Reviewerly/exHarmony huggingface dataset:
Single-Server Private Information Retrieval With Side Information Under Arbitrary Popularity Profiles

This paper introduces a generalization of the Private Information Retrieval with Side Information (PIR-SI) problem called Popularity-Aware PIR-SI (PA-PIR-SI). The PAPIR-SI problem includes one or more remote servers storing copies of a dataset of K messages, and a user who knows M out of K messages—the identities of which are unknown to the server—as a prior side information, and wishes to retrieve one of the remaining K M messages. The goal of the user is to minimize the amount of information they must download from the server while revealing no information about the identity of the desired message. In contrast to PIR-SI, in PA-PIR-SI, the dataset messages are not assumed to be equally popular. That is, given the M side information messages, each of the remaining K M messages is not necessarily equally likely to be the message desired by the user. In this work, we focus on the single-server setting of PA-PIR-SI, and establish lower and upper bounds on the capacity of this setting—defined as the maximum possible achievable download rate. Our upper bound holds for any message popularity profile, and is the same as the capacity of single-server PIR-SI. We prove the lower bound by presenting a PA-PIR-SI scheme which takes a novel probabilistic approach—carefully designed based on the popularity profile—to integrate two existing PIR-SI schemes. The rate of our scheme is strictly higher than that of the only existing PIR-SI scheme applicable to the PA-PIR-SI setting.
Digital Blind Box: Random Symmetric Private Information Retrieval

We introduce the problem of random symmetric private information retrieval (RSPIR). In canonical PIR, a user downloads a message out of K messages from N non-colluding and replicated databases in such a way that no database can know which message the user has downloaded (user privacy). In SPIR, the privacy is symmetric, in that, not only that the databases cannot know which message the user has downloaded, the user itself cannot learn anything further than the particular message it has downloaded (database privacy). In RSPIR, different from SPIR, the user does not have an input to the databases, i.e., the user does not pick a specific message to download, instead is content with any one of the messages. In RSPIR, the databases need to send symbols to the user in such a way that the user is guaranteed to download a message correctly (random reliability), the databases do not know which message the user has received (user privacy), and the user does not learn anything further than the one message it has received (database privacy). This is the digital version of a blind box, also known as gachapon, which implements the above specified setting with physical objects for entertainment. This is also the blind version of 1-out-of-K oblivious transfer (OT), an important cryptographic primitive. We study the information-theoretic capacity of RSPIR for the case of N = 2 databases. We determine its exact capacity for the cases of K = 2,3,4 messages. While we provide a general achievable scheme that is applicable to any number of messages, the capacity for K ≥5 remains open.
The effect of the use of T or V pronouns in Dutch HR communication

In an online experiment among native speakers of Dutch we measured addressees' responses to emails written in the informal pronoun T or the formal pronoun V in HR communication. 172 participants (61 male, mean age 37 years) read either the V-versions or the T-versions of two invitation emails and two rejection emails by four different fictitious recruiters. After each email, participants had to score their appreciation of the company and the recruiter on five different scales each, such as The recruiter who wrote this email seems … [scale from friendly to unfriendly]. We hypothesized that (i) the V-pronoun would be more appreciated in letters of rejection, and the T-pronoun in letters of invitation, and (ii) older people would appreciate the V-pronoun more than the T-pronoun, and the other way around for younger people. Although neither of these hypotheses was supported, we did find a small effect of pronoun: Emails written in V were more highly appreciated than emails in T, irrespective of type of email (invitation or rejection), and irrespective of the participant's age, gender, and level of education. At the same time, we observed differences in the strength of this effect across different scales.
Exploring Crowdsourced Content Moderation Through Lens of Reddit during COVID-19

In 2020, when COVID-19 struck, social media gained even more influence in people’s lives due to increased online activity. This event led to a surge of false information and cyberbullying, making content moderation harder than ever. Given this challenge, exploring opportunities to explore content moderation solutions to reduce hate speech and fake news on social media is vital. In this paper, we examine if existing content moderation systems are enough during global pandemics and, if not, where gaps may lie. Due to its intriguing Decentralized Content Management System (DCMS), we chose Reddit as the key social networking platform for our hypothesis testing. We used 1.8 million Reddit posts from COVID-19-related subreddits from January 2020 to April 2021. Our findings reveal several significant trends regarding the effect of a worldwide event on content moderation methods designed to lessen the prevalence of hazardous content and fake news. In light of these considerations, we provide the results of comprehensive research conducted with particular attention paid to the user-generated material and the DCMS of Reddit.
Classification of Caries X-Ray Images using Multilayer Perceptron Models Based Shape Features

Dental caries is one of the diseases that are often experienced by society, one way to detect it by taking pictures using Computed Radiography technology. The aim of this study was to develop a method of classifying dental caries imagery with the Hu Moment Invariant (HMI) and Multilayer Perceptron (MLP) feature extraction methods as an alternative to facilitate the detection of dental caries. The dental caries image used is a dental caries image for grade 1, class 2, class 3, class 4, with a total of 220 images of which 90% are as training data and 10% data testing. Hu Moment Invariant is used as a method of feature extraction and image classification using the Multilayer Perceptron (MLP) method. Classification is carried out with 2 classifier models namely Levenberg-Marquardt (LM), and Bayesian Regularization (BR) with a ratio of 3 types of Hidden Layer (HL) namely Hidden Layer 1, 5, and 10. The results of the analysis showed that the classification of dental caries imagery using HMI feature extraction and MLP classification will be obtained the best results when using the LM Hidden Layer 10 Model with the best training and testing accuracy results with a value of 96.1% and 98.3% and an average computing time between 1 to 14 seconds.
Autonomous Mobile Robot Navigation for Complicated Environments by Switching Multiple Control Policies

In recent years, many navigation methods using deep reinforcement learning for autonomous mobile robots have been proposed to apply to various dynamic environments. However, since the learning results depend on the simulation environment, it may be inappropriate to apply them directly to the real environments from the standpoint of safety and efficiency. To solve the problem, in this paper, we propose a multi-policy switching method that enables safe and efficient navigation for autonomous mobile robots that share space with humans in various real-world environments, such as dense and crowded situations. Specifically, the method switches the navigation rule among four policies including deep reinforcement learning-based policy according to the size of the target robot’s movable space (i.e., unoccupied area around the robot). We verify the effectiveness of the proposed method by conducting navigation experiments with computer simulation. The results show that the proposed method improves collision avoidance rate in a narrow space where the robot tends to halt or oscillate by existing methods.
Programming Pedagogy and Assessment in the Era of AI/ML: A Position Paper

A growing set of tasks that once depended critically on human cognition can now be effectively accomplished by automated systems, either independently or in partnership with humans. This trend is being accelerated by advances in Artificial Intelligence and Machine Learning, and it forces educators to consider whether tasks emphasized by existing curricula will remain relevant once their students graduate. For introductory programming (CS1), existing curricula emphasize the task of code writing. We survey recent research on automated systems for writing code, some of which are as proficient as undergraduates on CS1-level programming tasks. Further, these tools are rapidly improving and some of them are available freely for students. Thus, we posit that pedagogy and assessment for code writing requires rethinking. We examine the components of the code-writing task using a six-step framework proposed in the literature, and we identify the impact of automated systems at each step. In response to this impact, we propose changes to CS1 pedagogy and assessment.
Automated optical inspection of FAST’s reflector surface using drones and computer vision

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) is the world ' s largest single-dish radio telescope. Its large reflecting surface achieves unprecedented sensitivity but is prone to damage, such as dents and holes, caused by naturally-occurring falling objects. Hence, the timely and accurate detection of surface defects is crucial for FAST's stable operation. Conventional manual inspection involves human inspectors climbing up and examining the large surface visually, a time-consuming and potentially unreliable process. To accelerate the inspection process and increase its accuracy, this work makes the first step towards automating the inspection of FAST by integrating deep-learning techniques with drone technology. First, a drone flies over the surface along a predetermined route. Since surface defects significantly vary in scale and show high inter-class similarity, directly applying existing deep detectors to detect defects on the drone imagery is highly prone to missing and misidentifying defects. As a remedy, we introduce cross-fusion, a dedicated plug-in operation for deep detectors that enables the adaptive fusion of multi-level features in a point-wise selective fashion, depending on local defect patterns. Consequently, strong semantics and fine-grained details are dynamically fused at different positions to support the accurate detection of defects of various scales and types. Our AI-powered drone-based automated inspection is time-efficient, reliable, and has good accessibility, which guarantees the long-term and stable operation of FAST.
Pseudo-Labeling and Meta Reweighting Learning for Image Aesthetic Quality Assessment

In the tasks of image aesthetic quality assessment, it is difficult to reach both the high score area and low score area due to the normal distribution of aesthetic datasets. To reduce the error in labelling and solve the problem of normal data distribution, we propose a new aesthetic mixed dataset with classification and regression called AMD-CR, and we train a meta reweighting network to reweight the loss of training data differently. In addition, we provide a training strategy according to different stages based on pseudo labels, and then we use it for aesthetic training according to different stages in classification and regression tasks. In the construction of the network structure, we construct an aesthetic adaptive block (AAB) structure that can adapt to any size of the input images. Besides, we also use the efficient channel attention (ECA) to strengthen the feature extracting ability of each task. The experimental result shows that our method improves 0.1112 compared with the conventional method in SROCC. The method can also help to find best aesthetic path planning for unmanned aerial vehicles (UAV) and vehicles.
Structure–Attribute Social Network Graph Data Publishing Satisfying Differential Privacy

With the development of big data, data collection and publishing are symmetrical. The purpose of data collection is to better publish data. To better collect user data and promote data analysis, publishing massive amounts of data can better provide services for people’s lives. However, in the process of publishing data, the problem of low data availability caused by over protection is widespread. In addition, the attacker indirectly obtains the data of the target user by accessing the data of the user’s friends or neighbors, which leads to the disclosure of the user’s privacy. In order to solve these problems, a structure–attribute social network data publishing model is proposed. This model protects the privacy of user attribute data and prevents homogeneity attacks through attribute data perturbation. In addition, the model disrupts the structure of social networks by introducing uncertainty graphs into network partitions to generate published social network data. Our scheme has been tested on three public datasets, and the results show that our scheme can retain the social network structure as much as possible.
Initial experimental results on a superconducting-qubit reset based on photon-assisted quasiparticle tunneling

We present here our recent results on qubit reset scheme based on a quantum-circuit refrigerator (QCR). In particular, we use the photon-assisted quasiparticle tunneling through a superconductor--insulator--normal-metal--insulator--superconductor junction to controllably decrease the energy relaxation time of the qubit during the QCR operation. In our experiment, we use a transmon qubit with dispersive readout. The QCR is capacitively coupled to the qubit through its normal-metal island. We employ rapid, square-shaped QCR control voltage pulses with durations in the range of 2--350 ns and a variety of amplitudes to optimize the reset time and fidelity. Consequently, we reach a qubit ground-state probability of roughly 97% with 80-ns pulses starting from the first excited state. The qubit state probability is extracted from averaged readout signal, where the calibration is based of the Rabi oscillations, thus not distinguishing the residual thermal population of the qubit.
Geochemical Characteristics of Soils to the Impact of Diamond Mining in Siberia (Russia)

This article presents the results of long-term research and monitoring of the soil cover exposed to the impact of the mining and processing plant developing diamond deposits in the northeast of Siberia. The soil collection includes 436 samples of different types of Cryosols. Soil pH; soil organic carbon (SOC); granulometric composition; and mobile forms of Pb, Ni, Mn, Cd, Co, Cr, Zn, Cu, and As were identified in the samples. Multivariate statistics of the correlation matrix, clustering analysis (CA), and principal component analysis (PCA) were used to determine the sources of heavy metals. The intensity of the accumulation of chemical elements in the soil was assessed using calculated concentration coefficients (Kc) and the index of total contamination of the soil cover (Zc). In the study area, Cryosols are characterized by biogenic accumulation of Ni, Mn, and Cd in the upper soil layer and Cr, Ni, Co, Mn, and Cu in the suprapermafrost horizon. Correlation matrix, CA, and PCA revealed three distinct sources that could be considered for the investigated potentially toxic elements (PTEs): anthropogenic, lithogenic, and the source which comes from a mixed contribution of anthropogenic and lithogenic factors. The most anthropogenic contribution in the heavy metals in the study area appears in Zn, Cd, As, and Pb. The assessment interpreted that origin of Mn in the area is most likely to be a natural source. The content of Co, Cr, and Ni are controlled by both lithogenic control and anthropogenic sources. Active accumulation of mobile forms of Mn, Zn, and Ni with anomalously high concentration coefficients can be traced in the soils in the impact zone of mining operations. Anthropogenic soil contamination is spread over an area of 260 km2.
Enhanced Firefly-K-Means Clustering with Adaptive Mutation and Central Limit Theorem for Automatic Clustering of High-Dimensional Datasets

Metaheuristic algorithms have been hybridized with the standard K-means to address the latter’s challenges in finding a solution to automatic clustering problems. However, the distance calculations required in the standard K-means phase of the hybrid clustering algorithms increase as the number of clusters increases, and the associated computational cost rises in proportion to the dataset dimensionality. The use of the standard K-means algorithm in the metaheuristic-based K-means hybrid algorithm for the automatic clustering of high-dimensional real-world datasets poses a great challenge to the clustering performance of the resultant hybrid algorithms in terms of computational cost. Reducing the computation time required in the K-means phase of the hybrid algorithm for the automatic clustering of high-dimensional datasets will inevitably reduce the algorithm’s complexity. In this paper, a preprocessing phase is introduced into the K-means phase of an improved firefly-based K-means hybrid algorithm using the concept of the central limit theorem to partition the high-dimensional dataset into subgroups of randomly formed subsets on which the K-means algorithm is applied to obtain representative cluster centers for the final clustering procedure. The enhanced firefly algorithm (FA) is hybridized with the CLT-based K-means algorithm to automatically determine the optimum number of cluster centroids and generate corresponding optimum initial cluster centroids for the K-means algorithm to achieve optimal global convergence. Twenty high-dimensional datasets from the UCI machine learning repository are used to investigate the performance of the proposed algorithm. The empirical results indicate that the hybrid FA-K-means clustering method demonstrates statistically significant superiority in the employed performance measures and reducing computation time cost for clustering high-dimensional dataset problems, compared to other advanced hybrid search variants.
Emoji Use as a Catalyst for Relationship Building and Sustaining Attention in Online Classes: An Empirical Study

Motivating students and sustaining their attention are essential for online classes in higher education. In particular, effective and efficient online communication between students and professors serves as a core driver for increased learning motivation and sustained attention. The purpose of this study was to examine the use of emojis and professor-student email communication as methods of motivating students and sustaining their attention. Specifically, this study hypothesized that a professor’s use of emojis in a welcome letter could result in the enhancement of psychological relationships with student recipients, such as students’ perceived authenticity and intimacy with the professor, leading to increased intrinsic learning motivation and sustained attention in the professor’s online class. The sample for the study was comprised of 297 undergraduate students from public universities in the United States. Data was collected via a scenario-based survey approach. The empirical findings indicated that emojis in a professor’s welcome letter could lead students to perceive the professor as authentic and friendly, making students more intrinsically motivated and more inclined to pay attention in the professor’s online class. Theoretical implications for the education field and pedagogical implications for developing online classes in higher education were proposed.
Two-Dimensional Path Planning Platform for Autonomous Walk behind Hand Tractor

The use of autonomous vehicles in agriculture has increased in recent years. To fully automate agricultural missions, particularly the tillage process using the walk-behind hand tractor, the path planning problem for the robot must be solved so that all points in the intended region of interest may be traced. The current planning algorithm has been successful in determining the best tillage path. On the other hand, the algorithm ignores the path built using the dynamic starting point, finish point and path distance. We propose a path planning concept for back-and-forth path patterns. Our algorithm employs a novel approach based on Laravel and Google Maps, which considers the user’s desired distance interval, start point, and finish point. We demonstrated auto-generating vertex-edge pathways in this research. Field trials using a walk-behind hand tractor in a plowing mission have been successfully conducted to validate the accuracy of the resulting waypoint coordinates.
CA-STD: Scene Text Detection in Arbitrary Shape Based on Conditional Attention

Scene Text Detection (STD) is critical for obtaining textual information from natural scenes, serving for automated driving and security surveillance. However, existing text detection methods fall short when dealing with the variation in text curvatures, orientations, and aspect ratios in complex backgrounds. To meet the challenge, we propose a method called CA-STD to detect arbitrarily shaped text against a complicated background. Firstly, a Feature Refinement Module (FRM) is proposed to enhance feature representation. Additionally, the conditional attention mechanism is proposed not only to decouple the spatial and textual information from scene text images, but also to model the relationship among different feature vectors. Finally, the Contour Information Aggregation (CIA) is presented to enrich the feature representation of text contours by considering circular topology and semantic information simultaneously to obtain the detection curves with arbitrary shapes. The proposed CA-STD method is evaluated on different datasets with extensive experiments. On the one hand, the CA-STD outperforms state-of-the-art methods and achieves 82.9 in precision on the dataset of TotalText. On the other hand, the method has better performance than state-of-the-art methods and achieves the F1 score of 83.8 on the dataset of CTW-1500. The quantitative and qualitative analysis proves that the CA-STD can detect variably shaped scene text effectively.
Image recognition based on optical reservoir computing

We propose an image recognition approach using a single physical node based optical reservoir computing. Specifically, an optically injected semiconductor laser with self-delayed feedback is used as the reservoir. We perform a handwritten-digit recognition task by greatly increasing the number of virtual nodes in delayed feedback using outputs from multiple delay times. Final simulation results confirm that the recognition accuracy can reach 99% after systematically optimizing the reservoir hyperparameters. Due to its simple architecture, this scheme may provide a resource-efficient alternative approach to image recognition.