Reviewerly/exHarmony huggingface dataset:
Self-supervised 4D Spatio-temporal Feature Learning via Order Prediction of Sequential Point Cloud Clips

Recently 3D scene understanding attracts attention for many applications, however, annotating a vast amount of 3D data for training is usually expensive and time consuming. To alleviate the needs of ground truth, we propose a self-supervised schema to learn 4D spatio-temporal features (i.e. 3 spatial dimensions plus 1 temporal dimension) from dynamic point cloud data by predicting the temporal order of sampled and shuffled point cloud clips. 3D sequential point cloud contains precious geometric and depth information to better recognize activities in 3D space compared to videos. To learn the 4D spatio-temporal features, we introduce 4D convolution neural networks to predict the temporal order on a self-created large scale dataset, NTU-PCLs, derived from the NTU-RGB+D dataset. The efficacy of the learned 4D spatio-temporal features is verified on two tasks: 1) Self-supervised 3D nearest neighbor retrieval; and 2) Self-supervised representation learning transferred for action recognition on a smaller 3D dataset. Our extensive experiments prove the effectiveness of the proposed self-supervised learning method which achieves comparable results w.r.t. the fully-supervised methods on action recognition on MSRAction3D dataset.
Navigation of a Fuzzy-Controlled Wheeled Robot Through the Combination of Expert Knowledge and Data-Driven Multiobjective Evolutionary Learning

This article proposes a navigation scheme for a wheeled robot in unknown environments. The navigation scheme consists of obstacle boundary following (OBF), target seeking (TS), and vertex point seeking (VPS) behaviors and a behavior supervisor. The OBF behavior is achieved by a fuzzy controller (FC). This article formulates the FC design problem as a new constrained multiobjective optimization problem and finds a set of nondominated FC solutions through the combination of expert knowledge and data-driven multiobjective ant colony optimization. The TS behavior is achieved by new fuzzy proportional-integral-derivative (PID) and proportional-derivative (PD) controllers that control the orientation and speed of the robot, respectively. The VPS behavior is proposed to shorten the navigation route by controlling the robot to move toward a new subgoal determined from the vertex point of an obstacle. A new behavior supervisor that manages the switching among the OBF, TS, and VPS behaviors in unknown environments is proposed. In the navigation of a real robot, a new robot localization method through the fusion of encoders and an infrared localization sensor using a particle filter is proposed. Finally, this article presents simulations and experiments to verify the feasibility and advantages of the navigation scheme.
Real-Time Panoptic Segmentation with Prototype Masks for Automated Driving

In this paper we propose a fast fully convolutional neural network for panoptic segmentation that can provide an accurate semantic and instance-level representation of the environment in the 2D space. We tackle panoptic segmentation as a dense classification problem and generate masks for stuff classes as well as for each instance of things classes. Our network employs a shared backbone and Feature Pyramid Network for multi-scale feature extraction which we extend with dual-decoders that learn background and foreground specific masks. Guided by object proposals, the panoptic head assembles location-sensitive prototype masks using a learned weighting scheme. Our solution runs in real-time, in 82 ms on high resolution images, making it suitable for robotic applications and automated driving. Extensive experiments on the Cityscapes dataset demonstrate that our panoptic segmentation network is robust and accurate, with 57.3% PQ and 76.9% mIoU.
Bidirectional Stereo Matching Network With Double Cost Volumes

In stereo matching, the high-quality cost volume is the key to improve the matching accuracy. Current stereo matching networks only use traditional methods or neural networks to generate one or more cost volumes. They do not consider combining different matching cost computation methods to improve the quality of cost volume. Therefore, we propose BSDCNet, a real-time stereo matching network consisting of two main modules: Double Matching Cost Computation and Bidirectional Cost Aggregation Network. The Double Matching Cost Computation module generates two different cost volumes through traditional methods and neural networks. The Bidirectional Cost Aggregation Network is a two-branch structure, which can aggregate the above two cost volumes with different network depths. Finally, we also design a feature fusion module (FFM) to fuse the two-branch features and use the result for disparity estimation. Our network only uses 3D cost volumes and two-dimensional convolutions. Thus it is much faster than state-of-the-art stereo networks (e.g., 36× than GC-Net, 16× than PSMNet, and 72× than GA-Net). Meanwhile, according to the KITTI official website, our network is more accurate than other fast stereo networks (e.g., Fast DS-CS, RTSNet, and DispNetC), demonstrating that our network can generate a real-time and accurate stereo matching result.
Chaotic Evolutionary Algorithm

The paper describes a chaotic optimization algorithm to globally find a minimum of a nonlinear unconstrained optimization problem. It is a hybrid algorithm that operates in two stages. While in stage one a chaotic map helps in taking the search near to the global optimum point, stage two finds the exact solution using the evolutionary search algorithm. Results of the algorithm for some benchmark functions are also provided to corroborate the algorithm.
Reliability Evaluation of ML systems, the oracle problem

The growing adoption of machine learning (ML) in safety-critical contexts makes reliability evaluation of ML systems a crucial task. Although testing represents one of the most used practices to evaluate the reliability of “traditional” systems, just few techniques can be used to evaluate ML-systems' reliability due to the oracle problem. In this paper, I present a test oracle surrogate able to automatically classify tests' outcome to obtain feedback about tests whose expected output is unknown. For this purpose, various sources of knowledge are considered to evaluate the outcome of each test. The aim is to exploit this test oracle surrogate to apply classical testing strategies to perform reliability assessment of ML systems. Some preliminary experiments have been performed considering a Convolutional Neural Network (CNN) and exploiting the well known MNIST dataset. These results promise that the presented technique can be effectively used to evaluate the reliability of ML systems.
The Cost of Privacy in Asynchronous Differentially-Private Machine Learning

We consider training machine learning models using data located on multiple private and geographically-scattered servers with different privacy settings. Due to the distributed nature of the data, communicating with all collaborating private data owners simultaneously may prove challenging or altogether impossible. We consider differentially-private asynchronous algorithms for collaboratively training machine-learning models on multiple private datasets. The asynchronous nature of the algorithms implies that a central learner interacts with the private data owners one-on-one whenever they are available for communication without needing to aggregate query responses to construct gradients of the entire fitness function. Therefore, the algorithm efficiently scales to many data owners. We define the cost of privacy as the difference between the fitness of a privacy-preserving machine-learning model and the fitness of trained machine-learning model in the absence of privacy concerns. We demonstrate that the cost of privacy has an upper bound that is inversely proportional to the combined size of the training datasets squared and the sum of the privacy budgets squared. We validate the theoretical results with experiments on financial and medical datasets. The experiments illustrate that collaboration among more than 10 data owners with at least 10,000 records with privacy budgets greater than or equal to 1 results in a superior machine-learning model in comparison to a model trained in isolation on only one of the datasets, illustrating the value of collaboration and the cost of the privacy. The number of the collaborating datasets can be lowered if the privacy budget is higher.
Enhanced (n, n)-threshold QR code secret sharing scheme based on error correction mechanism

In recent years, QR code is popular online and offline, and it is an ideal media for secret sharing. This paper utilizes the error correction capacity of QR code to propose an (n, n)-threshold secret sharing scheme for QR code. A secret QR code can be split and encoded into n cover QR codes. The generated marked QR codes still carry cover messages so that they will greatly reduce the suspicion of unauthorized people while transmitted in the public channel. The secret QR code can be easily reconstructed by using XOR operation when all the n authorized participants collaborate. Experimental results show that the proposed scheme is feasible and has high robustness. In addition, compared to the state-of-the-art work, the proposed scheme achieves a higher security.
Augmented reality sandboxes: children’s play and storytelling with mirror worlds

Augmented Reality (AR) technology has provided a new technological platform for 'mirror worlds', where layers of information, meaning, and functions are integrated with a digital twin of the real world. To explore mirror worlds, we designed and developed +Andscape, an interactive AR sandbox. In this conceptual and empirical case study, we observed children's (5–6-year-old, N = 16) collaborative play and storytelling with +Andscape. The qualitative content analysis from observational video-based data allows us to infer how children's play with the AR sandbox engaged their questioning and reflections of both the real world events and the computational mirror worlds. The use of the tool triggered children's imagination and opened for them a story world for exploration of current media events in a unique way. We conclude that when introducing mirror worlds, the focus should be on creative play, participation and storytelling through which the children can construct their own story worlds.
CMG2Vec: A composite meta-graph based heterogeneous information network embedding approach

Heterogeneous information network embedding has been intensively studied in the past years. However, existing methods require users to manually assign meta-paths or meta-graphs in advance. Meanwhile, most of previous approaches only consider a single type of meta-path or meta-graph which is usually sparse and biased, and thus the node representations learned may be incomprehensive and inaccurate. To tackle these limitations, we proposed an extensible semantic description structure, called Composite Meta-Graph(CMG). By virtue of such a structure, users do not need to worry about selection of an appropriate meta-path or meta-graph. Rich semantic relations and rich structural contexts between nodes of different types and of different distances can be elaborated accurately according to CMG. Moreover, a CMG based heterogeneous information embedding framework, namely CMG2Vec, is also proposed. By expanding the auto-encoder into a heterogeneous network scenario, CMG2Vec can embed proximities between nodes of multiple orders learned from CMG into latent representations after a series of encoding–decoding non-linear mapping. During the fusing process, an attention mechanism is adopted to automatically learn weights of these latent vectors, which enables each final node representation to focus on proximity of the most informative order. Experimental results on three large-scale datasets demonstrate that our method outperforms existing state-of-the-art homogeneous and heterogeneous network embedding approaches in three network mining tasks in terms of node classification, node clustering, and node similarity search.
Quantifying proportions of different material sources to loess based on a grid search and Monte Carlo model: A case study of the Ili Valley, Central Asia

The Ili Valley is among the main distribution areas of loess deposits in Xinjiang Province, Central Asia, while the provenance of Ili loess remains under debate. In this study, samples from near-surface loess, two types of topsoil and modern riverbed sediment were analyzed for their concentrations of major and trace geochemical elements to determine the relative proportions of different provenances of loess deposits in different zones of the Ili Valley. The results obtained by the grid search technology and Monte Carlo model indicated that the proximal material is dominated in the Ili loess. Alluvial-diluvial sediments as the main local material source have significantly influenced loess in the western region of Ili Valley. Moreover, this influence gradually decreases in the eastern region and the Zhaosu Basin. The proportion of modern riverbed sediment in the eastern Ili Valley is significantly lower than that in the Zhaosu Basin and is lowest in the western Ili Valley. However, the proportion of dust and topsoil type-II with the mean value of 11.8% and 7.2%, respectively, is highest in the western Ili Valley and lowest in the Zhaosu Basin. The complex natural background of the Ili Valley can be used to interpret the quantitative results and the geochemical characteristics of Ili loess from different regions. The reliability of the proposed method can be assessed by environmental indicators such as grain size, and geomorphic-hydrological background and other published records.
Predicting Spatio-Temporal Entropic Differences for Robust No Reference Video Quality Assessment

We consider the problem of robust no reference (NR) video quality assessment (VQA) where the algorithms need to have good generalization performance when they are trained and tested on different datasets. We specifically address this question in the context of predicting video quality for compression and transmission applications. Motivated by the success of the spatio-temporal entropic differences video quality predictor in this context, we design a framework using convolutional neural networks to predict spatial and temporal entropic differences without the need for a reference or human opinion score. This approach enables our model to capture both spatial and temporal distortions effectively and allows for robust generalization. We evaluate our algorithms on a variety of datasets and show superior cross database performance when compared to state of the art NR VQA algorithms.
Money Decentralization under Direct Democracy Procedures. The Case of Classical Athens

By analyzing the case of Athens during the Classical period (508-323 BCE) the main thesis of this paper is that under direct democracy procedures and the related institutional setup, a monetary system without a Central Bank may function relatively well. We focus on the following issues: (i) Τhe procedures of currency issuing in the Athenian city-state, (ii) why the Athenian drachma become the leading international currency in the Mediterranean world (iii) how and towards which targets monetary policy without a Central Bank was possible (iv) defining the targets of monetary policy and the mechanisms for its implementation (v) the role of money in the economy (vi) the issue of deficit spending (vii) the reasons of the replacement of the Athenian drachma as a leading currency by others from the Hellenistic period onwards (viii) the correlation of our findings regarding the decentralized character of monetary policy in Classical Athens to today’s realities, such as the issue of cryptocurrencies. Our analysis shows that monetary policy without a Central Bank was possible, with its foremost aim being the stability of the currency (mainly, silver coins) in order to enhance trust in it and so, make it an international currency which could outcompete other currencies. Since there was no Central Bank like today, monetary policy decisions were taken by the popular assembly of citizens in combination with the market forces themselves.
A Dozen Stickers on a Mailbox

Many non-profit peer-to-peer exchange arrangements and profit-driven, multi-sided online marketplaces leverage underutilized resources, such as tools, to optimize their use to capacity. They often rely on a digital platform in pursuit of their social aspirations and/or economic objectives. We report on a field study of a local sharing community that employs a set of stickers illustrating different household items, typically placed on community members' mailboxes, along with complementary digital tools. The stickers are used to communicate the availability of resources among neighbors to facilitate social encounters and to encourage sustainable use and re-use of shared resources. Through in-depth qualitative interviews with sixteen participants, we describe the opportunities and limitations of this approach to peer-to-peer exchange. We offer insights for designers of resource sharing communities into facilitating face-to-face encounters and the online interactions needed to support them.
A Chatbot System to Support Mine Safety Procedures during Natural Disasters

This study developed a chatbot to improve the efficiency of government activation of mine safety procedures during natural disasters. Taiwan has a comprehensive governmental system dedicated to responding to frequent natural disasters, and the Bureau of Mines has instituted clear procedures to ensure the delivery of disaster alarms and damage reports. However, the labor- and time-consumption procedures are inefficient. In this study, we propose a system framework for disaster-related information retrieval and immediate notifications to support the execution of mine safety procedures. The framework utilizes instant messaging (IM) applications as the user interface to look up information and send messages to announce the occurrence of disaster events. We evaluated the efficiency of the procedures before and after adopting the system and achieved a time-cost reduction of 55.8 min among three types of disaster events. The study has proven the feasibility of adopting novel techniques for decision-making and assures the improvement of the efficiency and effectiveness of the procedure activation.
Nicknames as identity badges: How self-reflective nicknames can facilitate users’ online social interactions

An Internet nickname is essential in facilitating online interpersonal interactions. It is a pivotal element of interaction that provides other users with an initial impression and affects the decisions and behaviors of users during their online interactions. Although the existing literature has found that nicknames are relevant to users’ psychology and behavior, direct research on the motivation for and the effect of choosing a particular Internet nickname has been very limited. Borrowing from relevant theories on avatars, this paper summarizes the motivations behind Internet nickname choices in terms of three aspects: virtual exploration, social navigation and contextual adaptation. Moreover, from the perspective of collective self-esteem (CSE) and group identification, this research explores the influence mechanism between the motivations underlying Internet nickname choices and users’ online social interactions. A total of 394 samples were obtained from online communities in China to test our research model. We found that virtual exploration, social navigation and contextual adaptation are three motivations for creating Internet nicknames, and they have direct effects on the online social interactions of users. CSE has a full mediating effect between virtual exploration and online social interaction and acts as a mediated variable between social navigation and online social interaction. Group identification fully mediates the relationship between social navigation and online social interaction as well as that between contextual adaptation and online social interaction. The conclusion of this paper provides not only a new perspective for the study of Internet nicknames but also a potentially feasible way to stimulate users’ online social interactions.
Imagining daylight: Evaluating participants' perception of daylight in work environments

This paper presents an experimental research study intended to evaluate daylight perception in work environments. The current metrics for assessing daylight in work environments involve quantifying daylight and do not provide insight into the actual appearance of the space, thus overlooking the qualitative aspects of light and their effect on occupants. Conceptual content cognitive mapping (3CM), a mixed methodology, was used to obtain participants' hierarchical knowledge structure and mental model of daylight in work environments. This method was selected to understand the end users' perception of an environment rather than measuring the physical environment. A cognitive map of 50 participants, half architects and half non-architects, was created using 3CM. The results present a list of daylight lexicon and a vast selection of concepts related to physical and psychological comfort among all participants. This experiment reveals that connectivity and emotional reactions are the two dominant underlying dimensions describing daylight in work environments. The results illustrate both similarities and disjunctions between two groups of participants regarding the selected daylight terms and characterizations. The results also indicate the need to augment current planning practices with a qualitative multi-disciplinary approach to create more pleasant working environments.
Practical source monitoring for continuous-variable quantum key distribution

Abstract Source monitoring is a matter of vital importance for continuous-variable quantum key distribution (QKD), since it is the first barrier to ensure the practical security. In a practical scenario, the difficulty arises with the existence of the laser intensity fluctuation, which not only contributes to the source noise but also causes the deviation of source monitoring result. We first illustrate the effect of the relative intensity noise (RIN) on the ultimate security by explicitly showing the parameter estimation procedure, and find that the secret key rate is overestimated in this situation. To defend the continuous-variable QKD system against potential attacks utilizing this impairment to conceal itself, a source monitoring scheme is devised by adopting the seminal one-time shot-noise unit calibration approach into the monitor module. It is shown that by using the practical source monitoring scheme the impact of the RIN can be modeled and monitored in real time to circumvent an overestimated secret key rate. This scheme also lowers the system complexity thus is suitable for widespread application.