic decoder that tackles the runtime bottlenecks of the general QLDPC decoder while still maintaining comparable decoding performance. These tools eventually allow to confirm theoretical results around QLDPC codes in a more practical setting and showcase the value of software tools (in addition to theoretical considerations) for investigating codes for practical applications. The resulting tool, which is publicly available at https://github.com/lucasberent/qecc under the MIT license, is meant to provide a playground for the search for "practically good" quantum codes.
TiC-SAT

Transformer models have achieved impressive results in various AI scenarios, ranging from vision to natural language processing. However, their computational complexity and their vast number of parameters hinder their implementations on resource-constrained platforms. Furthermore, while loosely-coupled hardware accelerators have been proposed in the literature, data transfer costs limit their speed-up potential. We address this challenge along two axes. First, we introduce tightly-coupled, small-scale systolic arrays (TiC-SATs), governed by dedicated ISA extensions, as dedicated functional units to speed up execution. Then, thanks to the tightly-coupled architecture, we employ software optimizations to maximize data reuse, thus lowering miss rates across cache hierarchies. Full system simulations across various BERT and Vision-Transformer models are employed to validate our strategy, resulting in substantial application-wide speed-ups (e.g., up to 89.5X for BERT-large). TiC-SAT is available as an open-source framework1.
Virtual Reality Campuses as New Educational Metaverses

This paper focuses on the potential value and future prospects of using virtual reality (VR) technology in online education. In detailing online education and the latest VR technology, we focus on metaverse construction and artificial intelligence (AI) for educational VR use. In particular, we describe a virtual university campus in which on-demand VR lectures are conducted in virtual lecture halls, automated evaluations of student learning and training using machine learning, and the linking of multiple digital campuses.
ZPA: A Smart Home Privacy Analysis System Based on ZigBee Encrypted Traffic

Currently, the ZigBee protocol is widely used in smart homes and provides convenience to people. However, smart home devices often carry a large amount of real physical world information, which may result in information leakage problems. In this paper, to reveal the privacy security issues existing in ZigBee-based smart home networks, we design a smart home privacy analysis system based on ZigBee-encrypted traffic, called ZPA. ZPA can extract ZigBee data features based on the device’s operating mode and time window and use state-of-the-art machine-learning models to identify the type and status of smart home devices that could leak users’ private information. Through the analysis of 20 different devices from 5 manufacturers, the results show that even if the ZigBee traffic is protected by encryption, the accuracy of the proposed method in device type identification and state inference can reach approximately 93% and 98%, respectively. The types and statuses of devices in smart homes will reveal the user’s activity information to a certain extent. The privacy security of ZigBee-based smart devices still needs to be further strengthened.
Quantum information masking of an arbitrary unknown state can be realized in the multipartite lower-dimensional systems

Abstract Quantum information masking is a protocol that hides the original quantum information from subsystems and spreads it over quantum correlation, which is available to multipartite except bipartite systems. In this work, we explicitly study the quantum information masking in multipartite scenario and prove that there exists an universal quantum masker can mask the information encoding in the unknown states into a multipartite system with lower-dimensional local systems, namely, all the k -level quantum states can be masked into a m -qudit systems ( m ≥ 4) whose local dimension d ≤ k . In order to observe the masking process intuitively, explicitly controlled operations are provided. Our scheme well demonstrates the abundance of quantum correlation between multipartite quantum system and has potential application in the security of quantum information processing.
Deep Mining Technology of Database Information Based on Artificial Intelligence Technology

The database is the core of the information management system. When there is no modern management tool such as enterprise information management system, the status of enterprise resources and the direction of adjustment are unclear, and it is quite difficult to make adjustment arrangements. In order to improve the information mining ability of enterprise database in the future, this paper combines intelligent optimization technology under artificial intelligence technology with information mining technology, and compares its information mining ability with traditional technology. The research results show that after using the intelligent optimization algorithm, the maximum mining times can reach 60 times/min, while the maximum mining times of the traditional algorithm is 33 times/min. This shows that the mining speed of the intelligent optimization algorithm used in this paper is much higher than that of the traditional algorithm. And the mining speed of enterprises transformed through intelligent optimization is nearly 40% higher than that of traditional algorithms.
Social Network Text Sentiment Analysis Method Based on CNN-BiGRU in Big Data Environment

Aiming at the serious colloquialism of social network texts and the sparse semantic features, this article proposes a CNN-BiGRU-based sentiment analysis method for social network texts in the big data environment. First, the dependency syntax tree is introduced to represent the dependency relationship between words to construct the word vector to represent the text. Then, sentiment features with different granularity are extracted by multiple convolution kernels of different sizes in a convolution neural network (CNN). These sentiment features are input into bidirectional gated recurrent unit (BiGRU) network for analysis to obtain deeper sentiment features. Finally, a certain number of neurons are discarded by the Dropout method, and sentiment types are classified by the Sigmoid activation function. The Weibo_senti_100k Weibo data set is used to demonstrate the proposed method. The results show that if the Dropout value is set to 0.25 and the Adam optimizer is selected, the analysis performance is the best. The accuracy, precision, recall, and AUC are about 94.09%, 95.13%, 92.87%, and 0.953, respectively, which has certain application value.
Wide &amp; deep generative adversarial networks for recommendation system

Generative Adversarial Networks (GANs) has achieved great success in computer vision like Image Inpainting, Image Super-Resolution. Many researchers apply it to improve the effectiveness of recommendation system. However, GANs-based methods obtain users’ preferences using a single Neural Network framework in generative model, which may not be fully mined. Furthermore, most GANs-based algorithms adopt cross-entropy loss to get pair-wise bias, but these methods don’t reveal global data distribution loss when data are sparse. Those problems will influence the performance of the algorithm and result in poor accuracy. To address these problems, we introduce Wide &amp; Deep Generative Adversarial Networks for Recommendation System (a.k.a W &amp; DGAN) in this paper. On the one hand, we employ Wide &amp; Deep Learning as a generative model capable of extracting both explicit and implicit information of user preferences. Furthermore, we combine Cross-Entropy loss in G with Wasserstein loss in D to get data distribution, then, the joint loss will be to receive the training information feedback from data distribution. Empirical results on three public benchmarks show that W&amp;DGAN significantly outperforms state-of-the-art methods.
A quantum threshold signature protocol based on mutual unbiased basis

For the existing quantum threshold signature protocols based on entangled state, it is difficult to prepare quantum resources. Meanwhile, for the quantum threshold signature protocols based on single-particle sequence, it has disadvantages of high computation and communication consumptions. To remedy these shortcomings, in this paper, we proposed a quantum threshold signature protocol based on mutual unbiased basis (MUB). In initialization stage, the message owner Alice prepares a quantum sequence [Formula: see text] with length of [Formula: see text], each single particle in [Formula: see text] is chosen from MUBs. The signature key is generated by Alice using threshold secret-sharing distribution algorithm, the verification key is generated by [Formula: see text] signers via threshold secret-sharing recovery algorithm. In signature generation phase, the signers execute the signature algorithm sequentially until the last signer and then obtain the final signature, next send it to verifier. In signature verification phase, the verifier Ver verifies the original message and the final signature using the verification key. Security analysis shows that the proposed protocol can resist interception-forgery attack, collusion attack and denial attack. Performance analysis shows that the proposed protocol is easier to prepare quantum resources than similar protocols, and has lower communication consumption, especially when the number of signers [Formula: see text] and increase gradually. Finally, the correctness of the proposed protocol is verified by simulation experiment on quantum cloud platform.
Report on the 1st Workshop on Measuring the Quality of Explanations in Recommender Systems (QUARE 2022) at SIGIR 2022

Explainable recommenders are systems that explain why an item is recommended, in addition to suggesting relevant items to the users of the system. Although explanations are known to be able to significantly affect a user's decision-making process, significant gaps remain concerning methodologies to evaluate them. This hinders cross-comparison between explainable recommendation approaches and is one of the issues hampering the widespread adoption of explanations in industry settings. The goal of QUARE '22 was to promote discussion upon future research and practice directions around evaluation methodologies for explanations in recommender systems. To that end, we brought together researchers and practitioners from academia and industry in a half-day event, co-located with SIGIR 2022. The workshop's program included two keynote talks, three sessions of technical paper presentations in the form of lightning talks followed by panel discussions, and a final plenary discussion session. Although the area of explanations for recommender systems is still in its early stages, QUARE saw the participation of researchers and practitioners from several fields, laying the groundwork for the creation of a community around this topic and indicating promising directions for future research and development. Date: 15 July, 2022. Website: https://sites.google.com/view/quare-2022/home.
Raw Waveform Based Speaker Identification Using Deep Neural Networks

Deep learning is attracting tremendous prominence as an adequate replacement for i-vectors in the speaker identification task. Deep neural networks have attained much attention in the end-to-end (E2E) speaker identification domain. Earlier, DNN trained on handcrafted speech features like Mel-filter banks and Mel-frequency cepstral coefficients. Later, as the raw speech signal is lossless, processing raw waveforms have become an active research area in E2E speaker identification, automatic music tagging, and speech recognition fields. Convolutional neural networks (CNNs) have recently shown promising results when fed directly with raw speech samples. CNN analyzes waveforms to discover low-level speech representations rather than conventional handcrafted features, which may enable the system to handle speaker properties like pitch and formants more efficiently. An efficient design of neural networks is vital to achieving this. The CNN architecture proposed in this paper promotes the deep convolutional layers to develop more efficient filters for end-to-end speaker identification systems. The proposed architecture converges quickly and outperforms conventional CNN on raw waveforms. This research work has been tested on the Librispeech dataset and improved the Speaker identification accuracy by 10% and decreased the validation loss by 32%.
A New Outlier Detection Algorithm Based on Fast Density Peak Clustering Outlier Factor

Outlier detection is an important field in data mining, which can be used in fraud detection, fault detection, and other fields. This article focuses on the problem where the density peak clustering algorithm needs a manual parameter setting and time complexity is high; the first is to use the k nearest neighbors clustering algorithm to replace the density peak of the density estimate, which adopts the KD-Tree index data structure calculation of data objects k close neighbors. Then it adopts the method of the product of density and distance automatic selection of clustering centers. In addition, the central relative distance and fast density peak clustering outliers were defined to characterize the degree of outliers of data objects. Then, based on fast density peak clustering outliers, an outlier detection algorithm was devised. Experiments on artificial and real data sets are performed to validate the algorithm, and the validity and time efficiency of the proposed algorithm are validated when compared to several conventional and innovative algorithms.