Reviewerly/exHarmony huggingface dataset:
Multiplexed telecommunication-band quantum networking with atom arrays in optical cavities

The realization of a quantum network node of matter-based qubits compatible with telecommunication-band operation and large-scale quantum information processing is an outstanding challenge that has limited the potential of elementary quantum networks. We propose a platform for interfacing quantum processors comprising neutral atom arrays with telecommunication-band photons in a multiplexed network architecture. The use of a large atom array instead of a single atom mitigates the deleterious effects of two-way communication and improves the entanglement rate between two nodes by nearly two orders of magnitude. Furthermore, this system simultaneously provides the ability to perform high-fidelity deterministic gates and readout within each node, opening the door to quantum repeater and purification protocols to enhance the length and fidelity of the network, respectively. Using intermediate nodes as quantum repeaters, we demonstrate the feasibility of entanglement distribution over $\ensuremath{\approx}1500\phantom{\rule{0.28em}{0ex}}\text{km}$ based on realistic assumptions, providing a blueprint for a transcontinental network. Finally, we demonstrate that our platform can distribute $\ensuremath{\gtrsim}25$ Bell pairs over metropolitan distances, which could serve as the backbone of a distributed fault-tolerant quantum computer.
Digital literacy skills of university students with visual impairment: A mixed-methods analysis

Both for new higher education students and especially for individuals with visual impairment (VI), becoming digitally literate is a necessity in order to be able to fully participate in society and to overcome certain educational barriers. This study explores the digital literacy (DL) skills of university students in Turkey with VI, along with the factors that can affect DL levels. A sequential explanatory mixed-methods design was employed. Quantitative data were collected from 60 participants during the first phase of the study through an online survey consisting of demographic questions and the DL Scale. Qualitative data were collected via semi-structured interviews held with eight participants as the second phase. The study found quite high levels for several self-reported technical and cognitive sub-factors of DL skills, and lower levels for the social sub-factor. Comparative analysis revealed no significant difference between gender, level of VI, and type of school. The results showed that daily use of the Internet had a significant positive effect on DL scores, and that there was a negative correlation between DL and age of starting to use technology. The qualitative findings suggested that the participants already possessed basic DL skills such as accessing information and creating files; however, they lacked skills in information management, effective collaboration, communication, and in digital content creation. The study sheds light on factors affecting DL of VI students and discusses implications for both researchers and practitioners.
In-Vehicle CAN Bus Tampering Attacks Detection for Connected and Autonomous Vehicles Using an Improved Isolation Forest Method

The development and applications of mobile communication technologies in intelligent autonomous transportation systems have led to an extraordinary rise in the mount of connected and autonomous vehicles (CAVs). Ensuring the security of in-vehicle communication data is the basis for the safety of cooperative transportation systems. An in-vehicle controller area network (CAN) bus is an important issue in in-vehicle security, and some hackers have mastered remote vehicle control methods through the CAN bus network. This paper proposes an improved isolation forest method with data mass (MS-iForest) for data tampering attack detection, in which we use data mass instead of the number of divisions and give an anomaly score ranking to quantify the degree of anomalies. This method is promising to be used as part of the intrusion detection system, like a security component in the onboard gateway, which can effectively avoid the data tampering attacks. We compare the proposed method with other anomaly detection schemes based on the data collected from an in-vehicle simulated dataset and two standard datasets. The experiment results show that the proposed method performs better than the other anomaly detection schemes in terms of the area under the receiver operating curve (AUC).
Learning Transferable User Representations with Sequential Behaviors via Contrastive Pre-training

Learning effective user representations from sequential user-item interactions is a fundamental problem for recommender systems (RS). Recently, several unsupervised methods focusing on user representations pre-training have been explored. In general, these methods apply similar learning paradigms by first corrupting the behavior sequence, and then restoring the original input with some item-level prediction loss functions. Despite its effectiveness, we argue that there exist important gaps between such item-level optimization objective and user-level representations, and as a result, the learned user representations may only lead to sub-optimal generalization performance. In this paper, we propose a novel self-supervised pre-training framework, called CLUE, which stands for employing Contrastive Learning for modeling sequence-level User rEpresentation. The core idea of CLUE is to regard each user behavior sequence as a whole and then construct the self-supervision signals by transforming the original user behaviors by data augmentations (DA). Specifically, we employ two Siamese (weight-sharing) networks to learn the user-oriented representations, where the optimization goal is to maximize the similarity of learned representations of the same user by these two encoders. More importantly, we perform careful investigation of the impacts of view generating strategies for user behavior inputs from a more comprehensive perspective, including processing sequential behaviors by explicit DA strategies and employing dropout as implicit DA. To verify the effectiveness of CLUE, we perform extensive experiments on several user-related tasks with different scales and characteristics. Our experimental results show that the user representations learned by CLUE surpass existing item-level baselines under several evaluation protocols.
An Intelligent Multisampling Tensor Model for Oral Cancer Classification

Oral cancer has been one of the most mortal diseases in the world, and accurate and timely treatment will efficiently improve the survival and cure rate of the patients. However, the traditional diagnosis ways by the clinicians could be laborious and easily misdiagnosed, and oral cancer usually with different morphological features, which makes it challenging to achieve the high accuracy classification automatically. To address this challenge, in this article, we propose an intelligent multisampling tensor model to achieve oral cancer and cyst classification from magnetic resonance imaging (MRI). Specially, our approach first encodes the input image by four simple sampling operations, which enables the model to learn more regional, global, influential, and correlative features, and then a representation fusion strategy is adopted to fuse those extracted representations. Afterward, those are contracted by sequences of matrix product states, which map the input representation into high dimensional space to conduct a classifier operation. Finally, we evaluate our proposed method on oral MRI cancer data, and the experimental result demonstrates that our approach could achieve competitive classification results.
Neural Network Structure Optimization by Simulated Annealing

A critical problem in large neural networks is over parameterization with a large number of weight parameters, which limits their use on edge devices due to prohibitive computational power and memory/storage requirements. To make neural networks more practical on edge devices and real-time industrial applications, they need to be compressed in advance. Since edge devices cannot train or access trained networks when internet resources are scarce, the preloading of smaller networks is essential. Various works in the literature have shown that the redundant branches can be pruned strategically in a fully connected network without sacrificing the performance significantly. However, majority of these methodologies need high computational resources to integrate weight training via the back-propagation algorithm during the process of network compression. In this work, we draw attention to the optimization of the network structure for preserving performance despite compression by pruning aggressively. The structure optimization is performed using the simulated annealing algorithm only, without utilizing back-propagation for branch weight training. Being a heuristic-based, non-convex optimization method, simulated annealing provides a globally near-optimal solution to this NP-hard problem for a given percentage of branch pruning. Our simulation results have shown that simulated annealing can significantly reduce the complexity of a fully connected network while maintaining the performance without the help of back-propagation.
Fuzzy dynamic parameter adaptation in the bird swarm algorithm for neural network optimization

Fuzzy dynamic parameter adaptation has proven to be of great help when it is implemented in bio-inspired algorithms for optimization in different application areas, such as control, mathematical functions, classification, among others. One of the main contributions of this work is the proposed improvement of the Bird Swarm algorithm using a Fuzzy System approach, and we called this improvement the Fuzzy Bird Swarm Algorithm. Furthermore, we use a set of complex Benchmark Functions of the Congress on Evolutionary Computation Competition 2017 to compare the results between the original algorithm and the proposed improvement of the algorithm. The fuzzy system is utilized for the dynamic parameter adaptation of the C1 and C2 parameters of the Bird Swarm Algorithm. As a result, the Fuzzy Bird Swarm Algorithm has enhanced exploration and exploitation abilities that help in achieving better results than the Bird Swarm Algorithm. We additionally test the algorithm's performance in a real problem in the medical area, using the optimization of a neural network to obtain the risk of developing hypertension. This neural network uses information, such as age, gender, body mass index, systolic pressure, diastolic pressure, if the patient smokes and if the patient has parents with hypertension. Hypertension is one of the leading causes of heart problems, which in turn are also one of the top causes of death. Moreover, these days it causes more complications and deaths in people infected with COVID-19, the virus of the ongoing pandemic. Based on the results obtained through the 30 experiments carried out in three different study cases, and the results obtained from the statistical tests, it can be concluded that the proposed method provides better performance when compared with the original method.
Screening Medical Face Mask for Coronavirus Prevention using Deep Learning and AutoML

In the last two years, the COVID-19 pandemic causes a global health crisis around the world. On the other hand, given the current shortage and limits of medical resources, the World Health Organization (WHO) suggests several measures to control the infection rate and avoid depleting limited medical resources. In fact, wearing a medical mask is one of the non-pharmaceutical measures that can be used to limit the spread of this pandemic. This paper aims to present a new deep learning model based on AutoML for medical face mask detection. This proposed model was trained on a publicly available dataset that contained three classes: With mask, Incorrect mask, and Without mask. The achieved results show that the proposed model reaches an Accuracy and sensitivity of 99.74% and 99% respectively.
Bias in Automated Speaker Recognition

Automated speaker recognition uses data processing to identify speakers by their voice. Today, automated speaker recognition is deployed on billions of smart devices and in services such as call centres. Despite their wide-scale deployment and known sources of bias in related domains like face recognition and natural language processing, bias in automated speaker recognition has not been studied systematically. We present an in-depth empirical and analytical study of bias in the machine learning development workflow of speaker verification, a voice biometric and core task in automated speaker recognition. Drawing on an established framework for understanding sources of harm in machine learning, we show that bias exists at every development stage in the well-known VoxCeleb Speaker Recognition Challenge, including data generation, model building, and implementation. Most affected are female speakers and non-US nationalities, who experience significant performance degradation. Leveraging the insights from our findings, we make practical recommendations for mitigating bias in automated speaker recognition, and outline future research directions.
CauAIN: Causal Aware Interaction Network for Emotion Recognition in Conversations

Emotion Recognition in Conversations has attained increasing interest in the natural language processing community. Many neural-network based approaches endeavor to solve the challenge of emotional dynamics in conversations and gain appealing results. However, these works are limited in capturing deep emotional clues in conversational context because they ignore the emotion cause that could be viewed as stimulus to the target emotion. In this work, we propose Causal Aware Interaction Network (CauAIN) to thoroughly understand the conversational context with the help of emotion cause detection. Specifically, we retrieve causal clues provided by commonsense knowledge to guide the process of causal utterance traceback. Both retrieve and traceback steps are performed from the perspective of intra- and inter-speaker interaction simultaneously. Experimental results on three benchmark datasets show that our model achieves better performance over most baseline models.
The very knotty lenser: Exploring the role of regularization in source and potential reconstructions using Gaussian process regression

Reconstructing lens potentials and lensed sources can easily become an underconstrained problem, even when the degrees of freedom are low, due to degeneracies, particularly when potential perturbations superimposed on a smooth lens are included. Regularization has traditionally been used to constrain the solutions where the data failed to do so, e.g. in unlensed parts of the source. In this exploratory work, we go beyond the usual choices of regularization and adopt observationally motivated priors for the source brightness. We also perform a similar comparison when reconstructing lens potential perturbations, which are assumed to be stationary, i.e. permeate the entire field of view. We find that physically motivated priors lead to lower residuals, avoid overfitting, and are decisively preferred within a Bayesian quantitative framework in all the examples considered. For the perturbations, choosing the wrong regularization can have a detrimental effect that even high-quality data cannot correct for, while using a purely smooth lens model can absorb them to a very high degree and lead to biased solutions. Finally, our new implementation of the semi-linear inversion technique provides the first quantitative framework for measuring degeneracies between the source and the potential perturbations.
A survey on blockchain, SDN and NFV for the smart-home security

Due to millions of loosely coupled devices, the smart-home security is gaining the attention of industry professionals, attackers, and academic researchers. The smart home is a typical home where many sensors, actuators, and IoT devices are used to automate home users’ daily activities. Although a smart home provides comfort, safety, and satisfaction to users, it opens up multiple challenging security issues when automating and offering intelligent services. Recent studies have investigated not only blockchain but SDN and NFV to address these challenges. We present a comprehensive survey on blockchain, SDN, and NFV for smart-home security. The paper also proposes a new architecture of the smart-home security. First, we describe the features of the smart home and its current security issues. Next, we outline the characteristics of blockchain, SDN, and NFV, including their contribution to improving the smart-home security. While SDN enhances the management and access control of the home network by providing a programmable controller to home nodes, NFV implements the functions of network appliances (e.g., network monitoring, firewall) as virtual machines and ensures the high availability of the network. Blockchain reinforces IoT data’s privacy, integrity, and security and improves the trust in transactions among untrusted IoT devices. Finally, we discuss open issues and challenges in the field and propose recommendations towards high-level security for the smart home.
TODOS as digital science-support environment to provide STEM-education

The amount of scientific information has been growing exponentially. It became more complicated to process and systemize this amount of unstructured data. The approach to systematization of scientific information based on the ontological IT platform Transdisciplinary Ontological Dialogs of Object-Oriented Systems (TODOS) has many benefits. It has been proposed to select semantic characteristics of each work for their further introduction into the IT platform TODOS. An ontological graph with a ranking function for previous scientific research and for a system of selection of journals has been worked out. These systems provide high performance of information management of scientific information.
Centralized information web-oriented educational environment of Ukraine

The modern development of science and technology has provided high quantity of information. This information must be systemized and classified. For taxonomization of educational materials, it was proposed to use existing graph-generators and graph-visualizers of the TODOS IT platform. A separate aspect of the TODOS IT platform is the possibility of using a centralized web-oriented learning environment. Creation of the system and transdisciplinary knowledge is a problem of modern education, which can be solved by creating a centralized web-oriented educational environment. Using this approach is an important part of the learning process. Such a centralized web-oriented environment based on the ontological approach involves filling, adaptive educational services with information resources that reflect the conceptual system of a particular discipline.One of the systems providing not only collection of information but include its systemizing is centralized web-oriented educational environment based on Ontology4 system. Ontology 4 use elements of the TODOS.The paper presents specific developments of one centralized web-oriented educational environment can be used to teach different subjects such as biology, chemistry, Ukrainian language and literature, using the STEM approach.
A self-adaptive gradient-based particle swarm optimization algorithm with dynamic population topology

The aggregation of individuals facilitates local information exchange, and the migration of individuals from one population to another leads to a dynamic community structure. In addition, the negative feedback regulation mechanism of organisms helps them in good living conditions. Based on the above knowledge, a novel particle swarm optimization algorithm with a self-organizing topology structure and self-adaptive adjustable parameters is proposed (KGPSO). During the optimization process, the K-Means clustering method periodically divides the particle swarm into multiple distance-based sub-swarms, and the optimal number of sub-swarms is determined by maximizing the Calinski-Harabasz index. This strategy helps maintain the population diversity and gives particles the ability to perceive the surrounding environment. The parameters used to update the particle velocity are adjusted based on the gradient descent of its fitness error, ensuring a dynamic balance between exploration and exploitation. The hyperparameters of KGPSO are tuned by Bayesian optimization method to improve the algorithm performance further. Two benchmark suites are used to evaluate the performance of KGPSO. Both ranking results and Wilcoxon signed-rank tests show that KGPSO performs best among the PSO algorithms tested. Moreover, the excellent optimization capability of KGPSO are proven in the process of X-ray CT image enhancement, making it possible to analyze the structure and motion of heterogeneous granular materials efficiently and robustly. In conclusion, the proposed KGPSO can provide a stable and powerful support for the frontier experimental research of granular materials and expand the research scope.
Research on Path-Planning Algorithm Integrating Optimization A-Star Algorithm and Artificial Potential Field Method

A fusion pathfinding algorithm based on the optimized A-star algorithm, the artificial potential field method and the least squares method is proposed to meet the performance requirements of path smoothing, response speed and computation time for the path planning of home cleaning robots. The fusion algorithm improves the operation rules of the traditional A-star algorithm, enabling global path planning to be completed quickly. At the same time, the operating rules of the artificial potential field method are changed according to the path points found by the optimal A-star algorithm, thus greatly avoiding the dilemma of being trapped in local optima. Finally, the least squares method is applied to fit the complete path to obtain a smooth path trajectory. Experiments show that the fusion algorithm significantly improves pathfinding efficiency and produces smoother and more continuous paths. Through simulation comparison experiments, the optimized A-star algorithm reduced path-planning time by 60% compared to the traditional A-star algorithm and 65.2% compared to the bidirectional A-star algorithm path-planning time. The fusion algorithm reduced the path-planning time by 65.2% compared to the ant colony algorithm and 83.64% compared to the RRT algorithm path-planning time.
CHORE: Contact, Human and Object Reconstruction from a Single RGB Image

Most prior works in perceiving 3D humans from images reason human in isolation without their surroundings. However, humans are constantly interacting with the surrounding objects, thus calling for models that can reason about not only the human but also the object and their interaction. The problem is extremely challenging due to heavy occlusions between humans and objects, diverse interaction types and depth ambiguity. In this paper, we introduce CHORE, a novel method that learns to jointly reconstruct the human and the object from a single RGB image. CHORE takes inspiration from recent advances in implicit surface learning and classical model-based fitting. We compute a neural reconstruction of human and object represented implicitly with two unsigned distance fields, a correspondence field to a parametric body and an object pose field. This allows us to robustly fit a parametric body model and a 3D object template, while reasoning about interactions. Furthermore, prior pixel-aligned implicit learning methods use synthetic data and make assumptions that are not met in the real data. We propose a elegant depth-aware scaling that allows more efficient shape learning on real data. Experiments show that our joint reconstruction learned with the proposed strategy significantly outperforms the SOTA. Our code and models are available at https://virtualhumans.mpi-inf.mpg.de/chore